Oliver T. Unke
Google (United States)(US)
Publications by Year
Research Areas
Machine Learning in Materials Science, Protein Structure and Dynamics, Computational Drug Discovery Methods, Advanced Chemical Physics Studies, Spectroscopy and Quantum Chemical Studies
Most-Cited Works
- → PhysNet: A Neural Network for Predicting Energies, Forces, Dipole Moments, and Partial Charges(2019)1,024 cited
- → SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects(2021)306 cited
- → Equivariant message passing for the prediction of tensorial properties and molecular spectra(2021)265 cited
- → Accurate global machine learning force fields for molecules with hundreds of atoms(2023)158 cited
- → Toolkit for the Construction of Reproducing Kernel-Based Representations of Data: Application to Multidimensional Potential Energy Surfaces(2017)132 cited
- → Biomolecular dynamics with machine-learned quantum-mechanical force fields trained on diverse chemical fragments(2024)97 cited
- → A reactive, scalable, and transferable model for molecular energies from a neural network approach based on local information(2018)97 cited
- → A Euclidean transformer for fast and stable machine learned force fields(2024)66 cited
- → Reactive dynamics and spectroscopy of hydrogen transfer from neural network-based reactive potential energy surfaces(2020)63 cited
- → Exhaustive state-to-state cross sections for reactive molecular collisions from importance sampling simulation and a neural network representation(2019)56 cited