The Potential of Neural Network Potentials

Abstract

In the next half-century, physical chemistry will likely undergo a profound transformation, driven predominantly by the combination of recent advances in quantum chemistry and machine learning (ML). Specifically, equivariant neural network potentials (NNPs) are a breakthrough new tool that are already enabling us to simulate systems at the molecular scale with unprecedented accuracy and speed, relying on nothing but fundamental physical laws. The continued development of this approach will realize Paul Dirac's 80-year-old vision of using quantum mechanics to unify physics with chemistry and providing invaluable tools for understanding materials science, biology, earth sciences, and beyond. The era of highly accurate and efficient firstprinciples molecular simulations will provide a wealth of training data that can be used to build automated computational methodologies, using tools such as diffusion models, for the design and optimization of systems at the molecular scale. Large language models (LLMs) will also evolve into increasingly indispensable tools for literature review, coding, idea generation, and scientific writing.

Theoretical Design Strategies for Area-Selective Atomic Layer Deposition

“…Recently, researchers are actively developing automated transition state search algorithms, , and their application to surface and solid state reactions is anticipated. Also, machine-learning (ML) or artificial intelligence (AI) techniques are emergent in chemical materials science. − An application of ML toward surface chemical research is the generation of interatomic potentials or force fields with chemical level accuracy. − This approach utilizes machine learning to generate force fields based on large amounts of data set, often gathered using DFT calculations, aiming to provide results that are more accurate than those of conventional force fields. In particular, universal interatomic potential that covers most of the periodic table can facilitate large-scale simulations of chemical reactions involving diverse elements in large scales, eliminating the need for case-by-case optimization of the force fields. − , …”