So long AIChe and Tennessee

November 12th, 2009 by Michael Doyle, PhD

The show was excellent and we saw many people; old friends and new, young users of modeling tools. The level of interest, in a range of approaches, from chemical process development to solar energy and bio mass, point to an exciting future and the opportunities that may occur as we face the challenges of the next decade and beyond.

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Headin’ to AIChE…

November 9th, 2009 by Michael Doyle, PhD

Sat here in my seat en-route to the 2009 AICHE meeting, although a weekend flight, I had plenty of time to catch up on the back issues of my journals. So it was with interest I read Derek Lowe’s column on Pharmaceutical production and formulation as well as Anne Thayer’s on sustainable chemical synthesis and Rick Mullins in Process IT. I wonder how these themes of better formulation design, greener synthesis and better reaction solvent use, and linking process chemistry into business decision making will be reflected in the talks over the next 4 days.

The location sounds very exciting and – I am shocked to say after traveling on business to 45 countries, its my first time in Nashville. I am excited and hope to have some excellent discussions with scientists and engineers.

This meeting is a mile stone for those of us in the modeling and simulation area, since we are now releasing Materials Studio 50 which represents over 15 years continuous development and which on a simple PC platform encompasses quantum, atomistic, meso and data scale simulations.

There is as I say above many challenges in chemistry now perhaps more than ever, and I am certain these tools have a growing place in the chemists tool set when facing them and a growing applicability across all areas of science and materials development.

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Informatics lessons from the MRS

May 19th, 2009 by George Fitzgerald, PhD

The Materials Research Society (MRS) “encourage communication and technical information exchange across the various fields of science affecting materials.” It sponsors a spring meeting held in San Francisco and a fall meeting in Boston. This year’s spring meeting covered topics ranging from amorphous materials to methods for environmental stability to multiple topics in nanotechnology. (See all symposium titles here.)

Most interesting to me was Symposium Z: “Computational Nano science — How to Exploit Synergy between Predictive Simulations and Experiment, which fits with the comments I made in my previous posting, and shows just how much active interest there is in this topic. Prof. Krishna Rajan, who heads the Combinatorial Sciences and Materials Informatics Collaboratory, demonstrated how he uses data mining as a tool to understand the formation of apatites (minerals of the form A10­(BO4) 8X2) based on data mining and statistical analysis. How do you get your head around and N-dimensional space? How do you grasp trends when there are dozens of variables to consider? Use methods like recursive partitioning and Principal Component Analysis (PCA). 

Simpler than the modeling approaches I mentioned in my earlier posting, these require only a statistical analysis of the data (some experimental results, some modeling output). The results reduce N-dimensional datasets to 2 or 3 dimensions that are “grasp-able” by mere humans. Applying these approaches to the apatite data clearly shows how the choices of cation and anion influence the stability of the crystal.

Just think how many other research problems we could understand if we had the tools to look at the data in the right way.

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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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