Discover New Materials for Batteries Through Modeling

March 12th, 2010 by Accelrys Team

In the 21st century, materials and energy are more topical than ever before. Insights at the atomistic and quantum level help us to design cleaner energy sources, and find less wasteful ways of using energy. Join us on March 16th as Dr. George Fitzgerald presents “High-throughput Quantum Chemistry and Virtual Screening for Lithium Ion Battery Electrolyte Materials.”

Register to learn:

  • How modeling can support the discovery of components to enhance the performance of lithium ion battery formulations
  • How to use Materials Studio components in Pipeline Pilot to analyze and screen a materials structure library for Li-Ion battery additives
  • Results from a collaboration with Mitsubishi Chemical Inc which was also published in The Journal of Power Sources

This presentation is part of our ongoing webinar series that showcases how Accelrys products and services are transforming materials research. You can download related archived presentations in this series or register for future webinars.

We look forward to sharing our insights with you throughout this webinar series.

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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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High Throughput – What’s a Researcher to do?

May 12th, 2009 by George Fitzgerald, PhD

High-throughput experimentation has been a mainstay in pharmaceutical discovery since the mid-1990’s. In a 1999 C&E News article (C&EN, vol. 77, pp 33-48 March 8, 1999) this approach was hailed as the next great thing. Unfortunately, we chemists soon realized that quantity is no replacement for quality; a notable article in the WSJ “Drug Industry’s Big Push into Technology Falls Short,” was critical of this approach.

 

At the time, I was working on a DOE-funded project (DE-FC26-02NT41218) for high-throughput catalyst discovery for NOx catalysis in lean diesel engines, together with GM and Engelhard (now BASF). In practice, our method was not to generate 1000’s of samples and hope for the best but to screen fewer carefully selected samples quickly, and subject the “winners” to more sophisticated testing.

 

The approach employed in our NOx project was based on analysis of experimental data, design of experiment, and fitting response surfaces – and it worked. As pointed out in a recent BIOIT World article, however, experimental data alone are usually too noisy to build reliable statistical models. What’s a researcher to do? Molecular modeling, of course – hey I’m a modeller: you knew I was going to suggest that.

 

The key for success, it seems, is to employ a plurality of methods, both experimental and computational. Given even a modest amount of experimental data, you’ll need a database with decent search & query tools and basic statistical approaches like principle component analysis. But atomistic modeling is also important. Work by a number of research groups has shown that you can generate good predictive models from quantum mechanical methods (QM) for lots of different kinds of materials. (Keep in mind that these examples barely scratch the surface of the available literature).

 

But how do get to the point that anybody can make use of QM-based results? Doing these calculations typically takes a log time.

 

QSAR (Quantitative Structure Activity Relationship) is a terrific way to leverage QM results for complex research topics. These research groups followed the same basic procedure:

  • Start with some experimental data
  • Generate a statistical model
  • Grind through a lot of calculations
  • Forward the “winners” for experimental testing

 

You can see in the examples above that the approach can actually work. But how do you figure out what QM calculations to perform, and how do you create good statistical models? Well, that’s a story for next month.

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