FPGA Accelerator for Machine Learning Interatomic Potential-Based Molecular Dynamics of Gold Nanoparticles

Abstract

Molecular dynamics (MD) simulations involve computations of forces between atoms and the total energy of the chemical systems. The scientific community is dependent on high-end servers for such computations that are generally sequential and highly power hungry, thereby restricting these computations in reaching experimentally relevant large systems. This work explores the concept of parallelization of the code and accelerating them by exploring the usage of high level synthesis (HLS) based Field Programmable Gate Array (FPGA). This work proposes a hardware and software based interface to implement parallel algorithms in an FPGA framework and communication between the software and hardware interface is implemented. The forces of Au 147 obtained through the ANN based interatomic potentials in the proposed model shows an acceleration of 1.5 times compared with an expensive server with several nodes. Taking this work forward can result in a lab-on-a-chip application and this would potentially be applied onto several large experimentally relevant chemical systems.