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:

Simulation Stimulates the Business of Food, Flavors and Fragrances

August 20th, 2010 by George Fitzgerald, PhD

Molecular and materials modeling has long been successful in areas such as catalysis, polymers, coatings, adhesives, and semiconductors. But did you know about all the applications in areas such as perfumes, chocolate, or cosmetics? These are just different types of materials after all, so it shouldn’t  surprise anyone that Accelrys announced that it’s making inroads into the consumer packaged goods sector.

Over the past year several of my fellow scientists and I have been investigating the ways that both modeling and informatics solutions can be applied to CPG R&D. There are some great write-ups out there:

Also note Dr. Felix Grant’s article Material Values in Scientific Computing World. And check out the great image on the cover.

What’s fueling this recent surge of scientific informatics and modeling into CPG? Like so many other R&D-based sectors, CPG has been challenged to remain competitive while holding down costs — and nothing says “take my money” like a wrinkle cream that really works, and is sold over the counter at the local pharmacy! As the articles I’ve cited illustrate, predictive analytics can help R&D teams find answers faster and for less $$ than can experimentation alone – a lesson that sectors such as Pharmaceuticals learned quite some time ago. Add to this the that predictive (molecular) modeling methods have become easier to use and are increasingly merged with informatics, and you gain unprecedented R&D capabilities.

Modern pharmaceuticals are extensively modeled before chemicals are ever mixed in the lab. Maybe the next great perfume will come out of a computer simulation, too.

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Materials Science, Modeling, and Sustainability: an Update

August 16th, 2010 by George Fitzgerald, PhD

What are critical problems in alternative energy research? How does modeling play a role in bringing us closer to answers?

A recent review article on this topic by long-time associate Prof. Richard Catlow, et. al, caught my attention. Readers of this blog will be familiar with our many posts pertaining to ‘green chemistry,’ sustainable solutions, and the like. Last month, Dr. Misbah Sarwar of Johnson Matthey was featured in a blog and delivered a webinar on the development of improved fuel cell catalysts. Dr. Michael Doyle has written a series on sustainability. Drs. Subramanian and Goldbeck-Wood have also blogged on these topics, as have I. All of us share a desire to use resources more responsibly and to ensure the long-term viability of our ecosphere. This will require the development of energy sources that are inexpensive, renewable, non-polluting, and CO2 neutral. Prof. Catlow provides an excellent overview on the applications of molecular modeling to R&D in this area. Read the paper for a very comprehensive set of research problems and case studies, but here are a few of the high points.

  • Hydrogen production. We hear a lot about the “hydrogen economy,” but where is all this hydrogen going to come from? Catlow’s review discusses the generation of hydrogen from water. Research challenges include developing photocatalysts capable of splitting water using sunlight.
  • Hydrogen storage. Once you’ve created the hydrogen, you need to carry it around. Transporting H2 as a compressed gas is risky, so most solutions involve storing it intercalated in a solid material. LiBH4 is a prototypical example of a material that can reversibly store and release H2, but the process is too slow to be practical.
  • Light absorption and emission. Solar cells hold particular appeal, because they produce electricity while just sitting there (at least in a place like San Diego; I’m not so sure about Seattle). One still needs to improve conversion efficiency and worry about manufacturing cost, ease of deployment, and stability )with respect to weathering, defects, aging, and so forth).
  • Energy storage and conversion. Fuel cells and batteries provide mobile electrical power for items as small as hand-held devices or as large as automobiles. Catlow and co-workers discussed solid oxide fuel cells (SOFC) in their paper. 

 The basic idea with modeling, remember, is that we can test a lot of materials for less cost and in less time than with experiment alone. Modeling can help you find materials with the optimal band gaps for capture generation of photoelectric energy. It can tell us the thermodynamic stability of these new materials: can we actually make them and will they stick around before decomposing.

Simulation might not hit a home run every time, but if you can screen out, say, 70% of the bad leads, you’ve saved a lot of time and money. And if you’re interested in saving the planet, isn’t it great if you can do it using less resources?

Check out some of my favorite resources on alternative energy, green chemistry, and climate change.

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Of Gas Hydrates, Concrete, and Oil Rigs

May 17th, 2010 by George Fitzgerald, PhD

The recent oil spill in the Gulf of Mexico is a catastrophe whose consequences will be felt for years to come. Prince William Sound, Alaska, still shows the presence of oil from the Valdez oil spill in 1989. Nevertheless, my intent in this blog entry is not to castigate the parties responsible for it or to lament the damage to the environment, but rather to discuss the chemistry that led to the initial explosion and subsequent tragedy. The problems began on 10 PM on April 20 when a surge of gas (apparently CH4) and oil shot up the drill pipe. Much mention has been made of hydrates, which seem to have contributed to this initial surge and which have subsequently impeded several attempts to cap the well.

Molecular model of gas hydrate

Molecular model of gas hydrate, from Leibniz Institut fuer Meereswissenschaften

Hydrates (as explained by the Leibniz Institut fuer Meereswissenschaften) are “non-stoichiometric compounds. Water molecules  form cage-like structures in which gas molecules are enclosed as guest molecules” (see image). These structures occur naturally in many deep ocean sites. Any number of gases can be found in the lattice, but the one of interest here is methane, which is produced by “zymotic decomposition of organic components or by bacterial reduction of CO2 in sediments.” These are stable over a specific range of temperatures and pressures as described here, and shown in the image below. There is a limited zone of stability for the hydrates: they extist at high pressures and relatively low temperatures. The weight of the ocean induces a pressure of roughly 1 atm for every 10 meters. Temperature initially drops with ocean depth, but then increases beneath the sea floor. Once you get deep enough – and hot enough – the hydrates are no longer stable. Consequently, their occurrence is limited to a relatively narrow band beneath the ocean floor. The operation was taking place in 1500 m (5,000 ft) of water, and drilling to a depth of 5000 m (18,000 ft).

Stability diagram for gas hydrates

Stability diagram for gas hydrates in marine environment, from Leibniz Institut

As reported in the Financial Times energysource blog, “It has been suggested that they [hydrates] may have been responsible for the leakage of gas into the Deepwater Horizon’s drill riser…” Why did the gas appear then? The crew of Deepwater Horizon was in the process of capping off the well, which involves pumping concrete into the top to seal it (nice graphic of that process in the FT). It is known that this process can destabilize hydrates: a 2009 report by Halliburton, reported again in the FT’s energysource blog, warned that “gas flow may occur after a cement job in deep-water environments that contain major hydrate zones.” As the FT blog summarizes, gas might stop flowing from the hydrates in a few hours or days, or – if you’re unlucky – it might notstop. The chemistry of concrete is explained on this site maintained by WHD Microanalysis Consultants Ltd, who mention that the curing of concrete is an exothermic process, with the period of maximum heat evolution occurring typically between about 10 and 20 hours after mixing. I can’t help wondering whether heat released by the setting concrete can contribute to destabilization of the gas hydrates. Anybody got any thoughts on that?

Subsequently, gas hydrates played a role in hindering attempts to stop the flow of oil. Remember the 100-ton steel and concrete box they tried to move on top of the hole? As reported in the FT (again): ‘When gas leaks out [from the well], its pressure drops and it cools… In the presence of water, light hydrocarbon liquids can react with water to form a … hydrate. This happens quite often in gas pipelines, but the circumstances 5000 ft down on the sea bed … make this very difficult to control.‘  This says that the leak from the well is creating even more  hydrates. The hydrates clogged the container and forced a halt to the operation.

Incidentally, methane hydrates would be a great source of energy. Unfortunately, that’s not a carbon-neutral process: there’s a tremendous amount of CO2 that would be released. Still, it’s really fascinating to see ice burn as the CH4 is released.

Fascinating chemistry. As a theoretical chemist, I’ve been thinking about how modeling could help. Modeling could predict (T,P) phase diagrams for CH4 in H2O lattices. Monte-Carlo simulations can predict loading curves for these structures, while molecular dynamics or DFT could predict thermodynamic and kinetic stability of the methane absorption. Ultimately, you’d like to be able to use such approaches to identify ways to stabilize these structures, or to destabilize them in a controlled manner: imagine pumping in a chemical that causes the CH4 to be released sloooowly. The advantage of computational methods, of course, is that models won’t blow up no matter how much pressure you apply to them or how much methane ‘escapes.’

Such a study might help prevent future tragedies, but the main focus of scientists & engineers now needs to be on the cleanup. More on that in a future blog.

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High-Throughput, Quantum Mechanics, and Lithium Ion Batteries

April 28th, 2010 by George Fitzgerald, PhD
3D Pareto Surface

3D Pareto surface shows the tradeoffs among target properties: dipole moment, chemical hardness, electron affinity. The optimal leads are colored red, poor leads blue.

How do you search through 106 materials to find just the one you want? In my very first blog post “High-Throughput- What’s a Researcher to Do?” I discussed some ideas. The recent ACS had a session devoted to doing just that for materials related to alternative energy, as I wrote here and here.   

My own contribution was work done with Dr. Ken Tasaki (of Mitsubishi Chemicals) and Dr. Mat Halls on high-throughput approaches for lithium ion battery electrolytes. This presentation is available now on Slideshare (a really terrific tool for sharing professional presentations).   

We used high-throughput computation and semi-empirical quantum mechanical methods to screen a family of compounds for use in lithium ion batteries. I won’t repeat the whole story here; you can read the slides foryourselves, but here are a couple take-away points:   

  • Automation makes a big difference. Obviously automation tools make it a lot easier to run a few 1000 calculations. But the real payoff comes when you do the analysis. When you can screen this many materials, you can start to perform interesting statistical analyses and observe trends. The 3D Pareto surface in the accompanying image shows that you can’t optimize all the properties simultaneously – you need to make tradeoffs. Charts like this one help you to understand the tradeoffs and make recommendations.
  • Don’t work any harder than you need to. I’m a QM guy and I like to do calculations as accurately as possible. That isn’t always possible when you want to study 1000s of molecules. Simply looking through the literature let us know that we can get away with semi-empirical.

Enjoy the Slideshare, watch for more applications of automation and high-throughput computation, and let me know about your applications, too.

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Searching the Proverbial Haystack – ACS Part II

April 2nd, 2010 by George Fitzgerald, PhD

As I wrote last week, “Chemistry for a Sustainable World” was the theme of the ACS spring meeting. In this blog, I report on the research of 3 other speakers of the Monday morning session of CINF. All of these authors are trying to find ways to discover optimal materials from the huge selection of possibilities.

Heard of genomics? Proteomics? These methods generate a lotof data in the analysis of genes or proteins. Information management tools are needed to handle the large amounts of data. The goal, ultimately, is to be able to design new ones (genes or proteins) on the basis of the available information. The number of materials to search (the ‘design space’) is truly enormous. Searching for optimum materials effiently requires tools that were discussed by three of my fellow speakers in the session.

Prof. Krishna Rajan (Iowa State), discussed his “omics” approach to materials. In his presentation he discussed “a new alternative strategy, based on statistical learning. It systematically integrates diverse attributes of chemical and electronic structure descriptors of atoms with descriptors …  to capture complexity in crystal geometry and bonding. … we have been able to discover … the chemical design rules governing the stability of these compounds…” To me, this is one of the key objectives for computational materials science: the development of these design rules. Design rules, empirical evidence, atomic-level insight – call it what you will, this sort of approach is necessary to make custom-designed materials feasible.

Prof. Geoffrey Hutchison (U. Pittsburgh) really did talk about “Finding a needle through the haystack.” He discussed the “reverse design problem.” Sure, we can predict the properties of any material that we can think up. But what we really want to know is what material will give us these properties. His group uses a combination of database searching, computation, and genetic algorithm optimization to search the haystack. It’s a very efficient way to search these huge design spaces.

Dr Berend Rinderspacher (US Army Research Lab) also discussed the reverse design problem. He pointed out that there are around 10200 compounds of a size typical for, e.g., electro-optical chromophores. He unveiled a general optimization algorithm based on an interpolation of property values, which has the additional advantage of handling multiple constraints; and showed applications to optimizing electro-optic chromophores and organo-metallic clusters.

Terrific work by all the speakers in this session, who are using all methods at their disposal – whether based on informatics or atomistic modeling – to come up with better ways of looking for better materials. Next blog: a summary of my own contribution to the session.

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Sustainable Solutions, High-Throughput, and ACS

March 26th, 2010 by George Fitzgerald, PhD

“Chemistry for a Sustainable World” was the theme of the ACS spring meeting. My own interests, of course, are in modeling, and more recently in high-throughput computation. The Monday morning session of CINF was devoted to the application of this to electronic and optical materials. The presentations included both methodological approaches to materials discover and applications to specific materials, with a focus on materials for alternative energy.

Steven Kaye of Wildcat Discovery Technologies(located only about 4 km from Accelrys) talked about high-throughput experimental approaches for applications such as batteries, hydrogen storage, carbon capture, and more. The company can screen 1000’s of materials a week, and produce these in gm or kg quantities for scale-up. Impressive work.

Alan Aspuru-Guzik of Harvard’s Clean Energy Project talked about the search for improved photovoltaics. He uses a combination of CHARMm and QCHEM software

to help look for the best molecules possible for: organic photovoltaics to provide inexpensive solar cells, polymers for the membranes used in fuel cells for electricity generation, and how best to assemble the molecules to make those devices.

Uniquely, the project uses the IBM World Community Grid to sort through the myriad materials and perform these long, tedious calculations. You may remember this approach from the SETI (at) home, which was among the first to try this. This gives everyonethe chance to contribute to these research projects: you download software from their site, and it runs on your home computer like a screensaver: when your machine is idle, it’s contributing to the project. Prof. Aspuru-Guzik said that some folks are so enthusiastic that they actually purchase computers just to contribute resources to the project. It’s great to see this sort of commitment from folks who can probably never be individually recognized for their efforts.

I don’t want to make this blog too long, so great talks by Prof. Krishna Rajan (Iowa State), Prof. Geoffrey Hutchison (U of Pittsburgh), and Dr. Berend Rinderspacher (Army Research Labs) will be covered in the next blog.

I was also quite happy to see that the some of the themes in my presentation were echoed by the others – so I’m not out in left field after all! I’ll blog about my own talk later on, but here’s a quick summary: Like Prof. Aspuru-Guzik’s work we used high-throughput computation to explore new materials, but we were searching for improved Li-ion battery electrolytes. We developed a combinatorial library of leads, set up automated computations using Pipeline Pilot and semiempirical VAMP calculations, and examined the results for the best leads. Stay tuned for a detailed explanation and a link to the slides.

And keep an eye out, too, for the 2nd part of Michael Doyle’s blog on Sustainabilty.

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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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