About: George Fitzgerald, PhD

My Bio:
Product manager with over 20 years experience advancing scientific software and services to the chemical and materials industries. Specialties include: - Density functional theory, materials modeling, information management, materials databases. - Catalysis design, thermodynamics and kinetics, IR, Raman, NMR spectroscopies.

All posts by George Fitzgerald, PhD:

Spectroscopy: Where Theory Meets the Real World

February 23rd, 2010 by George Fitzgerald, PhD

One of the most successful uses of quantum mechanical modeling methods is to predict spectra. These methods are capable of yielding good predictions of UV/Visible, NMR, Infrared, Raman, THz, and EELS (electron energy loss spectroscopy) to name just a few. Spectroscopy (according to Wikipedia) is the “study of the interaction between radiation and matter as a function of wavelength … or frequency.” How does this help chemists? We can use the spectra to determine the structure of new molecules or materials; to determine the composition of mixtures; or to follow the course of a chemical reaction in situ. How does modeling help with this? In a number of ways, but I’ll cover just 2.

One way modeling comes into play is by working with experimental results to remove ambiguities. When a chemist is trying the determine the structure of a new material, he or she takes a spectrum, or two, or three. His or her knowledge of the ingredients together with the spectra gives a pretty good idea what the chemical or crystal structure is. In a lot of cases the data are sufficient only  to narrow this down to 3-4 possible structures. Molecular modeling resolve this ambiguity by predicting the spectrum of each possibility; the spectrum that matches the experimental one presumably corresponds to the “right” one. Modeling is even more valuable when investigating defect structures like this work on Mg2.5VMoO8.

Another use is telling where experimentalists to look for the spectral peaks of a new compound. This can be especially important when trying to detect the spectra of new, novel, or poorly characterized materials. Experimental terahertz (THz) spectroscopy, for example, examines the spectral range of 3-120 cm-1, and can be used for detection and identification for a wide assortment of compounds including explosives like HMX. It’s a lot safer to investigate these materials by modeling than in the lab.

A recent blog by Dr. Damian Allishighlights the importance of doing the simulations correctly. (By the way, Damian, congrats on getting to page 1000.) A lot of work for the past 40-odd years has gone into predicting spectra of isolated – or gas phase – molecules. But materials like HMX are crystalline, and calculations on the isolated molecules make for poor comparison with crystals. The recent work underscores how important it is to simulate crystals using crystals. And it’s not just for THz spectra. Recent work on NMR leads to the same conclusion. A couple of programs can do this. Damian’s blog focuses on DMol3 and Crystal06, but we should also mention CASTEP and Gaussian as other applications capable of predicting a wide variety of properties for solids.

Let’s keep modeling – but be careful out there: short cuts will lead to poor results, and molecular modeling will end up taking the rap for user error.

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Calling DFT to Order

February 15th, 2010 by George Fitzgerald, PhD

One of the most interesting developments in density functional theory (DFT) in recent years is the emergence of the so-called “Order-N” methods. What’s that mean? Quantum chemists and physicists classify the computational cost of a method by how rapidly it scales with the number of electrons (or the number of molecular orbitals.) This can get into a real jargon of computational chemistry, but here are some examples:   

ONETEP gets its speed by using localized molecular orbitals (MOs). Top: a conventional MO is spatially delocalized, hence it interacts with many other MOs. Bottom: localized MOs do not interacte, hence less computational effort is required to evaluate matrix elements.

 Consider the N4 case as an example. This means that if you double the size of the system that you’re modeling, say from a single amino acid to a DNA base pair, the cost  (i.e., CPU time) goes up by roughly 16x. That makes many of these approaches prohibitive for systems with a large number of atoms. The good news is that it doesn’t really need to cost this much. The atomic orbitals that constitute the molecular orbitals have finite ranges, so clever implementations can hold down the scaling. The holy grail is to develop methods that scale as N1 or N, hence the expression “Order-N” or “linear scaling.” Using such a method, doubling the size of the system simply doubles the amount of CPU time.   

My favorite Order-N method is ONETEP (not surprising, considering that it’s distributed by Accelrys). As explained in their publications, this approach uses orbitals that can be spatially localized more than conventional molecular orbitals to achieve its speed. As a result of localization, there’s a lot of sparsity in the DFT calculation, meaning a lot of terms go to zero and don’t need to be evaluated. Consequently, it’s possible to perform DFT calculations on systems with 1000s of atoms. Because of its ability to treat system of this size, it’s ideally suited for nanotechnology applications. Some recent examples include silicon nanorods (Si766H462) or building quasicrystals (Penrose tiles) with 10,5-coronene.  

Why bring this up now? CECAM (Centre Euopéen de Calcul Atomique et Moléculaire) is hosting a workshop on linear-scaling DFT with ONETEP April 13-16 in Cambridge, UK. This is a chance for experienced modelers and newcomers to learn from the expert. Plus they’ll have access to the Cambridge Darwin supercomputer cluster, so attendees will have fun running some really big calculations. What kind of materials would you want to study if you had access to this sort of technology?

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Theory and Experiment in “Step” on Semiconductors

February 8th, 2010 by George Fitzgerald, PhD

A recent news article by the University of Texas at Dallas (UTD)  highlighted recent joint work by the Department of Materials Science and Engineering and Accelrys on critical surface reactions of Silicon. The research points the way to ”improve semiconductor devices’ performance in health care and solar power applications in particular.”

Who cares? Anybody who uses chips, solar cells, or any other device containing semiconductors (in other words, all of us.)  

Insertion of Nitrogen atom is predicted to occur preferentially at the step edge of Si(111)

 How does the latest research help? A typical semiconductor device consists of a metal oxide semiconductor layer (e.g., HfO2) deposited on a silicon substrate. As explained by co-author Dr. Mat Halls, formation of an SiO2interlayer between the silicon substrate and metal oxide can decrease semiconductor performance. One approach to solving this is to introduce a nitride barrier to prevent the growth of interfacial SiO2. The ability to introduce such heteroatoms into the topmost layers of Si affords additional opportunities to tune the surface properties by enhancing chemical reactivity at these sites to form functional surfaces. But how do you get the nitrogen to stick to the surface?     

In the latest research, published in Nature Materials, used infra-red spectroscopy  to explore the possible formation mechanisms of nitride on silicon surfaces terminated by hydrogen. Calculations using density functional theory (DFT) demonstrated how stepped edges are important to formation of the nitride layers. The reaction mechanism on the stepped surface provides a means of controlling the reaction. As the authors wrote: “The ability to control the reaction … enables the realization of applications … including sensing, electrical and thermal transport, and molecular computing.” This is a beautiful demonstration of the complementarity of theory and experiment. One can deal with facts, but requires interpretation. The other provides detailed explanations at the atomic level, but sometime requires an anchor to the “real world.” Together they can do more. Wouldn’t it be great if all viewpoints could be reconciled this well?

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DFT Redux

January 14th, 2010 by George Fitzgerald, PhD

I thought I’d start the year with an easy blog, simply following up on my earlier ramblings of 25 October 2009: DFT Goes (Even More) Mainstream. In that article I discussed the success of Density FunctionalTheory (DFT) and used the annual number of publications as a metric. The numbers show that publications grew by over 25% per annum, but the results for 2009 were naturally incomplete.

Happily the trend continued through 2009 for a total of 4621 DFT references in ACS Journals. Here are a few of my favorite publications, thought not all are drawn from the ACS citations. Yes, of course, these use Accelrys DFT packages, but they are still pretty cool articles:

Let me and my readers know what you think are the most interesting DFT articles from 2009.

†Strictly speaking, this was not QSAR, Quantitative Structure-Activity Relationship, because they didn’t actually base predictions on the structure. I use the term here more generally to refer to relationships that predict complex properites like catalytic activity, on the basis of simpler properties, like workfunction.

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Cosmetics got Chemistry

December 17th, 2009 by George Fitzgerald, PhD

Things have come a long way since the ancient Egyptians used galena (lead sulphite) as eye makeup. I spent most of last week in New York City at the annual meeting of the Society of Cosmetic Chemists. There is an amazing amount of very sophisticated chemistry going on in cosmetics.

One of the most enjoyable presentations was by Ricardo Diez of Chanel, Inc. Dr. Diez summarized developments in ‘cleansing products’ over the last 50 years. I put the term in quotation marks because what we call ’soap’ today is quite different from soap of 50 years ago. An excerpt from the New York Times of that era advised women to wash their hair no more often than about every 2 weeks. This stuff was really just your basic soap, i.e., fatty acid salts.

Dr. Diez reported that in the 1920’s German chemists created the first ’soap alternatives’ or detergents to support the textiles industry. These were the people who filed the patents “behind widely used anionic surfactants” still around today. Surfactants transformed soaps into milder, more effective cleaning agents. Over time, manufacturers made the products gentler (think Johnson’s ® ”No More Tears” ®). Then added silicones to combine shampoo and conditioner (e.g., Procter & Gamble’s “Pert Plus” ®). Finally, manufacturers combined moisturizers with the cleaners.

How did all this come about? Remember the DuPont slogan: Better living through chemistry? Some might find it frivolous to apply this expression to cosmetics. But the advances in ’soap’ have made a real improvements to peoples’ lives, making it easier and cheaper to practice good hygene – not just to keep up appearances. As a professional chemist, I’m proud to be associated with the scientists who’ve accomplished that.

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Theory Meets Industry In Nagoya

November 12th, 2009 by George Fitzgerald, PhD

I’ve enjoyed 2 days so far in Nagoya attending the “Theory Meets Industry” conference. There is some amazing work going on by both developers of computational methods and those who apply them. We’ve heard from developers like Bernard Delley and his recent work onTDDFT in DMol3, which will enable excited state calculations and UV spectra. We’ve also heard from Georg Kresse about his recent work on the Random Phase Approximation (RPA),which offers a way to improve not just DFT band gaps but total energies, as well.

There’s been an emphasis in alternative energy from the industrial participants. Applications are really diverse:

  • Rradiation damage in reactor containment materials by Christophe Domain of EDF
  • Improved solar cells by Royji Asahi of Toyota Central R&D Labs
  • Fischer-Tropsch catalysis by Werner Janse van Rensburg of Sasol Technologies
  • Hydrogen storage materials by Pascal Raybaud of IFP

This list also reflects the true international spirit of the conference.

I’ve also heard some interesting new approaches to doing calculations fast while not sacrificing accuracy. Gabor Csanyi of Cambridge University presented his Gaussian Approximation Potentials (GAP), an alternative to force fields that spans more of the potential energy surface. And Isao Tanaka of Kyoto University showed how he uses an improved Cluster Expansion method to study phase transitions. Keep your eye on these methods for future developments.

Today I make my own small contribution by presenting my work on high-throughput computation. Look for details on that in a future blog.

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Ohayou, Nagoya

November 5th, 2009 by George Fitzgerald, PhD

What would entice me to spend 12 hours squeezed into a 757? How about terrific sushi, good company, and great science. The 3rd Theory Meets Industry International Workshop will be held November 11-13 in Nagoya, Japan. As you can read on their website:

The purpose of the current workshop is to exchange ideas at the cutting edge of first principles calculations, particularly their application to real-world problems.
Nagoya Castle, build in 1612, is one of the most famous sight seeing spots in Nagoya. Image courtesy of Wikipedia, http://en.wikipedia.org/wiki/Nagoya

Nagoya Castle, built in 1612. Courtesy of Wikipedia, http://en.wikipedia.org/wiki/Nagoya

This is truly a meeting of both developers of modeling applications and industrial practitioners. Rivalries are put aside and scientists discuss what’s being developed and what needs to be developed.

 

Theoretical chemists such as Bernard Delley (Paul Scherrer Institute) and Georg Kresse (University of Vienna) will be in attendance. And scientists from around the world – quite literally – will present their applications of modeling. The list includes applications to energy harvesting, alloys, Fischer-Tropsch catalysis, and oxide semiconductors – to name just a few. For a complete list download the programme.

Stay tuned for more info. In so far as jetlag allows, I’ll post updates by blog and twitter during the meeting.

では、また (See you)

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DFT Goes (Even More) Mainstream

October 25th, 2009 by George Fitzgerald, PhD

When I did my graduate work in quantum chemistry, doing a calculation on something the size of, say, hexatriene was a huge deal. In those days, the calculations were limited to people with expertise and perseverance – and a lot of patience. GUI? We didn’t need no stinkin’ GUI. We set up input files by hand, even had to work out the Cartesian coordinates of the atoms on a hand calculator. Those times have changed.

A combination of factors helped to make quantum mechanical calculations accessible to a wider range of users:

  • Computers got faster. These calculations take a long time, but computers today are around 1,000-10,000x faster than when I was in grad school.
  • Modeling methods got faster. Besides simply optimizing software performance, there were breakthrough approaches like density functional theory (DFT), which delivered reliable results with less CPU time.
  • Graphical User Interfaces (GUIs). Yes, it turns out that we do need those stinkin’ GUIs. It’s just not feasible to set up calculations for anything more than around a dozen atoms without a sketcher.

DFT has done particularly well over the past 10 years or so. On top of everything else, the methods have been extended to include systems with periodic boundary conditions, so chemists have started getting into solid state calculations. With this approach you can study heterogeneous catalysis on an extended (periodic) surface; predict crystal structures; or calculate elastic constants. Searching ACS Number of occurrences of 'Density Functional Theory' in ACS journals has grown by about 25% each year since 1990for “density functional theory” shows a staggering increase from 37 publications in1989 to almost 4000 so far this year.

Among some interesting research directions are those to compute solid-state spectra. This includes NMR, Raman, and EELS (electron energy loss spectra), all of which are part of the drive to make DFT more relevant by connecting theory with experiment. Raman is used, for example, to characterize reactions in situ. NMR can be used to discriminate among crystal polymorphs. EELS has “enabled detailed measurements of the atomic and electronic properties of single columns of atoms, and in a few cases, of single atoms.” Earlier this year, there was even a workshop sponsored by the Oxford University Department of Materials to promote these computational approaches specifically to the experimental community. This combination of theory and experiment facilitates the identification of unknown compounds; the elucidation of reaction mechanisms; and the characterization of molecular & crystal structure.

One of the most gratifying measures of success is the appearance of articles not directed at other specialists. NewScientist recently ran an article about using DFT to predict crystal structure entirely from first principle – a sort of DFT for the common man type article.

As a developer and practitioner of DFT, I’m really pleased to see how far the awareness of theory in general and DFT in particular has spread. Who knows: someday there may even be an iPhone application for DFT.

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Methanol from Biomass – Separating the Wheat from the Chaff (or the Cob from the Stalk)

September 16th, 2009 by George Fitzgerald, PhD

A number of recent (and not so recent) initiatives in Congress are designed to encourage production of ethanol as an alternative fuel, but how much is really feasible and how much is catering to eco-hype? A search of congressional bills for the 111th congress turns up an astonishing 1592 bills relating to “energy” and 150 to “renewable energy.” These bills do everything from providing tax credits for growing corn, to funding development of production facilities, to providing tax credits for consumers. But which options really make sense?

The debate over methanol from corn has been going on for a while, and judging from the available information, there’s plenty to be concerned about. Paztec and Pimantel have published numbers that suggest that such production is a net energy loss. This is supported by an EPA report summarized in Chemical & Engineering News (C&EN) May 11, 2009 [sorry, you’ll need a subscription to read that]. A 2007 energy law set a production target of 36 billion gallons of biofuels by 2022. As reported in C&EN, the law requires a full life-cycle analysis that “reflects a growing concern that ethanol may result in higher CO2 emissions due to land-use practices, such as clearing rain forest…”  And another recent C&EN article discussed the potholes on the road to commercial biofuels. According to the article, of the six cellulosic ethanol projects to receive DoE grants in 2007, none of the projects has been built, although one is under construction.

Yet, optimism abounds. As reported in C&EN, Sean O’Hanlon of the American Biofuels Council is confident that next-generation biofuels will deliver. On top of that, Exxon plans to invest up to $600 million to develop biofuels from algae.  And there’s no shortage of small startups trying to reach similar goals.

Despite the differences between the optimists and pessimists, I think that they agree on one thing: the need for higher efficiency. Given the current efficiencies of biofuel production, internal combustion engines, and fuel cells, biofuels can’t reach the goals that we’ve set for them (e.g., 10% of electricity from renewable sources by 2012, and 25 percent by 2025). What is unquestionably needed is more fundamental research. To underscore some of my favorite, recent high points:

These are just a few examples of the many fundamental advances that will be required to make biofuel sustainable and commercially viable.

Scientists regularly cry out for more fundamental research funding at the start of each federal budget cycle. The American Reinvestment and Recovery Act (ARRA) of 2009 provides for $4.6 billion in DOE grants for basic R&D. The latest congressional omnibus bill provides $151.1 billion in federal R&D, an increase of $6.8 billion or 4.7 percent above the FY 2008 value. This is a real good start. Let’s make sure that we use the money wisely.

Here are the results from the poll attached to this blog post:

GreenEnergy2

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Green Chemical Applications Highlight Japan UGM

July 23rd, 2009 by George Fitzgerald, PhD
Frank Brown gives Plenary talk at Japan User Group Meeting

Frank Brown, Accelrys CSO, delivers plenary talk at Japan User Group Meeting

Materials Science modeling can be used to address a heap of research topics including batteries, fuel cells, catalysts, and light weight materials – to name just a few. The Asian markets have adopted the materials modeling approach quite enthusiastically. Accelrys’ Japan User Group Meeting(in Japanese but most titles are in English – scroll down) attracted about 150 users with 45% in Materials Science, and the rest split between life science and data pipelining tracks. Contrast that with the US UGM which attracted roughly the same number of users, with the bulk focusing on data pipelining. These guys are really into quantum mechanics.

The user presentations included research on lithium-ion batteries, photocatalysis, and solar cells. Attendees weren’t just academics: scientists from Showa Denko and Mitsubishi Chemicals gave presentations, along with colleagues from Tokyo Institute of Technology, the Japan Fine Ceramics Center, and Ryukoku University. In the past few years, Japanese researchers have filed a number of patents based on the results of modeling, such as this one on lithium ion batteries. To be sure, US researchers have done this too, (here, for example) but perhaps not so recently as their Japanese counterparts. Can we directly attribute the success of products such as Toyota’s hybrid vehicle - the Prius - to advanced modeling techniques? Perhaps not, but the Japanese have certainly invested heavily in this area, and believe in the returns.

Modeling has long worked with experiment in materials science to increase R&D efficiency. See for example, the Vision 2020 report on modeling. You won’t hit a home run with every calculation, but the results will narrow the alternatives and let the experiments focus on the most promising leads. Modeling has been applied to a number of areas of “green chemistry” or alternative energy besides the examples presented at the User Group Meeting. Consider these examples, which show only the tip of the iceberg:

There’s a real opportunity for these methods to make the world a better place. I hope all scientists will take a look at how their research could benefit.

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