Abstract
The electronic structure of a material, such as its density of states (DOS), provides key insights into its physical and functional properties and serves as a valuable source of high-quality features for many materials screening and discovery workflows. However, the computational cost of calculating the DOS, most commonly with density functional theory (DFT), becomes prohibitive for meeting high-fidelity or high-throughput requirements, necessitating a cheaper but sufficiently accurate surrogate. To fulfill this demand, we develop a general machine learning method based on graph neural networks for predicting the DOS purely from atomic positions, six orders of magnitude faster than DFT. This approach can effectively use large materials databases and be applied generally across the entire periodic table to materials classes of arbitrary compositional and structural diversity. We furthermore devise a highly adaptable scheme for physically informed learning which encourages the DOS prediction to favor physically reasonable solutions defined by any set of desired constraints. This functionality provides a means for ensuring that the predicted DOS is reliable enough to be used as an input to downstream materials screening workflows to predict more complex functional properties, which rely on accurate physical features.
| Original language | English |
|---|---|
| Pages (from-to) | 4848-4855 |
| Number of pages | 8 |
| Journal | Chemistry of Materials |
| Volume | 34 |
| Issue number | 11 |
| DOIs | |
| State | Published - Jun 14 2022 |
Funding
This work (graph neural network prediction (GNN) of DOS for diverse materials) was performed at Oak Ridge National Laboratory's Center for Nanophase Materials Sciences, which is a US Department of Energy Office of Science User Facility. The Center for Understanding and Control of Acid Gas-Induced Evolution of Materials for Energy (UNCAGE-ME), an Energy Frontier Research Center funded by U.S. Department of Energy, Office of Science, Basic Energy Sciences supported extension of GNNs for DOS prediction. V.F. was also supported by a Eugene P. Wigner Fellowship at Oak Ridge National Laboratory. O.R.N.L. is operated by UT-Battelle, LLC., for the U.S. Department of Energy under Contract DEAC05-00OR22725. This research used resources of the National Energy Research Scientific Computing Center, supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231. This work (graph neural network prediction (GNN) of DOS for diverse materials) was performed at Oak Ridge National Laboratory’s Center for Nanophase Materials Sciences, which is a US Department of Energy Office of Science User Facility. The Center for Understanding and Control of Acid Gas-Induced Evolution of Materials for Energy (UNCAGE-ME), an Energy Frontier Research Center funded by U.S. Department of Energy, Office of Science, Basic Energy Sciences supported extension of GNNs for DOS prediction. V.F. was also supported by a Eugene P. Wigner Fellowship at Oak Ridge National Laboratory. O.R.N.L. is operated by UT-Battelle, LLC., for the U.S. Department of Energy under Contract DEAC05-00OR22725. This research used resources of the National Energy Research Scientific Computing Center, supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231.