Nicholas Terrel

Machine Learning Interatomic Potentials (MLIPs), trained with Quantum Mechanics data, can model potential energy surfaces for molecular systems with very high accuracy and extreme speedups compared to reference quantum calculations, offering a powerful tool for studying complex chemical and biological systems. This work presents the LAMMPS-ANI interface, which scales our ANI neural network potential models for large systems, demonstrated with up to 100 million atoms across up to 128 NVIDIA A100 GPUs. The high performance of LAMMPS-ANI was achieved through a comprehensive code redesign, in-depth performance profiling, and advanced GPU performance optimizations. Our benchmarks show that ANI is 30 to 60 times faster than the state-of-the-art Allegro Model, emphasizing its speed and efficiency. We highlight our work in large-scale molecular dynamics using ANI potentials, presenting benchmark results for water boxes (up to 100 million atoms) and a solvated HIV capsid (44 million atoms). We also present results for accurately simulating complex reaction processes at unprecedented scales, such as methane combustion (300 thousand atoms) and early Earth chemistry experiment (228 thousand atoms) demonstrating the spontaneous formation of glycine.

In this work we introduce TorchANI 2.0, a significantly improved version of the free and open source TorchANI software package for training and evaluation of ANI (ANAKIN-ME) deep learning models. TorchANI 2.0 builds upon the foundation of its predecessor, while addressing its limitations and introducing new features. These changes greatly enhance its extensibility, performance, and suitability as a framework for developing models ready for molecular dynamics applications. These improvements include the introduction of a modular system to add arbitrary pairwise potentials to models, CUDA-accelerated optimization for faster and more memory-efficient calculation of local atomic features, and a batched system for better performance of network ensembles, among others. Our benchmarks demonstrate that TorchANI 2.0 achieves significant speedup over previous versions in both training and inference, and the library enhancements allow users to train physically constrained models that better represent important qualities of chemical systems. We demonstrate this by introducing three new ANI models that incorporate these features and evaluating their capabilities