How many modelers?

March 8th, 2010 by Gerhard Goldbeck-Wood, PhD

How many of “us” are out there? I mean how many people doing modeling and simulation? I’d really like to know, ideally broken down by discipline, such as Materials Science vs Life Science, and quantum, classical and mesoscale.

Alas, there are preciously few statistics on that, so when I read in the Monthly Update (Feb 2010) of the Psi-k network that they conducted a study on size of the ab initio simulation community, it got my immediate attention.

Representing a network of people from the quantum mechanics field, Peter Dederichs, Volker Heine and colleagues Phivos Mavropoulos and Dirk Tunger from Research Center Jülich searched publications by keywords such as ‘ab initio’, and made sure not to double-count authors. In fact they tend to underestimate by assuming people with the same surname and first initial are the same. As Prof Dederichs, the chair of the network tells me, checks were also made to ensure that papers from completely different fields are not included. Also they estimate that their keyword range underestimates the number of papers by about 10%. Of course there are those that didn’t publish a paper in 2008, the year for which the study was done. Moreover, Dederichs says, there are those who published papers which don’t have proper keywords like “ab initio” or “first principles” in the abstract or title, so they are not found in the search. All of that is likely to compensate for counting co-authors that are not actually modelers.

All in all, they come up with about 23,000 people! And the number of publications in the field indicates a linear rise year on year.

That’s quite a lot more than they expected, and I agree. The global distribution was also surprising, with about 11,000 in Europe, about 5,600 in America, and 5,700 in East Asia (China, Japan, Korea, Taiwan and Singapore). That’s a lot of QM guys, especially here in Europe. Now, there will be a response from the US on that one I guess?

I wonder how many classical modelers there are. I’d hazard a guess that the number of classical modelers is about half those in the QM community, at least in the Materials Science field. Assuming that the mesoscale modeling community is quite small, that would make for a total of at least 30,000 modelers worldwide.

What is your view, or informed opinion? Anybody else knows about or has done some studies? I am going to open up a poll in the right sidebar on the number of people involved in quantum, classical and mesoscale modeling in total. It would be great to hear also how you came up with your selection.

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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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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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Materials Studio in Action: Solving Real World Industrial Problems

November 9th, 2009 by Accelrys Team

Materials Studio Webinar Series: Part II

This Wednesday, join Dr. Agnes Derecskei-Kovacs, Principal Scientist at Millennium Inorganic Chemicals, to hear how her team, consisting of both experimentalists and modelers, helped increase the company’s R&D efficiency in the search for improved catalysts. This live webinar, Catalysis Applications in Industry, will use case studies to illustrate the team’s theoretical and experimental approaches as they are applied to solve real life industrial problems. Agnes will explain how rapid developments in molecular modeling are enabling the enhancement and acceleration of catalyst design at Millennium Inorganic Chemicals.

Don’t miss this webinar on Nov. 11 at 8am PST / 4pm GMT. Register today!

To view upcoming webinars in the series, please visit: http://accelrys.com/events/webinars/materials-studio-50.

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Materials Studio 5.0 – It’s not all plane waving…

November 2nd, 2009 by Stephen Todd, PhD

As I mentioned in my last blog on Materials Studio 5.0 , as with all releases of Materials Studio, there is always a wide range of functionality added in a single release. Last time I focussed on some of the functionality in the classical simulations area but this time I want to focus on new developments in the quantum mechanics area.

One of the main projects is the prediction of Raman spectra using CASTEP. This is highly anticipated and requested functionality and really makes CASTEP a unique tool for predicting a wide range of spectra from simple infra-red to core level spectroscopy (more on that later!)

CASTEP is an interesting code as it is not developed solely in house but in collaboration with a highly motivated and scientifically brilliant team who call themselves the CASTEP Developers Group (CDG). We have had a long and very successful relationship with these guys and I would consider them a top rating example of how academic and commercial collaboration can really produce high quality results which benefits all users.

Anyway, back to the Raman functionality. We were very fortunate that this was added into the release very early on so we managed to get some alpha feedback as early as March. At this time the functionality wasn’t running in parallel so the customer actually ran the calculation on a laptop and just left it going for several days (or more)! Also, one of our quantum mechanics supremo’s, Victor Milman, has already produced a paper (which has just been accepted). By the time we got around to the beta testing, we had showed some results at our Korean and Japan User Group Meetings and had customers salivating at the prospect of trying it. This has been one of the most pleasant beta tests I have been in involved with as nearly all the customers have had real success at predicting the Raman spectra of in-house materials. Although one comment from nearly all has been the calculations are pretty computationally expensive – time to use those cores!

Talking of cores, we also released core level spectroscopy functionality in Materials Studio 4.4. This allows you to simulate EELS or ELNES spectra. As often happens, we didn’t have time to fit all the bells and whistles we wanted into that release so we extended the tools in 5.0 to include smearing for the spectra which improves agreement with experimental spectra. Also, one of my colleagues took advantage of another piece of new functionality, exposure of CASTEP through MaterialsScript, to create an extensive script which automates several core level spectra calculations to improve the overall agreement with experiment – neat stuff!

Of course, there are many other enhancements in the quantum mechanics tools including major performance improvements for ONETEP, new elements in AM1* for VAMP, and extensions to QMERA that have been delivered from the Nanotechnology Consortium. If you want to read more about these, check out the “What’s new in Materials Studio 5.0″ on our website.

Next time, I want to jump a few size scales up to the mesoscale.

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Take the leap: Materials Studio 5.0

October 16th, 2009 by Gerhard Goldbeck-Wood, PhD

Just back from the EUGM and Nanotech Consortium Meeting, a week of lively discussions (and foosball matches ;-) and of course our announcement of the release of Materials Studio 5.0. It’s been great finally to talk about and demo all the new features, which we are all so excited about. Getting the requests in for shipment of the new version already … well, it won’t be long.

You can read more about Materials Studio 5.0 at a high level in our Press Release, or in more detail in our ‘What’s New’ document. Perhaps you have read the ‘Transforming Materials Modeling’ tag line in there: imagine the discussions we’ve had about that: “Is it really?” “What is transforming…” and so on. But honestly it is what we are aiming to do with Materials Studio, and there are many things in the 5.0 release that make a real difference.

My take right now from the discussions at the Consortium and User Group Meetings is that the efficiency you gain because of the integration and flexibility this new release provides is quite a step change. The new Amorphous Cell for example got some wows from Materials Science and Life Science folks alike. It’s really a kind of universal structure builder. Want to build a nanocomposite, for example with nanotubes and polymers around them: not a problem. And perhaps there is some small molecule inside the tube: easy.  And what about a protein soaked in a solution: consider it done!

For the second ‘transforming’ example, for me it’s Kinetix, the new Kinetic Monte Carlo module we built for the Nanotech Consortium. I alluded to Kinetic Monte Carlo development earlier, and thanks to a great collaboration with Tonek Jansen and Johan Lukkien from TU Eindhoven, you can now simulate processes such as a Fuel Cell cathode reaction in Materials Studio, over real time scales of minutes. Considering we start at femtoseconds, that’s quite a leap anyway.

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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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