R is a free software environment for statistical computing and graphics. It compiles and runs on a wide variety of UNIX platforms, Windows and MacOS. Set RGui Environment Language language = EN inside the file Rconsole (in my installation it is C:\Program Files\R\R-2.15.2\etc\Rconsole); 较早版本的中文教程 （ 数据分析与绘图的编程环境.pdf ，2006 ）。 English Version in RGUI-Help-Mannuals (in PDF)-An Introduction to R. Eclipse as IDE for R Programming StatET is an Eclipse based IDE (integrated development environment) for R. It offers a set of mature tools for R coding and package building. This includes a fully integrated R Console, Object Browser and R Help System, whereas multiple local and remote installations of R are supported. 1. From the Eclipse menu bar click Help - Install New Software 2. Click the‘Add’ button. The “Add Site” dialog appears 3. Type in a friendly name for your remote resource, such as StatET 4. Paste the URL (http://download.walware.de/eclipse-4.4, this is the latest version available at StatET website, as of 20-July-2015) into the ‘Location’ box, make sure to check the option of Contact all update sites during install to find required software, then click ‘OK’ 5. Select (check WalWare-Libraries, StatET, Utlilities ) the package components that you want to install, then click ‘Next’ 6. A review screen showing your selection(s) displays. Click ‘Next’ 7. Accept the license agreement, then click ‘Finish’ 8. Now Eclipse will install StatET, but it will take a few minutes. During the installation you may be asked to accept a digital certificate from Eclipse.org. Configure the StatET Eclipse plugin 1. Install R, if you need to. NOTE: as far as Eclipse is concerned, it makes no difference if you run 32 or 64-bit R. Just be sure that you identify 32 or 64-bit R to Eclipse, which we do in the next steps (among other things). 2. From the Eclipse menu bar click Help - Cheat Sheets: StatET: R in Eclipse to put up the official help. This is a handy, built-in guideline, but some of the steps inaccurately describe the menu choices (and one reason why I wrote these instructions). It is a good reference, though, for your later work, so keep it in mind. Click the ‘Cancel’ button to close the Cheat Sheet Selection dialog. 3. From the Eclipse menu bar click Window - Preferences: StatET : Run/Debug: R Interaction: Select ‘R Console inside Eclipse (default)’ for the ‘Connectorused to run R code’. Note that this is the default so it might already be this way. This is how I run mine. Be sure to click ‘Apply’ if you change the setting. 4. Back in the left pane click ‘R Environments’ (StatET : Run/Debug: R Environments, which is directly above the ‘R Interaction’ of the previous step). The ‘R Environments’ pane now appears on the right. Click the pane’s ‘Add’ button. The ‘Add R Environment Configuration’ dialog appears. Type in a friendly name in the ‘Name’ box, such as R.x64 2.15.1 or whatever your R version is, for example. For the ‘Location (R_HOME)’ box, paste in the path to your R install directory, for example C:\Program Files\R\R-2.15.1. (in the same directory with Eclipse) Make sure the ‘Architecture’ field is set to your R type (32 or 64-bit), such as x86_64. Click ‘OK’ to save your changes and close the dialog, which returns us to the ‘R Environments’ pane, which now shows our newly created R environment. Click ‘Apply’ and ‘OK’ to save and close the ‘R Environments’ pane. 5. From the Eclipse menu bar click Run - Run Configurations. The ‘Run Configurations’dialog box appears. In the left pane click ‘R Console’, right click and select ‘New’ to define a new console entry with a friendly name in the ‘Name’ box, such as ‘MyConsole’. Under the ‘Main’ tab ensure that ‘Launch Type’ is set properly for your configuration. If you are uncertain, choose ‘Rterm’ (this selection is VERY IMPORTANT). For ‘Working Directory’ just provide a path to your projects directory, for example C:\d2\SOFTWARE_DEVELOPMENT\Research\R\Projects No further changes are required, but you can check to make sure that the ‘R Environment’ under the next tab (‘R Config’) is set to that environment you named previously. (Workbench Default) Click ‘Apply’and then ‘Close’ to close the dialog 6. Set perspective for R project If you can not see the R-project under File – New, switch Eclipse’s perspective to StatETby clicking Window – Open Perspective – Other – StatET. References R (programming language), from Wikipedia, the free encyclopedia . The R Project for Statistical Computing . R for Windows FAQ . Eclipse, from Wikipedia, the free encyclopedia . Eclipse . StatET for R . Setting up StatET Eclipse in Windows . R applications see details .
在利用mothur align.seqs模块将tags比对到sivla参考序列时，为了减少计算量，通常会对silva进行区域的筛选。 针对v4区域的参考位置是start=11894, end=25319 v3-5区域的参考位置是start＝6426，end=27654 v3-4区域的参考位置是start=6428, end=23444 材料参考源： http://www.mothur.org/forum/viewtopic.php?p=7548 http://www.mothur.org/forum/viewtopic.php?f=3t=3327p=9623hilit=pcr.seqs+v3_4#p9623 http://www.mothur.org/forum/viewtopic.php?f=3t=2498p=10536hilit=pcr.seqs+v3_4#p10536 具体的位置界定方法： 1） Find an Ecoli 16S sequence， 2）Trim the sequence to the region within your primers. Whether or not to keep the primer sequences doesn't matter 3）Align the trimmed sequence to the reference alignment (SILVA is preferred) 4）Run summary.seqs on the aligned sequence 5）Use the start and end numbers inpcr.seqs 具体的操作实例可参考： http://www.mothur.org/forum/viewtopic.phpf=3t=2498p=10536hilit=pcr.seqs+v3_4#p10536 在实际使用中遇到以下问题： Some of you sequences generated alignments that eliminated too many bases 针对以上情况，为了保证结果准确性，同时不严重增加计算量的情况下： v3-4区域目前的建议是：start=1, end=25000 相关的具体原理需进一步研究
On precisely this spot, thousands of years ago, the Greek people peacefully celebrated their unity in spite of their sometimes hostile diversity. Pierre de Coubertin, whose heart is resting here in Ancient Olympia, rediscovered this unique gift of the Greek people to human culture after more than 1,500 years. He took the idea of the Olympic Games, breathed new life into it, developed it and, together with Greece, presented it to the entire world in 1896. This is why especially here, in Ancient Olympia, we feel the breath of cultural history. But we also feel our responsibility for the future of everything the Olympic Games represent in terms of their culture and values. Today, our Russian partners and friends in particular have a vital part to play in this responsibility. For this reason, I am very happy to greet the Doyen of the IOC, Vitaly Smirnov, the president of the Russian National Olympic Committee and IOC colleague, Alexander Zhukov, together with IOC member Shamil Tarpischev, the Mayor of Sochi, Anatoly Pakhomov, and the CEO of the Organising Committee for the XXII Olympic Winter Games in Sochi, Dmitry Chernyshenko. I am certain that they will offer us and the world excellent Games, to which we can all eagerly look forward. The flame lit today by the Greek sun takes on this responsibility for a peaceful celebration here and now; the torches will carry it into the Olympic future. Thus the Olympic Torch Relay will be a messenger for the Olympic values of excellence, friendship and respect without any form of discrimination. In the coming months, this message will reach and inspire people from all walks of life. The goal and culmination of this journey will be the Olympic Stadium in Sochi on 7 February 2014. There, the Olympic flame will remind all the athletes, participants and spectators of our Olympic values. Just as in Ancient Greece, the Olympic Games cannot directly settle political problems or secure lasting peace between peoples. The Olympic flame thus reminds us to be aware of our own Olympic limits; but it reminds us also to use the strength of our values and symbols for the positive development of global human society. The Olympic Games, the Olympic athletes and above all the Olympic Village can be a powerful symbol; they can set an example for peaceful coexistence and mutual respect. They should inspire the people of the world, and especially the political authorities, by showing them that quarrels and conflicts can be addressed with peaceful means, that we can transcend all boundaries to agree on global rules for human competition and conflict resolution. Understood in this way, the Olympic message sees the global diversity of cultures, societies and life choices as a source of enrichment. It accuses no-one and it excludes no-one. But it does require us all to defend and uphold the Olympic values in all the sports competitions; among all those taking part; and at all the Olympic venues. Only then can we use our positive message of tolerance and respect through fair play in sport to set an example for the harmonious development of humanity. In this sense, the Olympic flame should be an inspiration for billions of people across the globe. It should transmit the sporting joy of living and encourage people to engage in sports activity. And now, as our Russian friends say: Poyékhali! Let’s go! Bolshóye spasíba. Efkharistó poli. Thank you very much. Merci à vous tous. 原文见 http://www.olympic.org/Documents/IOC_President/2013-09-29_Speech_IOC_President_Bach_Torch_Lighting_English.pdf
有了一些思路，开始准备撰写文章了。 总结一下SCI期刊论文的撰写方法。 准备工作： 1. Four questions: (1) Why did you start? (2) What did you find? (3) What did you do? (4) What does it mean? 2. Preparation (1) The title and abstract of paper 论文的题目和摘要帮助读者更好的了解论文内容，也可帮助作者更好的组织论文结构。 (2) Analyze the research result 在结果和数据的分析中，要考虑哪些表格或图形要采用，如果发现有欠缺，要修改，甚至更改或者推翻论文的结论。 (3) Read the references.
Gregory Chaitin Let me start with Hermann Weyl, who was a fine mathematician and mathematical physicist. He wrote books on quantum mechanics and general relativity. He also wrote two books on philosophy: The Open World: Three Lectures on the Metaphysical Implications of Science (1932), a small book with three lectures that Weyl gave at Yale University in New Haven, and Philosophy of Mathematics and Natural Science , published by Princeton University Press in 1949, an expanded version of a book he originally published in German. In these two books Weyl emphasizes the importance for the philosophy of science of an idea that Leibniz had about complexity, a very fundamental idea. The question is what is a law of nature, what does it mean to say that nature follows laws? Here is how Weyl explains Leibniz's idea in The Open World , pp. 40-41: The concept of a law becomes vacuous if arbitrarily complicated laws are permitted, for then there is always a law. In other words, given any set of experimental data, there is always a complicated ad hoc law. That is valueless; simplicity is an intrinsic part of the concept of a law of nature. What did Leibniz actually say about complexity? Well, I have been able to find three or perhaps four places where Leibniz says something important about complexity. Let me run through them before I return to Weyl and Popper and more modern developments. First of all, Leibniz refers to complexity in Sections V and VI of his 1686 Discours de métaphysique , notes he wrote when his attempt to improve the pumps removing water from the silver mines in the Harz mountains was interrupted by a snow storm. These notes were not published until more than a century after Leibniz's death. In fact, most of Leibniz's best ideas were expressed in letters to the leading European intellectuals of his time, or were found many years after Leibniz's death in his private papers. You must remember that at that time there were not many scientific journals. Instead European intellectuals were joined in what was referred to as the Republic of Letters. Indeed, publishing could be risky. Leibniz sent a summary of the Discours de métaphysique to the philosophe Arnauld, himself a Jansenist fugitive from Louis XIV, who was so horrified at the possible heretical implications, that Leibniz never sent the Discours to anyone else. Also, the title of the Discours was supplied by the editor who found it among Leibniz's papers, not by Leibniz. I should add that Leibniz's papers were preserved by chance, because most of them dealt with affairs of state. When Leibniz died, his patron, the Duke of Hanover, by then the King of England, ordered that they be preserved, sealed, in the Hanover royal archives, not given to Leibniz's relatives. Furthermore, Leibniz produced no definitive summary of his views. His ideas are always in a constant state of development, and he flies like a butterfly from subject to subject, throwing out fundamental ideas, but rarely, except in the case of the calculus, pausing to develop them. In Section V of the Discours , Leibniz states that God has created the best of all possible worlds, in that all the richness and diversity that we observe in the universe is the product of a simple, elegant, beautiful set of ideas. God simultaneously maximizes the richness of the world, and minimizes the complexity of the laws which determine this world. In modern terminology, the world is understandable, comprehensible, science is possible. You see, the Discours was written in 1686, the year before Leibniz's nemesis Newton published his Principia , when medieval theology and modern science, then called mechanical philosophy, still coexisted. At that time the question of why science is possible was still a serious one. Modern science was still young and had not yet obliterated all opposition. The deeper idea, the one that so impressed Weyl, is in Section VI of the Discours . There Leibniz considers "experimental data" obtained by scattering spots of ink on a piece of paper by shaking a quill pen. Consider the finite set of data points thus obtained, and let us ask what it means to say that they obey a law of nature. Well, says Leibniz, that cannot just mean that there is a mathematical equation passing through that set of points, because there is always such an equation! The set of points obey a law only if there is a simple equation passing through them, not if the equation is "fort composée" = very complex, because then there is always an equation. Another place where Leibniz refers to complexity is in Section 7 of his Principles of Nature and Grace (1714), where he asks why is there something rather than nothing, why is the world non-empty, because "nothing is simpler and easier than something!" In modern terms, where does the complexity in the world come from? In Leibniz's view, from God; in modern terminology, from the choice of the laws of nature and the initial conditions that determine the world. Here I should mention a remarkable contemporary development: Max Tegmark's amazing idea that the ensemble of all possible laws, all possible universes, is simpler than picking any individual universe. In other words, the multiverse is more fundamental than the question of the laws of our particular universe, which merely happens to be our postal address in the multiverse of all possible worlds! To illustrate this idea, the set of all positive integers 1, 2, 3, ... is very simple, even though particular positive integers such as 9859436643312312 can be arbitrarily complex. A third place where Leibniz refers to complexity is in Sections 33-35 of his Monadology (1714), where he discusses what it means to provide a mathematical proof. He observes that to prove a complicated statement we break it up into simpler statements, until we reach statements that are so simple that they are self-evident and don't need to be proved. In other words, a proof reduces something complicated to a consequence of simpler statements, with an infinite regress avoided by stopping when our analysis reduces things to a consequence of principles that are so simple that no proof is required. There may be yet another interesting remark by Leibniz on complexity, but I have not been able to discover the original source and verify this. It seems that Leibniz was once asked why he had avoided crushing a spider, whereupon he replied that it was a shame to destroy such an intricate mechanism. If we take "intricate" to be a synonym for "complex," then this perhaps shows that Leibniz appreciated that biological organisms are extremely complex. These are the four most interesting texts by Leibniz on complexity that I've discovered. As my friend Stephen Wolfram has remarked, the vast Leibniz Nachlass may well conceal other treasures, because editors publish only what they can understand. This happens only when an age has independently developed an idea to the point that they can appreciate its value plus the fact that Leibniz captured the essential concept. Having told you about what I think are the most interesting observations that Leibniz makes about simplicity and complexity, let me get back to Weyl and Popper. Weyl observes that this crucial idea of complexity, the fundamental role of which has been identified by Leibniz, is unfortunately very hard to pin down. How can we measure the complexity of an equation? Well, roughly speaking, by its size, but that is highly time-dependent, as mathematical notation changes over the years and it is highly arbitrary which mathematical functions one takes as given, as primitive operations. Should one accept Bessel functions, for instance, as part of standard mathematical notation? This train of thought is finally taken up by Karl Popper in his book The Logic of Scientific Discovery (1959), which was also originally published in German, and which has an entire chapter on simplicity, Chapter VII. In that chapter Popper reviews Weyl's remarks, and adds that if Weyl cannot provide a stable definition of complexity, then this must be very hard to do. At this point these ideas temporarily disappear from the scene, only to be taken up again, to reappear, metamorphised, in a field that I call algorithmic information theory . AIT provides, I believe, an answer to the question of how to give a precise definition of the complexity of a law. It does this by changing the context. Instead of considering the experimental data to be points, and a law to be an equation, AIT makes everything digital, everything becomes 0s and 1s. In AIT, a law of nature is a piece of software, a computer algorithm, and instead of trying to measure the complexity of a law via the size of an equation, we now consider the size of programs, the number of bits in the software that implements our theory: Law: Equation → Software, Complexity: Size of equation → Size of program, Bits of software. The following diagram illustrates the central idea of AIT, which is a very simple toy model of the scientific enterprise: Theory (01100...11) → COMPUTER → Experimental Data (110...0). In this model, both the theory and the data are finite strings of bits. A theory is software for explaining the data, and in the AIT model this means the software produces or calculates the data exactly, without any mistakes. In other words, in our model a scientific theory is a program whose output is the data, self-contained software, without any input. And what becomes of Leibniz's fundamental observation about the meaning of "law?" Before there was always a complicated equation that passes through the data points. Now there is always a theory with the same number of bits as the data it explains, because the software can always contain the data it is trying to calculate as a constant, thus avoiding any calculation. Here we do not have a law; there is no real theory. Data follows a law, can be understood, only if the program for calculating it is much smaller than the data it explains. In other words, understanding is compression, comprehension is compression, a scientific theory unifies many seemingly disparate phenomena and shows that they reflect a common underlying mechanism. To repeat, we consider a computer program to be a theory for its output, that is the essential idea, and both theory and output are finite strings of bits whose size can be compared. And the best theory is the smallest program that produces that data, that precise output. That's our version of what some people call Occam's razor. This approach enables us to proceed mathematically, to define complexity precisely and to prove things about it. And once you start down this road, the first thing you discover is that most finite strings of bits are lawless, algorithmically irreducible, algorithmically random, because there is no theory substantially smaller than the data itself. In other words, the smallest program that produces that output has about the same size as the output. The second thing you discover is that you can never be sure you have the best theory. Before I discuss this, perhaps I should mention that AIT was originally proposed, independently, by three people, Ray Solomonoff, A. N. Kolmogorov, and myself, in the 1960s. But the original theory was not quite right. A decade later, in the mid 1970s, what I believe to be the definitive version of the theory emerged, this time independently due to me and to Leonid Levin, although Levin did not get the definition of relative complexity precisely right. I will say more about the 1970s version of AIT, which employs what I call "self-delimiting programs," later, when I discuss the halting probability Ω. But for now, let me get back to the question of proving that you have the best theory, that you have the smallest program that produces the output it does. Is this easy to do? It turns out this is extremely difficult to do, and this provides a new complexity-based view of incompleteness that is very different from the classical incompleteness results of Gdel (1931) and Turing (1936). Let me show you why. First of all, I'll call a program "elegant" if it's the best theory for its output, if it is the smallest program in your programming language that produces the output it does. We fix the programming language under discussion, and we consider the problem of using a formal axiomatic theory, a mathematical theory with a finite number of axioms written in an artificial formal language and employing the rules of mathematical logic, to prove that individual programs are elegant. Let's show that this is hard to do by considering the following program P : P produces the output of the first provably elegant program that is larger than P . In other words, P systematically searches through the tree of all possible proofs in the formal theory until it finds a proof that a program Q , that is larger than P , is elegant, then P runs this program Q and produces the same output that Q does. But this is impossible, because P is too small to produce that output! P cannot produce the same output as a provably elegant program Q that is larger than P , not by the definition of elegant, not if we assume that all provably elegant programs are in fact actually elegant. Hence, if our formal theory only proves that elegant programs are elegant, then it can only prove that finitely many individual programs are elegant. This is a rather different way to get incompleteness, not at all like Gdel's "This statement is unprovable" or Turing's observation that no formal theory can enable you to always solve individual instances of the halting problem. It's different because it involves complexity. It shows that the world of mathematical ideas is infinitely complex, while our formal theories necessarily have finite complexity. Indeed, just proving that individual programs are elegant requires infinite complexity. And what precisely do I mean by the complexity of a formal mathematical theory? Well, if you take a close look at the paradoxical program P above, whose size gives an upper bound on what can be proved, that upper bound is essentially just the size in bits of a program for running through the tree of all possible proofs using mathematical logic to produce all the theorems, all the consequences of our axioms. In other words, in AIT the complexity of a math theory is just the size of the smallest program for generating all the theorems of the theory. And what we just proved is that if a program Q is more complicated than your theory T , T can't enable you to prove that Q is elegant. In other words, it takes an N -bit theory to prove that an N -bit program is elegant. The Platonic world of mathematical ideas is infinitely complex, but what we can know is only a finite part of this infinite complexity, depending on the complexity of our theories. Let's now compare math with biology. Biology deals with very complicated systems. There are no simple equations for your spouse, or for a human society. But math is even more complicated than biology. The human genome consists of 3 × 10 9 bases, which is 6 × 10 9 bits, which is large, but which is only finite. Math, however, is infinitely complicated, provably so. An even more dramatic illustration of these ideas is provided by the halting probability Ω, which is defined to be the probability that a program generated by coin tossing eventually halts. In other words, each K -bit program that halts contributes 1 over 2 K to the halting probability Ω. To show that Ω is a well-defined probability between zero and one it is essential to use the 1970s version of AIT with self-delimiting programs. With the 1960s version of AIT, the halting probability cannot be defined, because the sum of the relevant probabilities diverges, which is one of the reasons it was necessary to change AIT. Anyway, Ω is a kind of DNA for pure math, because it tells you the answer to every individual instance of the halting problem. Furthermore, if you write Ω's numerical value out in binary, in base-two, what you get is an infinite string of irreducible mathematical facts: Ω = .11011... Each of these bits, each bit of Ω, has to be a 0 or a 1, but it's so delicately balanced, that we will never know. More precisely, it takes an N -bit theory to be able to determine N bits of Ω. Employing Leibnizian terminology, we can restate this as follows: The bits of Ω are mathematical facts that refute the principle of sufficient reason, because there is no reason they have the values they do, no reason simpler than themselves. The bits of Ω are in the Platonic world of ideas and therefore necessary truths, but they look very much like contingent truths, like accidents. And that's the surprising place where Leibniz's ideas on complexity lead, to a place where math seems to have no structure, none that we will ever be able to perceive. How would Leibniz react to this? First of all, I think that he would instantly be able to understand everything. He knew all about 0s and 1s, and had even proposed that the Duke of Hanover cast a silver medal in honor of base-two arithmetic, in honor of the fact that everything can be represented by 0s and 1s. Several designs for this medal were found among Leibniz's papers, but they were never cast, until Stephen Wolfram took one and had it made in silver and gave it to me as a 60th birthday present. And Leibniz also understood very well the idea of a formal theory as one in which we can mechanically deduce all the consequences. In fact, the calculus was just one case of this. Christian Huygens, who taught Leibniz mathematics in Paris, hated the calculus, because it was mechanical and automatically gave answers, merely with formal manipulations, without any understanding of what the formulas meant. But that was precisely the idea, and how Leibniz's version of the calculus differed from Newton's. Leibniz invented a notation which led you automatically, mechanically, to the answer, just by following certain formal rules. And the idea of computing by machine was certainly not foreign to Leibniz. He was elected to the London Royal Society, before the priority dispute with Newton soured everything, on the basis of his design for a machine to multiply. (Pascal's original calculating machine could only add.) So I do not think that Leibniz would have been shocked; I think that he would have liked Ω and its paradoxical properties. Leibniz was open to all systèmes du monde , he found good in every philosophy, ancient, scholastic, mechanical, Kabbalah, alchemy, Chinese, Catholic, Protestant. He delighted in showing that apparently contradictory philosophical systems were in fact compatible. This was at the heart of his effort to reunify Catholicism and Protestantism. And I believe it explains the fantastic character of his Monadology , which complicated as it was, showed that certain apparently contradictory ideas were in fact not totally irreconcilable. I think we need ideas to inspire us. And one way to do this is to pick heroes who exemplify the best that mankind can produce. We could do much worse than pick Leibniz as one of these exemplifying heroes. 原文见 http://www.umcs.maine.edu/~chaitin/apa.html
http://semiaccurate.com/2010/02/25/tsmc-start-22nm-trial-runs-2012/ TSMC to start 22nm trial runs in 201228nm is slightly delayed Feb 25, 2010 by Lars-Gran Nilsson Share on digg Share on reddit Share on hackernews Share on email More Sharing Services THE MAKING OF computer chips is a complicated business, not only for the chip designers but also for the foundries. TSMC’s senior VP of RD, Shang-Yi Chiang has announced that the company is getting ready for 22nm trial runs towards the end of 2012. According to Digitimes , he also revealed information about TSMC’s 28nm progress. The company is getting ready to begin trial production on one of its nodes at the end of June. This means that we might start seeing real 28nm products sometime towards the end of the year, but this would be limited to TSMC’s low power silicon oxynitride process. However, TSMC is planning to kick off its 28nm high performance application node using its high-k metal gate process for trial production in September, and this will then be followed by the low power version trial production in December. This means that TSMC is at least one quarter late, as the company issued a press release in August of last year stating that it was planning to start its first 28nm “risk production” of its low power silicon oxynitride process in the first quarter of 2010, while the high-k metal gate process was scheduled for the second quarter. Altera, Fujitsu Microelectronics, Qualcomm and Xilinx are said to be TSMC’s first 28nm customers. We would expect many others to follow, although TSMC isn’t the only company getting ready to start 28nm production this year and it might be seeing some competition from GlobalFoundries, as it is also getting ready with its own 28nm process and should kick that off about the same time as TSMC moves to its high-k metal gate process. As for the 22nm process, not much else was said, although the initial runs will be on the high performance process node moving to the low power version by early 2013. Considering the trouble TSMC has had with its 40nm process and the slight delay in kicking off the 28nm production, we won’t place any bets on its 22nm process coming in on time. On a side note, TSMC claims that it’s currently able to churn out 80,000 12-inch 40nm wafers per quarter, although it expects to double this by the end of this year. This is all done in Fab 12, but the company is planning to add a second 40nm production facility called Fab 14 in order to be able to meet increased demand. S|A
http://www.businesswire.com/news/home/20120315005089/en/3M-Invests-Silicon-Anode-Lithium-Ion-Batteries 3M Invests in Novel Silicon Anode for Lithium Ion Batteries 3M Research to Pioneer the Future; Company Expands Manufacturing ST. PAUL, Minn.--( BUSINESS WIRE )--3M, the leading United States (US) battery materials supplier, is investing in research and manufacturing of novel Silicon (Si) based 3M anode materials. The technology enables advanced batteries for reliable power that is required to keep up with the global increase of mobile societies and electric vehicles. “Our investment into research and development, coupled with our experience and portfolio of more than 40 core technologies – including nanotechnology, adhesives, precision coating, fluoromaterials – give us the tools and confidence in our ability to develop next-generation materials for better cells.” 3M was recently granted another U.S. patent, 8,071,238 for its Silicon anode compositions that can increase cell capacity by over 40 percent when matched with high-energy battery cathodes. The company has invested resources and expertise toward commercialization of battery technology for the past 15 years. 3M’s investments into the high-energy metal based anode for lithium ion batteries include matching a recent U.S. Department of Energy (DOE) grant for $4.6 million as part of efforts to build more energy-efficient vehicles. The research will help to develop and integrate new cell materials that will make a transformative change in energy density and in cost in lithium ion batteries used in electric vehicles. Especially critical to the project success is 3M’s Si based anode material. The 3M investment in research and development includes putting in 3M’s best battery materials technology for cathode, anode and battery electrolyte additives into the project. “3M has a proven track record of being an innovator in battery materials, and we are committed to supporting the growing U.S. and global lithium ion battery industry,” said Chris Milker, business development manager for 3M Electronic Markets Materials Division. “Our investment into research and development, coupled with our experience and portfolio of more than 40 core technologies – including nanotechnology, adhesives, precision coating, fluoromaterials – give us the tools and confidence in our ability to develop next-generation materials for better cells.” The new research efforts deepen 3M’s rich history of sustainability and in making a global impact through innovation. The research expands upon the company’s long-standing initiatives in the battery market to commercialize battery technology for electric vehicles and consumer electronics. In addition to its investment in robust research and development, 3M recently completed the first phase of Silicon anode manufacturing capacity expansion in early 2012 in its Cottage Grove, Minn., facility. The expansion included the installation of large-scale manufacturing equipment specialized to 3M and its proprietary anode chemistry. The U.S.-based facility will provide Si anode material to 3M’s global battery customers. 3M is well ahead of its time in pioneering research for lithium ion battery materials, which began in the 1990s for early auto market applications. Lithium ion batteries are a common source of power for laptop computers and electronic handheld devices and emerged as a power source for battery powered hand tools. In addition, 3M lithium ion technology is emerging for transport applications including the hybrid vehicles market. Because of the company’s consistent investment into the industry, 3M has uniquely developed three critical battery materials used in lithium ion batteries. These include silicon anode chemistry, novel cathode technologies (nickel, manganese, cobalt) and electrolyte (salts and additives). Besides battery cathode, anode and electrolyte technologies, 3M also offers tapes and adhesives for assembly of consumer electronics and fluids to manage heat during the manufacture of electronic devices. Using its broad portfolio of battery materials, 3M has the unique capability to integrate these materials to solve customers’ battery problems. For more information about 3M battery materials visit www.3m.com/batterymaterials About 3M 3M captures the spark of new ideas and transforms them into thousands of ingenious products. Our culture of creative collaboration inspires a never-ending stream of powerful technologies that make life better. 3M is the innovation company that never stops inventing. With $30 billion in sales, 3M employs 84,000 people worldwide and has operations in more than 65 countries. For more information, visit www.3M.com or follow @3MNews on Twitter.
SÃO CARLOS, BRAZIL, FEBRUARY 04-07, 2013 Organizing committee: Alexandre Nolasco de Carvalho (ICMC/USP) Ederson Moreira dos Santos (ICMC/USP) Ma To Fu (ICMC/USP) Márcia Cristina Anderson Braz Federson (ICMC/USP) Marcio Fuzeto Gameiro (ICMC/USP) Sérgio Henrique Monari Soares (ICMC/USP) Scientific committee: Alexandre Nolasco de Carvalho (Universidade de São Paulo/Brazil) Carlos Rocha (Instituto Superior Técnico/Portugal) George R. Sell (University of Minnesota/USA) Jianhong Wu (York University/Canada) Joan Solà-Morales (Universitat Politècnica de Catalunya/Spain) John Mallet-Paret (Brown University/USA) José M. Arrieta (Universidad Complutense de Madrid/Spain) Konstantin Mischaikow (Rutgers University/USA) Marco Antonio Teixeira (Universidade Estadual de Campinas/Brazil) Orlando Francisco Lopes (Universidade de São Paulo/Brazil) Peter Kloeden (Goethe University Frankfurt/Germany) Sérgio Henrique Monari Soares (Universidade de São Paulo/Brazil) Shui-Nee Chow (Georgia Tech/USA) Tomás Caraballo (Universidade de Sevilla/Spain) Waldyr M. Oliva (Instituto Superior Técnico/Portugal and Universidade de São Paulo/Brazil) Yingfei Yi (Georgia Tech/USA)
http://www.03964.com/read/7c7f4d52e250dac51ed82edd.html A Beginner’s Guide to Materials Studio and DFT Calculations with Castep P. Hasnip (pjh503@york.ac.uk) September 18, 2007 Materials Studio collects all of its les into “Projects”. We’ll start by creating a new project. 1 Now we’ve got a blank project, and we want to dene a simulation cell to perform a Castep calculation on. First we add a “3D Atomistic document”. 2 3 We’re going to start by simulating an eight atom silicon FCC cell, so rename the le accordingly. First we’ll create the unit cell. 4 5 The default is space group P1, i.e. no symmetry. Silicon has the diamond structure (space group FD3M). By telling Materials Studio this symmetry it will automatically apply it to the atoms, thus generating atoms at the symmetry points. 6 Now to add the lattice constant – click on the “Lattice” tab near the top of the “Build Crystal” window. Since FD3M is cubic (FCC) Materials Studio knows only a has to be set, and the angles and other lattice constants are greyed-out. Enter “5.4”, and then click on “Build”. 7 Now we’ll add a single silicon atom... 8 Add a silicon atom at the origin, by changing the “element” from its default and clicking “Add”. By default the co-ordinates are in fractionals, but you can change this on the “Options” tab. 9 Since we’d already told Materials Studio what the symmetry of the crystal was, our single silicon atom is replicated at each symmetry site and we now have a shiny new eight-atom silicon unit cell. You can rotate the view by holding down the left or right mouse button and dragging, or move it by holding down the middle button. Use the mouse wheel, or both the left and right buttons simultaneously, to zoom in and out. 10 By default the atoms are shown as little crosses with lines for bonds, and silicon atoms are coloured brownish orange. You can always change this if you don’t like it. The “bonds” are just guesses made by Materials Studio based on the element’s typical bond-lengths. We’re now ready to run Castep to nd the groundstate charge density. Click on the Castep icon, which is a set of three wavy lines (to represent plane-waves), and select “Calculation”. 11 Materials Studio oers a high-level interface to Castep, with cut-o energy, k-point sampling, convergence tolerances etc. all set by the single setting “Quality”. We’ll look at how to specify these things later, but for now we’ll just do a very quick, rough calculation of the groundstate energy and density of our cell. Make sure the task is “Energy”, and select “Coarse” for quality, and “LDA” for the XC functional. 12 If you want to run your calculations on Lagavulin (or anywhere else you have Castep available) then you’ll want to click “Files” in the Castep window. Simply select “Save Files” to save the cell and param les. By default these are written to a folder in “My Documents” called “Materials Studio Projects” but be warned – cell les are hidden les, and you won’t be able to see them unless you tell Windows you want to view “Hidden and System Files” for that folder. 13 If you want to run Castep on the PC you’re using, you just need to click “Run” on the Castep window. You should see this window appear: Materials Studio is telling you that your system isn’t actually the primitive unit cell, and it’s oering to convert it to the primitive cell for you. For now choose “No”. Castep runs via a “Gateway”, which might be on your local computer or on a remote machine. This Gateway handles Materials Studio’s requests to run calculations and copies the les to and from the “Castep Server”. 14 Since the Gateway is actually a modied web server it is sensible to enforce some security measures. If your Gateway is password-protected (recommended), you’ll need to enter your Gateway username and password (which are not necessarily the same as your Windows ones). 15 When the Castep job is running you will see its job ID and other details appear in the “job explorer” window. You can check its status from here, although our crude silicon calculation is so quick you probably won’t have time now. 16 Castep reports back when it is nished, and Materials Studio copies the results of the calculation back. The .castep le is opened automatically so you can see what happened in the calculation. The main text output le from castep is displayed in Materials Studio. It starts with a welcome banner, then a summary of the parameters and cell that were used for the calculation. 17 After that, there is a summary of the electronic energy minimisation which shows the iterations Castep performed trying to nd the groundstate density that was consistent with the Kohn-Sham potential. This is the so-called “self-consistent eld” or “SCF” condition, and each line is tagged with “– SCF” so you can nd them easily. ------------------------------------------------------------------------ -- SCF SCF loop Energy Fermi Energy gain Timer -- SCF energy per atom (sec) -- SCF ------------------------------------------------------------------------ -- SCF Initial 2.11973065E+002 4.85767974E+001 0.61 -- SCF Warning: There are no empty bands for at least one kpoint and spin; this may slow the convergence and/or lead to an inaccurate groundstate. If this warning persists, you should consider increasing nextra_bands and/or reducing smearing_width in the param file. Recommend using nextra_bands of 7 to 15. 1 -7.22277610E+002 1.02240172E+001 1.16781334E+002 0.88 2 -8.53739673E+002 6.90687627E+000 1.64327579E+001 1.12 3 -8.62681938E+002 6.65069587E+000 1.11778315E+000 1.39 4 -8.62169156E+002 6.69758744E+000 -6.40977798E-002 1.72 5 -8.61880601E+002 6.78641872E+000 -3.60693332E-002 2.06 6 -8.61884687E+002 6.79549194E+000 5.10791707E-004 2.44 7 -8.61884645E+002 6.79874201E+000 -5.25062118E-006 2.75 8 -8.61884639E+002 6.79822409E+000 -8.40318139E-007 2.98 -----------------------------------------------------------------------Final energy, E = -861.8846385210 Final free energy (E-TS) = -861.8846385210 (energies not corrected for finite basis set) NB est. 0K energy (E-0.5TS) = -861.8846385210 eV eV ---------SCF SCF SCF SCF SCF SCF SCF SCF SCF eV 18 We’ll look at this output in more detail later. For now just note that the energy converges fairly rapidly to about 861.88eV, but that the energy is sometimes higher than this and sometimes lower. Let’s have a look at the calculated groundstate charge density. 19 The Castep Analysis window lets you look at various properties you might have calculated during the Castep job. Select “Electron density”. Notice there’s a “Save” button which lets you write the density out to a text le so you can analyse it with another program. We don’t need this now, so just click on “Import”. 20 WARNING: amongst the properties listed here are “Band structure” and “Density of states”. If you select one of these from an energy calculation, Materials Studio will plot the band structure/DOS, but it takes the eigenvalues and k-points from the SCF calculation, not a proper band structure or DOS calculation. 21 By default an isosurface of the charge density is overlaid on your simulation cell. 22 To change the isosurface Materials Studio is plotting, you need to change the “Display style”. Either use the right mouse button when the cursor is over the simulation cell, or use the drop-down menus: Notice that this is also the place you need to come to if you want to change the atom colouring or representation (e.g. from crosses and lines to ball-and-stick). 23 24 Try changing the value of the isosurface your plotting, to see where the charge density is greatest and least. 25 Hopefully you’ve now got the hang of the basic interface. Go back to your simulation system and open up the Castep window again. This time select the “Electronic” tab. 26 This tab has a little more detail, and actually tells you what cut-o energy and k-point grid Castep will use for the given settings. Nevertheless we usually want ner control than this, so click on “More”. 27 Now at last we have four tabs that let us set some of the convergence parameters directly. 28 Basis Allows you to set a cut-o energy, as well as control the nite basis set correction. SCF Sets the convergence tolerance for the groundstate electronic energy minimisation, as well as details of the algorithm used. k-points Controls the Brillouin zone sampling directly. You can either specify a grid, or a desired separation between k-points. Potentials Allows you to change the pseudopotentials used for the elements in your system. In fact if you double-click on your param le in the project window you can edit it directly, but we’ll restrict ourselves to using the GUI for now. 29 Before we continue, here’s a quick recap of the basic approximations we use when performing practical DFT calculations: Exchange-correlation (XC) Functional - we don’t know the exact density functional, so we have to approximate it. There are two common approximations: – LDA - the Local Density Approximation assumes the XC at any point is the same as that of a homogeneous electron gas with the same density. – PBE - this is a “Generalised Gradient Approximation” (GGA) and includes some of the eects of the gradient of the density. You might think PBE is always better than LDA, but that’s not true, both are approximations. You should try each one before deciding which is appropriate to your research project. Basis set - the wavefunction is represented by an expansion in a plane-wave basis. In theory the basis set required is innite, but since the energy converges rapidly with basis set size we can safely truncate the expansion. The size of the basis set is controlled by the cut-o energy. Brillouin zone sampling - calculating the energy terms requires us to integrate quantities over the whole of the rst Brillouin zone. In practice we approximate these integrals by sums over a discrete set of k-points. 30 Exercise 1. Using the Basis and k-points tabs, investigate how the calculated energy of the simulation cell converges with increased cut-o energy, and increased k-point sampling density. Why do they show these trends Exercise 2. Create a unit cell for bulk aluminium. Aluminium is also FCC, with spacegroup FM-3M and a lattice constant of about 4.05 . Investigate convergence of the calcuA lated aluminium energy with respect to cut-o energy and k-point sampling. Compare the total electronic energy with the total electronic free energy for both silicon and aluminium. Why do they dier for one and not the other 31 During your calculations you might see a warning like this in the castep output: Warning: There are no empty bands for at least one kpoint and spin; this may slow the convergence and/or lead to an inaccurate groundstate. If this warning persists, you should consider increasing nextra_bands and/or reducing smearing_width in the param file. Recommend using nextra_bands of 7 to 15. Recall that the electronic energy minimisation algorithms need to include the entire set of occupied states. If the highest state you’ve included in the calculation is occupied, Castep has no way of knowing whether the next state should also have been occupied, and so recommends you include more bands. Only when the highest state is unoccupied can Castep be sure that all of the occupied bands have been included. You can change the number of “empty” bands included in the Castep calculation from the SCF tab of the Castep Electronic Options window of Materials Studio, or just by editing the param le directly. 32 Exercise 3. Repeat the energy convergence test with respect to k-point sampling for aluminium, but using a smearing of 0.5eV (see the SCF tab; the default is 0.1eV). Feel free to use either Materials Studio, or direct editing of the param and cell les. You will probably need to increase the number of empty bands to 8 or so. Compare the results with the previous aluminium calculations. Why the dierence Choose a particular k-point sampling density and look at the nal total energy, free energy, and estimated zero temperature energy for the 0.5 eV smearing and compare them to the results with the original smearing. 33 Exercise 4. Go back to your silicon calculation, and look at the SCF tab on the CASTEP Electronic Options window. We’re using the “Density Mixing” algorithm, and if you click on “More” you’ll see we’re using a Pulay mixing scheme with a charge mixing amplitude of 0.5. Investigate what happens as you vary this initial amplitude from close to 0 to close to 1. The Pulay algorithm takes over after the rst few SCF cycles, and overrides the mixing charge amplitude. This is not true of the Kerker scheme. Use the “More” button and change the mixing scheme to Kerker, and investigate the eects of the mixing charge amplitude again. Exercise 5. Have a play with the Castep interface and Castep. Why don’t you see whether you can get Castep to fail to converge Remember what causes density mixing to be unstable: metals, degeneracies (band-crossings), multiple spin states, long cells, small smearing 34 widths etc. The only restriction is computational time, so if you make a large cell try not to have too many atoms in it or Castep won’t nish in time! If you manage to make Castep fail to converge, try to x it by varying the DM parameters. If that doesn’t work, how does EDFT do Remember you can always save your cell and param les and copy them to Lagavulin if your PC isn’t fast enough. 35 You can also try Castep on your favourite system. Things you might nd useful: Materials Studio ships with lots of sample structures, just click “File” then “Import” and have a look, or create your own. To create a supercell from a unit cell, click on the “Build” menu, then select “Symmetry” and then “Supercell”. To modify atoms just left-click (or dragselect) to select them, and then you can use the “Modify” menu to change their element. Materials Studio has a useful surface builder so you can cleave crystals along bizarre planes without too much eort. 36 If you’re stuck for things to do: Try making a supercell of two aluminium FCC cells, and swapping one of the aluminium atoms for erbium. Run that, and see what happens. Can you improve it Use the task “properties” in the Castep window to calculate the DOS and band structures of silicon and aluminium. Now create a simple molecule surrounded by vacuum, and calculate its band structure and DOS. Do you get what you expect Calculate the binding energy of a simple molecule. Run Castep for the molecule, and then again for a single atom of each of the elements in turn. Subtract the energies, and see what you get. How does the result change if you change the cut-o energy for (a) one calculation; (b) all the calculations 37
China emphatically demonstrated their dominance of Badminton as all of their Doubles top seeds eased to victories on the opening day of London 2012 competition. The Asian nation boasts the top pairs in each of the three Doubles events at Wembley Arena and there were no mistakes as all featured in the evening session. Zhang Nan and Zhao Yunlei were hardly troubled as they brushed off the challenge of Germans Michael Fuchs and Birgit Michels in Group A, winning 21-6 21-7 in 28 minutes. Such was their superiority that the longest rally of the match was only 19 strokes. It was a similar story in the women's Doubles as Wang Xiaoli and Yu Yang saw off Canada's Michele Li and Alex Bruce 21-11 21-7 in Group D. In the men's event, Cai Yun and Fu Haifeng were not kept long by Australians Ross Smith and Glenn Warfe in Group A, winning 21-11 21-17. Zhao was also in action earlier in the day in the women's Doubles, in which she is seeded second with Tian Qing , and enjoyed a 21-11 21-12 win over Poon Lok Yan and Tse Ying Suet of Hong Kong. Also impressive from China was women's third seed Li Xuerui , who was a late addition to the team but looked imperious as Peru's Claudia Rivero was vanquished 21-5 21-6 in just 22 minutes. Yet the Chinese may not have it all their own way, with the All England men's Doubles champions, the second-seeded Chung Jae-sung and Lee Dong-dae, beating former world leaders Howard Bach and Tony Gunawan 21-15 21-19. With the top seeds in the men's and women's Singles not in action, former Olympic and world champion Taufik Hidayat took centre stage to make a winning start in one of the highlights. The 2004 Athens gold medallist, seeded 11th, proved too strong for the Czech Republic 's Petr Koukal , winning 21-8 21-8 in Group O. Germany's Marc Zwiebler also made swift work of the world's number 209, Mohamed Ajfan Rasheed of the Maldives. 原文见 http://www.london2012.com/news/articles/day-review-china-start-style.html
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