By computing properties of all known materials, the Materials Project aims to remove guesswork from materials design in a variety of applications. Experimental research can be targeted to the most promising compounds from computational data sets. Researchers will be able to data-mine scientific trends in materials properties. By providing materials researchers with the information they need to design better, the Materials Project aims to accelerate innovation in materials research.
Supercomputing clusters at national laboratories provide the infrastructure that enables our computations, data, and algorithms to run at unparalleled speed. We principally use the Lawrence Berkeley National Laboratory's NERSC Scientific Computing Center and Computational Research Division, but we are also active with Oak Ridge's OLCF, Argonne's ALCF, and San Diego's SDSC.
Computational materials science is now powerful enough that it can predict many properties of materials before those materials are ever synthesized in the lab. By scaling materials computations over supercomputing clusters, we have predicted several new battery materials which were made and tested in the lab. Recently, we have also identified new transparent conducting oxides and thermoelectric materials using this approach.
Associate Professor, Department of Materials Science and
University of California at Berkeley
Staff Scientist, Lawrence Berkeley National Laboratory
GitHub users with pull requests merged to MP code repositories.
Development of the Materials Project is supported by the U.S. Department of Energy (DOE) through its Office of Science, via the Basic Energy Sciences (BES) and Advanced Scientific Computing Research (ASCR) programs, and through its Office of Energy Efficiency and Renewable Energy (EERE), via the Battery Materials Research (BMR, formerly BATT) program. A notable source of support within DOE-BES is the Joint Center for Energy Storage Research (JCESR).
The Materials Project is also supported by a Laboratory Directed Research and Development grant from LBNL, and by the U.S. National Science Foundation (NSF) via the Data Infrastructure Building Blocks (DIBBS) program. Disseminated science is supported by DOE (BES and BMR), NSF, Gillette, Volkswagen, Umicore, and Bosch.