I still remember those early days very clearly. Materials Studio 1.0 included the crystal builder and polymer building tools, and Discover, Amorphous Cell and Reflex modules. There was no quantum mechanics (2.0), no mesoscale (2.1 and 2.2), and no QSAR (3.0). However, even then, it was a pretty special product. I remember one of my colleagues and myself doing early testing of Discover and thinking that the calculation had failed – it was actually running so fast that we didn’t realize it had completed!

Back then, Materials Studio 1.0 ran on Windows 95, Windows 98, Windows NT, Red Hat Linux 6.0, and SGI IRIX 6.2. I am surprised we didn’t support Windows 3.1 as well! This rapidly changed as Windows 2000 was released and we had a widely adopted Windows platform. And it has been onwards and upwards since then with support for 64-bit Linux and current development of 64-bit Windows server executables.

And now we have the Materials Studio Collection as well as Materials Studio. This gives the opportunity to deliver the vision that first started with the release of Materials Studio – enabling non-experts to use modeling and simulation tools. Through the capabilities of Pipeline Pilot for web and Microsoft SharePoint deployment of protocols, we have the ability to offer simplified interfaces for property calculations but which still use the expert tools behind the scenes.

Over the years, I have been constantly amazed by what our customers and their imagination achieve with Materials Studio. I fully expect this to continue with the Materials Studio Collection and future versions of Materials Studio.

As Timbuk 3 once sung, “The futures so bright, I gotta wear shades”. I’ll be getting my new sunglasses next week.

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.

So why exactly do I live and work in one of the flattest areas of the UK, a city just south of an area  of former swampland that the natives lovingly call “The Fens?”

It’s a long story really, a story of science  and adventure initially, but I suppose I ended up here because Cambridge is both the seat of a world-famous university and a European innovation hub that benefits from the ready availability of smart people and a prestigious address. That, and the absence of valleys, turned a wet piece of English countryside into Europe’s “Silicon Fen.”

No wonder then that at some point, around  1990, a Cambridge University spinoff called Cambridge Molecular Design became one of the precursors of Accelrys and evolved into what is now our European Headquarters.

I had an opportunity to reminisce about Accelrys’ roots and connections with Cambridge on the occasion of a recent meeting with the CEO of Cambridge Network, Matt Schofield. Cambridge Network provides many invaluable services to the Silicon Fen community. It helps employers find the right employees, it organizes and helps companies host outstanding events , runs special interest groups, and it offers a range of member benefits and business development opportunities for its members. In short, it offers tools and information that bring the Cambridge community together.

Accelrys has deep roots within this community.  Some of our flagship Materials Science products such as CASTEP and ONETEP originated here and are being continuously enhanced by teams of researchers at Cambridge University and their collaborators, and we benefit from many other fruitful  partnerships such as the one with Cambridge Crystallographic Data Centre. Several Accelrys scientists, myself included, learned their trade as part of thriving university departments such as the Cavendish Laboratory. Many of our customers benefit from the close geographical proximity that the Silicon Fen provides; and several of our partners have a presence in Cambridge and surrounding areas.

But at the same time, the Cambridge Network meeting reminded me that even more could be done to join efforts with Cambridge companies and university departments. Globalization is wonderful, but nothing beats partners next door. So we’ll continue to look to for Silicon Fen collaborations. Which is easy thanks to Cambridge Network, and because there’s not a single mountain obstructing our view …

Future webinars will include customer case studies and deep dives into new functionalities found in CASTEP and Amorphous Cell.

To learn more and register, visit: http://accelrys.com/events/webinars/materials-studio-50/.

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.

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.

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).

• Bligaard (now at the T.U. Denmark) and co-workers described methods for alloy design;
• Farrusseng of the Institute of Research on Catalysis recently reviewed applications to catalysis;
• Closer to home, Mat Halls of Accelrys and colleagues at Mitsubishi Chemicals are doing research on improved electrolytes for lithium ion batteries.

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.