Richard G. Hennig

Benchmarking of fast and interpretable UF machine learning potentials

Machine learning interatomic potentials (MLIPs) have emerged as a powerful alternative to density functional theory (DFT) for molecular dynamics simulations, offering near-DFT accuracy at a fraction of the computational cost. However, many state-of-the-art MLIPs remain computatio...

How Accurately Can We Describe Spin Crossover?

Abstract The complicated physicochemical properties of metal complexes that exhibit thermal spin crossover make it difficult for routine electronic structure calculations to yield an accurate transition temperature prediction, T1/2. The difficulty lies in the intricate connection...