5 papers
Six Open Questions in Machine-Learned Interatomic Potential Foundation Models
Isabel Creed, Tim Rein, Ingvars Vitenburgs +21
Machine-learned interatomic potentials (MLIPs) have had a profound impact on molecular modelling in recent years, promising to resolve the long-standing tension between the scale a…
Nonadiabatic reactive scattering of hydrogen on different surface facets of copper
Wojciech G. Stark, Connor L. Box, Matthias Sachs +2
Dissociative chemisorption is a key process in hydrogen-metal surface chemistry, where nonadiabatic effects due to low-lying electron-hole-pair excitations may affect reaction outc…
Machine Learning and Data-Driven Methods in Computational Surface and Interface Science
Lukas Hörmann, Wojciech G. Stark, Reinhard J. Maurer
Nanoscale design of surfaces and interfaces is essential for modern technologies like organic LEDs, batteries, fuel cells, superlubricating surfaces, and heterogeneous catalysis. H…
Fine-tuning foundation models of materials interatomic potentials with frozen transfer learning
Mariia Radova, Wojciech G. Stark, Connor S. Allen +2
Machine-learned interatomic potentials are revolutionising atomistic materials simulations by providing accurate and scalable predictions within the scope covered by the training d…
NQCDynamics.jl: A Julia Package for Nonadiabatic Quantum Classical Molecular Dynamics in the Condensed Phase
James Gardner, Oscar A. Douglas-Gallardo, Wojciech G. Stark +4
Accurate and efficient methods to simulate nonadiabatic and quantum nuclear effects in high-dimensional and dissipative systems are crucial for the prediction of chemical dynamics…