Tag: preprint

By Posted on Posted in Machine Learning in Chemistry, Method Development, News Tagged with , , , , , , ,

Are universal machine learning potentials for excited states possible? Such a potential would be a major breakthrough — enabling key applications like the design of advanced photomaterials. We’ve already seen successful universal potentials for ground states — ANI-1ccx, MACE-OFF, our …

Meet OMNI-P2x — the First Universal ML Potential for Excited States! Read more »

By Posted on Posted in Machine Learning in Chemistry, Method Development, News, Semiempirical Methods Tagged with , , , ,

Theoretical IR (infrared) spectroscopy is a powerful tool for assisting chemical structure identification. However, approaches based on quantum chemical calculations suffer from either high computational cost (e.g., density functional theory, DFT) or insufficient accuracy (semi-empirical methods).  Hence, we introduce a new …

ML-enhanced Fast and Interpretable Simulation of IR Spectra Read more »