Michael Gastegger
Publications by Year
Research Areas
Machine Learning in Materials Science, Computational Drug Discovery Methods, Protein Structure and Dynamics, Various Chemistry Research Topics, Mass Spectrometry Techniques and Applications
Most-Cited Works
- → Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems(2021)776 cited
- → Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions(2019)473 cited
- → SchNetPack: A Deep Learning Toolbox For Atomistic Systems(2018)441 cited
- → SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects(2021)307 cited
- → wACSF—Weighted atom-centered symmetry functions as descriptors in machine learning potentials(2018)291 cited
- → Equivariant message passing for the prediction of tensorial properties and molecular spectra(2021)265 cited
- → Combining SchNet and SHARC: The SchNarc Machine Learning Approach for Excited-State Dynamics(2020)203 cited
- → Inverse design of 3d molecular structures with conditional generative neural networks(2022)189 cited
- → Roadmap on Machine learning in electronic structure(2022)176 cited
- → High-Dimensional Neural Network Potentials for Organic Reactions and an Improved Training Algorithm(2015)132 cited