Thrust 1 - AI/ML-assisted materials discovery and design framework
(Li (Co-leader), Wang (Co-leader), Xia, Moraru, Ye, Yao, Antropov, Richard)
This thrust develops an AI/ML-assisted framework on high-performance and exascale computers to significantly accelerate discovery and design for functional materials with a focus on emergent magnetic and superconducting compounds. Our framework integrates AI/ML tools such as crystal graph convolutional neural network (CGCNN), artificial neural network (ANN) ML interatomic potentials, and materials databases, with state-of-the-art computational methods including adaptive genetic algorithm (AGA) and first-principles calculations on high-performance and exascale computers. Our implementation leverages recent GPU-based ANNs and first-principles calculation codes, and a parallel, task-based workflow manager, Parsl. This approach significantly reduces the time to-solution for materials discovery and design through high-performance computing.
The framework is ready for applications to search for ternary compounds. The speedup of the framework scales almost linearly with the number of GPUs. For any ternary system with three chemical elements, it produces a map of comprehensive composition-structure-energy landscape in just a few hours. Fig. 1 shows the scaling performance for Na-B-C ternary compounds. About 1000 candidate structures were selected from CGCNN for first-principles calculations. Using 32 GPUs on Perlmutter at NERSC, the energy calculations completed in about 3-4 hours as shown in Fig. 1 (a). The almost linear strong scaling performance can also be seen from Fig. 1 (b). For most ternary systems, about 2000 candidate structures per system will be selected for DFT calculations, which will complete within a few hours using 64 GPUs. Another notable advantage of the framework is its capability to perform the search for an arbitrary number of ternary systems simultaneously, and the parallel computation can scale up to several thousand GPUs. This means the ML framework can predict stable and metastable compounds for 100 different ternary systems in a few hours if 6000 GPUs on Perlmutter at NERSC are available.
