Michael Gastegger

Orcid: 0000-0001-7954-3275

According to our database1, Michael Gastegger authored at least 16 papers between 2018 and 2024.

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Bibliography

2024
Improved motif-scaffolding with SE(3) flow matching.
Trans. Mach. Learn. Res., 2024

Scaling up machine learning-based chemical plant simulation: A method for fine-tuning a model to induce stable fixed points.
Comput. Chem. Eng., 2024

2023
Prediction of Enzyme Catalysis by Computing Reaction Energy Barriers via Steered QM/MM Molecular Dynamics Simulations and Machine Learning.
J. Chem. Inf. Model., August, 2023

Scaling machine learning-based chemical plant simulation: A method for fine-tuning a model to induce stable fixed points.
CoRR, 2023

Relevant Walk Search for Explaining Graph Neural Networks.
Proceedings of the International Conference on Machine Learning, 2023

2022
Accurate Machine Learned Quantum-Mechanical Force Fields for Biomolecular Simulations.
CoRR, 2022

Automatic Identification of Chemical Moieties.
CoRR, 2022

2021
Inverse design of 3d molecular structures with conditional generative neural networks.
CoRR, 2021

SpookyNet: Learning Force Fields with Electronic Degrees of Freedom and Nonlocal Effects.
CoRR, 2021

SE(3)-equivariant prediction of molecular wavefunctions and electronic densities.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Equivariant message passing for the prediction of tensorial properties and molecular spectra.
Proceedings of the 38th International Conference on Machine Learning, 2021

2020
Combining SchNet and SHARC: The SchNarc machine learning approach for excited-state dynamics.
CoRR, 2020

2019
Quantum-Chemical Insights from Interpretable Atomistic Neural Networks.
Proceedings of the Explainable AI: Interpreting, 2019

Symmetry-adapted generation of 3d point sets for the targeted discovery of molecules.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

2018
Generating equilibrium molecules with deep neural networks.
CoRR, 2018

Quantum-chemical insights from interpretable atomistic neural networks.
CoRR, 2018


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