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.

In the first part of this series, we discussed the basic collection of cloud offerings and what type of value they provide to IT, Developers, and Customers.  The second part explained some of the Business Issues when leveraging the cloud from with your Enterprise environment.  In this post, we’ll focus more on the various services models that are associated with the Cloud.

Even within an ASP, there will be a range of providers.  Let’s take Accelrys Pipeline Pilot (PP) for example.  It’s a product that provides rich data-flow capabilities and has a specialty in science computing.   Today, most customers deploy PP on-premise by either the Research & Development (R&D) Information Technology (IT) department or by a group of scientists.  PP makes using and managing the platform in either of these scenarios extremely easy, yet powerfully scalable.  Regardless of how easy it is to manage PP, there are other concerns one must have when managing any platform or application.  This includes maintenance, backup and recovery, security, data management, etc.  Not to mention supporting an ever growing user base also looking to leverage Pipeline Pilot.  This could result in time being taken away from your main business driver: Science!

Taking the step to move your basic deployment into the “Cloud,” such as Amazon Web Service (AWS), is an interesting first start.  Now you don’t have to worry about the Operation System and everything underneath it (e.g. hardware, cooling, power, etc.), but you’re still left with everything else.  This is where Application Service Providers (ASP) comes into play.  An ASP can come in different packages.  For one, the ASP could actually be a group internally to your business. OK, so they’re not “really” an ASP, but they could function as one as they provide the service for a given cost and they’re not directly tied to your organization.  Hey, could this be Corporate IT?  Sure, or perhaps another scientific group within your business offering their investment to another team and doing cross charging to offset the costs.  And by the way, doing this in the cloud to remove the burden and cost from IT to manage it.  Perhaps this scenario has too many moving parts for your fancy.  I’ll move on.

A more traditional ASP manages the application and perhaps even provides application level support.  Taking Pipeline Pilot as an example again, providing application support really comes in two flavors.  The first is supporting the application platform and tools themselves; for instance, if you’re writing a protocol (a set of tasks in a data pipeline) or running an application built on PP.  The other is more focused on the science itself and relating it to the product.  While there may be many that could help with the PP Platform, Infrastructure, and even the tools, it’s a big leap to also support the science.  The key here for you is, when shopping for a cloud vendor or ASP take a look at the breath of services you’ll get from them and anticipate your need for science, application, and infrastructure support.  Not to mention the difference in cloud infrastructure that requires a Message Passing Interface (MPI) infrastructure (more on that later).

If you’re in any stage of interest, planning, evaluating, or deploying Accelrys products or other scientific applications in the Cloud, we’d love to hear from you!  As the leading provider of Scientific Informatics Solutions, we’re interested in supporting our customers no matter where there environment is – at home or in the cloud.  Visit our forums to continue the discussion: http://accelrys.org/

To view all Conrad’s Cloud Series posts, please visit: http://blog.accelrys.com/author/conrad/

There were several invited talks on semiconductors and catalyst nano particles, apart from my talk on alternate energy.  Many of the speakers discussed the suitability of a particular simulation approach for the study of specific applications, while others discussed the most recent state-of-the-art theoretical advances to tackle real problems at several timescales.  It is particularly challenging when simulations are to be used not just for gaining insights into a system but to be a predictive tool as well as for virtual screening.  While virtual screening is a well-studied art in the world of small molecule drug discovery, this is only now gaining traction in the materials world.

For further inight into virtual screening in materials, check out George Fitzgerald’s webinar on High-throughput Quantum Chemistry and Virtual Screening for Lithium Ion Battery Electrolyte Materials, next Wednesday, March 16.

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.

In the first part of this series, we discussed the basic collection of cloud offerings and what type of value they provide to IT, Developers, and Customers.  In this post, we’ll focus more on the business issues when leveraging the cloud.

One of the biggest hurdles leveraging Cloud Services is around securing and transporting of the data.  There’s no single answer or solution to resolve these issues, and there is no shortage of webinars, papers, conferences, etc. that focus on this so I don’t think I need to dig into that (just yet).  But what’s important to recognize is that all of the Cloud vendors, security experts, and network providers are working to both provide an answer that meets your business and technical requirements but also earns your trust.  The best way to get over that hump is to learn more and try it out.

First try the cloud on non-critical but impactful tasks.  Then start to increase your usage of critical data, connect directly to internal data, and perform tasks that provide real business value.  This isn’t an original approach since it’s pretty much the typically evaluation or Proof of Concept (POC), but that’s exactly the point!  Driving a project like this is more than just technology based, as you’ll most likely involve many people within your organization, such as Legal, Finance, IT, and Security in order to plan and complete the project.  There will be lots of concerns from these various groups, many reasonable and some that just requires lots of education.  So make sure you invest in educating them on the basics of the Cloud first.  This will make the rest of the process much smoother, but not easier.

Second, you’ll need to consider network bandwidth usage and data storage costs.  All of the cloud vendors have some sort of fee when uploading, downloading, and storing your data.  When you first look at this, its penny’s per GB, but when dealing with large data volumes and data transactions (e.g. Read and Writing across the network) those costs can get pretty high.  So your first thought will be that cloud pricing is extremely high, but what you may not be factoring in is all the things the cloud vendor is doing for you that’s beyond just the price of the disk, network, cooling, and power.  The cloud vendors typically offer a high SLA, so that includes data replication, de-duplication, resiliency, continuity, and more.  And not to mention the staff, planning, and operations to make all of that happen.  If you compared that to your own infrastructure and added that to your internal per-GB cost of storage, you’ll most likely see that the Cloud is more affordable but that assumes your meeting the same level of SLA and process as the Cloud vendors which most are not.  That said, there are some applications and data that may not be a good fit for many of the cloud vendors because of the special nature of the application, massive data size with high volume transactions, high throughput requirements, legal requirements, and more.  But this is starting to be the exception versus the rule.

Many organizations are making the leap of putting their most trusted data into the cloud, and some are doing it without realizing the significance. Email and Sales force automation having been leading the charge in hosted applications and Software as a Service (SaaS) deployments.  Now think of it this way, if you can store all of your communications and customer records on the cloud, why can’t you do more?  By businesses taking this leap, they start to build trust in external parties maintaining and operating their business critical services.  In a recent report by Goldman Sachs, they note that customers see a “shift towards cloud unstoppable”.  http://news.cnet.com/8301-13846_3-10453066-62.html The trend towards cloud services and applications won’t be a complete rip and replace, business will look to the cloud as an extension of their overall enterprise architecture and infrastructure.

When comparing the many Cloud/IaaS vendors in the market today, it’s already moving towards mass commodity price points and common functionality.  And that’s great if you want to take a piece of existing traditional on-premise software and simply deploy it to the cloud.  What you have to look out for are pitfalls in the software license, security, deployment architectures, and the fact that you’re still responsible for managing that software in the “Cloud”.  So the next layer to look for is a Services vendor that can deliver you the application.  This can at times come from the vendor directly or through partner network supported by the vendor.  Each has their own value proposition and differences in how flexible they can be delivering additional custom services.   Again, this type of application + service model isn’t new as the Application Service Provider (ASP) model has been around for years.  What’s new is that these ASP’s can still provide lots of value and cost reduction to the customer but now leveraging computing and storage that provided by a “Cloud” offering (e.g. AWS).

In the next part of this blog series, we’ll focus more on the various services models that will be available to customers based on a cloud version of Pipeline Pilot.

To view all Conrad’s Cloud Series posts, please visit: http://blog.accelrys.com/author/conrad/

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 Mg_2.5VMoO₈.

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.

Now let me quickly tackle a common question.  “What’s the difference between Amazon Web Services, Microsoft Azure, and Salesforce?  Aren’t they all the same?”  First off, this is a great question, but it’s really comparing apples, oranges, and tomatoes.  Yes, those are all fruits but each provide something very different to the consumer.  Where Clouds are different than fruit is that you can layer some of the clouds to deliver a service.  Remember that AWS is a Utility.  Microsoft Azure is a resource targeted towards developers.  Developers are different than IT and therefore have different requirements.  They like to write applications that typically consume some data and provide a User Interface.  They don’t want to be bothered with patch management, monitoring systems, deployment of servers, etc.  Microsoft Azure abstracts this from the developer.  They instead write to the “Fabric” of the Cloud Computing platform that Microsoft manages, which allows the developer focus on what they do best.  Finally, with Salesforce.com it’s even further abstracted.  You still have developers that can write applications based on Salesforce.com, but the developer is given even more constraints on what they can develop and how it can be implemented.

OK, enough of the Cloud Tutorial, but hopefully you have an understanding that there are many different types of clouds and how they can be used.  Are there challenges to adoption? You bet!  But there are always challenges when adopting technology.  While the above was about the technology, there are a number of business issues, concerns and questions that need to be addressed as well.  In the case of many organizations, one of the biggest hurdles is around securing and transporting of the data.

In the coming weeks, we’ll provide an update on our roadmap for leveraging, supporting, and providing guidance on using Cloud Computing and Virtualization technologies.  Accelrys has already been moving forward to partner with a number of Cloud vendors, Service Providers, and third-party software vendors to ensure our customer have the power of choice, delivery models, and a clear path to leverage Accelrys products in the cloud.

If you’re in any stage of interest, planning, evaluating, or deploying Accelrys products or other scientific applications in the Cloud, we’d love to hear from you!  As the leading provider of Scientific Informatics Solutions, we’re interested in supporting our customers no matter where there environment is – at home or in the cloud.  Visit our forums to continue the discussion: http://accelrys.org/

In the next part of this blog series, I’ll focus on the Business Issues found with leveraging the cloud.

To view all Conrad’s Cloud Series posts, please visit: http://blog.accelrys.com/author/conrad/

The scientific community is seeing an explosion of outsourcing, collaboration, massive data production and consumption, and financial pressures.  Driven by these challenges, Research & Development Information Technology (R&D IT) and even the scientists themselves are looking to the potential of Cloud Computing to enable an increase in science innovation and allow R&D IT to provide higher valued service along with reduced costs.  Cloud Computing isn’t a “silver bullet” to solve these challenges, but it does provides the tools to address many of these key business drivers.

I’m sure many of you have seen the benefits of the cloud such as cost reduction, cost management, on demand, and scalability.  But what does this mean in the context of a Scientist using a product such as Pipeline Pilot?  Before we can get into the specifics of how Cloud Computing will provide value to a Science organization, let’s first get the terminology straight.  This won’t be a deep dive into each area, but just a quick primer.

First off let’s just all agree that “Cloud Computing” is a pretty generic term and it actually comes in many different forms.  Here are some terms loosely used and thrown around with common examples:

• Platform Virtualization – Virtualization of computers or operating systems. It hides the physical characteristics of a computing platform from users, instead showing another abstract computing platform.
  • VMWare vSphere, Microsoft Hyper-V, Citrix XenServer

• Grid Computing – Combination of computer resources from multiple administrative domains applied to a common task, usually to a scientific, technical or business problem that requires a great number of computer processing cycles or the need to process large amounts of data.
  • Microsoft HPC, Sun Grid

• Managed Hosting – A dedicated hosting service, dedicated server, or managed hosting service is a type of Internet hosting in which the client leases an entire server not shared with anyone.

• Utility Computing (Cloud)- packaging of computing resources, such as computation and storage, as a metered service similar to a traditional public utility (aka Infrastructure as a Service)
  • Amazon EC2, Rackspace Cloud, GoGrid

• Platform as a Service (PaaS) – a computing platform and/or solution stack as a service, generally consuming cloud infrastructure and supporting cloud applications.
  • Microsoft Azure Services, Google App, Rackspace Cloud Apps

• Software as a Service (SaaS) – model of software deployment whereby an Application Service Provider (ASP)  licenses an application to customers for use as a service on demand
  • Salesforce.com

• Software plus Services (S+S) – combining hosted services with capabilities that are best achieved with locally running software.
  • Microsoft Exchange Hosted Services, Google Message Labs

That’s a pretty quick and dirty listing of terms, so I’ll add a little context next time…

To view all Conrad’s Cloud Series posts, please visit: http://blog.accelrys.com/author/conrad/

We have just launched an initiative to help scientists around the world with support for Discovery Studio and its integration with Pipeline Pilot through the New Discovery Studio Open Hour!

Discovery Studio Open Hour is an open session hosted by an Accelrys scientist to answer any questions or queries you might have with regard to Discovery Studio. These sessions are completely open and FREE to attend and do not require any previous registration at all.

Drop in anytime and stay as long as you like. These sessions are open for 1 hour and you can drop in for 10 minutes or you can stay for the entire hour. Science is never black or white, so if you feel like brainstorming an idea or need to get advice on a workflow, dial in and you’ll be connected to an expert! New to Discovery Studio and don’t really know how to take advantage of this powerful architecture? Our support scientists will help demonstrate how customized solutions can be easily developed. And with the growing number of custom DS scripts and protocols on our Accelrys Community forum, you may just find what you are looking for and the DS Open Hour would be a great time for further discussions!

Have a question about the FREE DSVisualizer? This might be the best forum to get started or ask questions and have fun learning about tips and tricks with the product!

For now, sessions are hosted the second Tuesday of each month in 2010. (Jan 12, Feb 9, March 9, April 13, May 11, June 8, July 13, Aug 10, Sept 14, Oct 12, Nov 9, Dec 14) and the website has all the webex and conference call details you’ll need.  Add the dates to your calendar …  we’ll see you then!

I had a really tough time choosing which talks to attend but mostly settled on Molecular Diagnostics, Personalized Diagnostics, Cancer Profiling and Pathways, and Informatics Systems.  It was painful to miss the RNA Interfere, Cancer Biologics, and Translational Medicine sessions.  Many talks totally rocked.  Here are some of my favorites, in no particular order.  These talks come to mind because the material was fascinating, the delivery was exceptional, and they were all in areas for which I have a passionate scientific interest.

• Single Molecule Real Time Biology: New technologies Enabling a More Complete Characterization of Disease Biology, Eric Schadt, Ph.D., Chief Scientific Officer, Pacific Biosciences
• The Onco-SNP and Cancer Risk: microRNA Binding Site Polymorphisms as Biomarkers, Joanne B. Weidhaas, Ph.D., Assistant Professor, Therapeutic Radiology, Yale University
• Expression Based Patient Stratification for Cancer Prognostics, Peter J. van der Spek, Ph.D., Department of Bioinformatics, Erasmus MC – Medical Faculty
• Consumers and Their Genomes, Brian Naughton, Ph.D., Founding Scientist, 23andMe
• Systematic Discovery of Cancer Gene Fusions using Paired End Transcriptome Sequencing, Chandan Kumar, Ph.D., Michigan Center for Translational Pathology, University of Michigan
• Enterprise Scientific Workflow Environment Drives Innovation, Daniel J. Chin, Ph.D., Senior Principal Research Scientist, Roche Palo Alto

This year I presented a poster on biomarkers ala Pipeline Pilot™, attended talks, and caught up with professional colleagues.  The Outrageous Character awards affectionately (and respectfully) go to Eric Schadt and Peter van der Spek.  The Thank You award goes to Daniel for his kind words about our work together.  The Exquisite Explanation awards go Joanne and Chandan.  They did an amazing job of bridging any gaps in the audience’s varied background by presenting technical concepts in essential simplicity—truly beautiful.  Brian gets the award for my Favorite DTC Genetics Company.  I have spent many hours studying my own SNPs data (and that of my family members) thanks to 23andme.   I have derived much pleasure from connecting with relatives, all over the world, that I found through the 23andme site.   I am very grateful that I was able to get this type of genetic information AND the raw data, too.