A machine learning potential with low error in the potential energies does not guarantee good performance for the simulations. One of the reasons is that it is hard to train machine learning potentials with balanced descriptions of different PES regions, …
Our work published in Scientific Data presents the WS22 database, which contains 10 flexible organic molecules of increasing complexity in chemical composition and accessible conformations. The WS22 database provides 1.18 million equilibrium and non-equilibrium molecular geometries together with many quantum …
Materials can simultaneously absorb not just one but two photons and molecules with strong two-photon absorption (TPA) are important in many fields such as unconverted laser, photodynamic therapy, and 3D printing. In our work published in Advanced Science (open access), …
Explaining and Predicting Two-Photon Absorption with Machine Learning Read more »
Lost in the sea of all machine learning potentials? Our overview and recommendations based on balanced analysis are just out in Chemical Science. In brief, kernel methods are a better choice for not too large data in terms of both …