Today, I am participating in a White House event highlighting the first results and next steps of the Materials Genome Initiative (MGI), which President Obama announced almost one year ago.
The name of this initiative is a riff on the Human Genome Project because it intends to marshal and organize significant scientific resources to gain a deep understanding of the structure and behavior of a vast array of materials. The goal is to help U.S. companies become more economically competitive by the application of discoveries in materials science to the development of new and improved products in a host of industries at a far greater speed and much lower cost than is currently possible.
IBM is well aware of the challenges in advancing materials science. IBM Research started the Battery 500 Project in 2009 to develop a new type of lithium-air battery technology that is expected to improve energy density tenfold -- dramatically increasing the amount of energy these batteries can generate and store. And we invented semiconductor silicon germanium, laying the groundwork for explosive advancement in wireless products.
There are a host of other projects in materials science that could lead to new desalinization membranes, development of biopolymers for medical applications and new materials used to break the memory bottleneck in advanced computers. The list goes on for considerable length, but the pioneering insight from IBM has been to advance material research by linking experimental techniques with large scale simulation and modeling.
To realize the goals of the MGI, it is essential that we build the right kind of supporting infrastructure. It needs to have three key characteristics:
- Massive compute power: Deep understanding of materials depends on an understanding of molecular structure and behavior under a wide array of stresses and forces. Modeling and simulation at the atomic level has been shown to generate keen insights into many materials, but massive amounts of compute power is often required. Using powerful supercomputers like the Blue Gene system has been very effective in conducting these types of simulations because we are able to explore millions of atoms in models of diverse materials. As the scale of the problem increases, understanding of the macro behavior of the target material deepens and the path to commercialization accelerates.
- Built for data and analytics: Modeling does not occur in a vacuum; the data that describes the underlying processes must be accommodated in the MGI infrastucture. In some cases, the sheer volume of data will present challenges to store and analyze. In other cases, the complexity of the data will warrant new models of organization and analysis. In all cases, the MGI infrastructure must ensure that data, analytics, modeling and simulation are inextricably linked in a way that leads to near real time understanding of very complex scientific problems. Compressing time to solution is what leads to competitive advantage.
- Collaborative: Material scientists must have the ability to collaborate widely. Sharing data and compute resources is a foundational requirement of the MGI. The computing infrastructure enabling this will also need to be secure and accommodate the presence of proprietary processes and insights from many of the likely industrial partners.
A key factor in these kinds of experiments is being able to use enough atoms that scientists get a realistic response from the simulation. Working with catalytic processes, for instance, the team at ANL has been able to model reactions involving about 1,000 atoms. With Mira, they’ll be able to model reactions involving tens of thousands of atoms.
Over at the Lawrence Livermore National Lab (LLNL), they will soon flip the switch on a 20-petaflop IBM Blue Gene/Q supercomputer named Sequoia. In 2005, researchers from LLNL and IBM were awarded the Gordon Bell Prize for pioneering materials science simulations, and the level of performance achieved, conducted on the Blue Gene/L supercomputer at LLNL. In that project, simulation capability was increased from thousands of atoms to millions of atoms, and the simulations still took many hours. With the advent of Sequoia, LLNL scientists will be able to run the same simulations in a few minutes -- or increase the fidelity of the model by adding 100s of millions more atoms.
In the late 1990's when the Human Genome Project was coming to fruition, a whole new industry -- bio-informatics -- was born. Hundreds of new companies burst into existence seemingly overnight. My belief is that we are at the cusp of a similar phenomenon with the MGI and IBM plans to be present at the dawn of a new age in materials science.
Other Materials Genome Initiative Projects
World Community Grid: Clean Energy Project