23 citations · 28 across the 13 of their papers we have counts for
5 papers · 1 filter
Machine Learning Nonadiabatic Dynamics: Eliminating Phase Freedom of Nonadiabatic Couplings with the State-Intraction State-Averaged Spin-Restricted Ensemble-Referenced Kohn-Sham Approach
Sung Wook Moon, Soohaeng Yoo Willow, Tae Hyeon Park +2
Excited-state molecular dynamics (ESMD) simulations near conical intersections (CIs) pose significant challenges when using machine learning potentials (MLPs). Although MLPs have g…
A Bayesian Committee Machine Potential for Oxygen-containing Organic Compounds
Seungwon Kim, D. ChangMo Yang, Soohaeng Yoo Willow +1
Understanding the pivotal role of oxygen-containing organic compounds in serving as an energy source for living organisms and contributing to protein formation is crucial in the fi…
A Bayesian Committee Machine Potential for Organic Nitrogen Compounds
Hyun Gyu Park, Soohaeng Yoo Willow, D. ChangMo Yang +1
Large-scale computer simulations of chemical atoms are used in a wide range of applications, including batteries, drugs, and more. However, there is a problem with efficiency as it…
A Sparse Bayesian Committee Machine Potential for Hydrocarbons
Soohaeng Yoo Willow, Gyung Su Kim, Miran Ha +2
Accurate and scalable universal interatomic potentials are key for understanding material properties at the atomic level, a task often hindered by the steep computational scaling.…
Active Sparse Bayesian Committee Machine Potential for Isothermal-Isobaric Molecular Dynamics Simulations
Soohaeng Yoo Willow, Dong Geon Kim, R. Sundheep +3
Recent advancements in machine learning potentials (MLPs) have significantly impacted the fields of chemistry, physics, and biology by enabling large-scale first-principles simulat…