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 the Lawrence Berkeley National Laboratory's NERSC Scientific Computing Center and Computational Research Division provide the infrastructure that enables our computations, data, and algorithms to run at unparalleled speed
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.
Chancellor's Professor, Department of Materials Science and Engineering, University of California at Berkeley
Faculty Fellow, Lawrence Berkeley National Laboratory
Assistant Professor, Department of Materials Science and Engineering, University of California at Berkeley
Staff Scientist, Lawrence Berkeley National Laboratory
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.
The Materials Project is also supported by a Laboratory Directed Research and Development grant from LBNL. Disseminated science is supported by DOE (BES and BMR), the National Science Foundation (NSF), Gillette, Umicore, and Bosch.