2 citations · 2 across the 3 of their papers we have counts for
3 papers · 1 filter
How Accurately Can We Describe Spin Crossover?
Angel Albavera-Mata, Daniel Mej{í}a-Rodr{í}guez, Niranjan Govind +3
The complicated physicochemical properties of metal complexes that exhibit thermal spin crossover make it difficult for routine electronic structure calculations to yield an accura…
Electrostatic Phenomenology Benchmarks for Machine-Learned Interatomic Potentials in Electrochemistry: Beyond the Energy-Force Metric
Barbara Sumić, Ria Vasdev, Sudheesh Kumar Ethirajan +8
Accurate treatment of long-range interactions in machine learning interatomic potentials (MLIPs) is essential for electrochemical simulations. However, aggregate energy and force e…
Machine-Learned Interatomic Potential for Predictive Simulation of MoS2 Epitaxy
Emir Bilgili, Nicholas Taormina, Richard Hennig +2
A machine-learned interatomic potential (MLIP) for multilayer MoS2 was developed using the ultra-fast force field (UF3) framework. The UF3 MLIP reproduces key properties in strong…