2 papers
physics.chem-ph2026
Machine Learning Interatomic Potentials: Advancing Open-Source Software for Efficient and Scalable Molecular Simulation
Christoph Brunken, Titouan Cormier, Lucien Walewski +15
Machine learning interatomic potentials (MLIPs) enable atomistic simulations with near ab initio accuracy at significantly reduced computational cost, but their broader adoption is…
cond-mat.str-el2025
Bayesian perspectives for quantum states and application to ab initio quantum chemistry
Yannic Rath, Massimo Bortone, George H. Booth
The quantum many-electron problem is not just at the heart of condensed matter phenomena, but also essential for first-principles simulation of chemical phenomena. Strong correlati…