| Abstract Scope |
Molecular simulations aim to model the spatiotemporal behavior of atomistic systems throughout biology, chemistry, and materials science. Given the computational burden of running such simulations for long timescales, machine learning force fields, and particularly neural network interatomic potentials, are an attractive alternative to ab-initio methods. This talk will focus on addressing current machine learning challenges in this space, with an emphasis on broad learning strategies that are applicable across a wide variety of systems and neural network architectures. I will discuss training procedures to improve the stability of molecular simulations over time, as well as formulations of symmetry operations in neural networks to learn more accurate representations of these atomistic systems. The applicability of these methods extends to other scientific domains, including neural PDE solvers. Finally, I will discuss how even as we build larger datasets in the future, these learning strategies remain general, as well as independent to specific neural network architectures. |