Publications

A.M. Cooper, J. Kästner, A. Urban, und N. Artrith. Efficient training of ANN potentials by including atomic forces via Taylor expansion and application to water and a transition-metal oxide. Npj Comput. Mater., (6):54, 2020. [PUMA: EXC2075 chemie from:danielborn kaestner kästner pn3 stuttgart theochem theoretische] URL

Alexander Denzel, und Johannes Kästner. Hessian Matrix Update Scheme for Transition State Search Based on Gaussian Process Regression. J. Chem. Theory Comput., (16):5083-5089, 2020. [PUMA: EXC2075 chemie from:danielborn kaestner kästner pn3 stuttgart theochem theoretische] URL

V. Zaverkin, und J. Kästner. Gaussian Moments as Physically Inspired Molecular Descriptors for Accurate and Scalable Machine Learning Potentials. J. Chem. Theory Comput., (16):5410-5421, 2020. [PUMA: EXC2075 chemie from:danielborn kaestner kästner pn6 stuttgart theochem theoretische] URL