Stefan Chmiela
Berlin Institute for the Foundations of Learning and DataTechnische Universität Berlin(DE)
Publications by Year
Research Areas
Machine Learning in Materials Science, Protein Structure and Dynamics, Computational Drug Discovery Methods, Force Microscopy Techniques and Applications, Model Reduction and Neural Networks
Most-Cited Works
- → Quantum-chemical insights from deep tensor neural networks(2017)1,462 cited
- → Machine learning of accurate energy-conserving molecular force fields(2017)1,192 cited
- → Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems(2021)776 cited
- → SchNet: A continuous-filter convolutional neural network for modeling quantum interactions(2017)470 cited
- → SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects(2021)307 cited
- → sGDML: Constructing accurate and data efficient molecular force fields using machine learning(2019)237 cited
- → Accurate global machine learning force fields for molecules with hundreds of atoms(2023)158 cited
- → Machine Learning Meets Quantum Physics(2020)145 cited
- → BIGDML—Towards accurate quantum machine learning force fields for materials(2022)73 cited