28 citations · 28 across the 2 of their papers we have counts for
2 papers
physics.chem-ph2026
A Data-Driven Parametric Reduced-Order Chemical Kinetics Model Derived from Atomistic Simulations
Michael N. Sakano, Alejandro Strachan
Coarse-grained modeling in molecular simulations serves not only to extend accessible time and length scales beyond atomistic limits, but also to reduce high-dimensional chemical d…
cond-mat.mtrl-sci2023★ 28 cited
High Pressure and Temperature Neural Network Reactive Force Field for Energetic Materials
Brenden W. Hamilton, Pilsun Yoo, Michael N. Sakano +2
Reactive force fields for molecular dynamics have enabled a wide range of studies in numerous material classes. These force fields are computationally inexpensive as compared to el…