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Michael Scherbela
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2020 – today
- 2024
- [i5]Leon Gerard, Michael Scherbela, Halvard Sutterud, W. Matthew C. Foulkes, Philipp Grohs:
Transferable Neural Wavefunctions for Solids. CoRR abs/2405.07599 (2024) - 2023
- [c2]Michael Scherbela, Leon Gerard, Philipp Grohs:
Variational Monte Carlo on a Budget - Fine-tuning pre-trained Neural Wavefunctions. NeurIPS 2023 - [i4]Michael Scherbela, Leon Gerard, Philipp Grohs:
Towards a Foundation Model for Neural Network Wavefunctions. CoRR abs/2303.09949 (2023) - [i3]Michael Scherbela, Leon Gerard, Philipp Grohs:
Variational Monte Carlo on a Budget - Fine-tuning pre-trained Neural Wavefunctions. CoRR abs/2307.09337 (2023) - 2022
- [j2]Michael Scherbela
, Rafael Reisenhofer
, Leon Gerard
, Philipp Marquetand
, Philipp Grohs
:
Solving the electronic Schrödinger equation for multiple nuclear geometries with weight-sharing deep neural networks. Nat. Comput. Sci. 2(5): 331-341 (2022) - [c1]Leon Gerard, Michael Scherbela, Philipp Marquetand, Philipp Grohs:
Gold-standard solutions to the Schrödinger equation using deep learning: How much physics do we need? NeurIPS 2022 - [i2]Leon Gerard, Michael Scherbela, Philipp Marquetand, Philipp Grohs:
Gold-standard solutions to the Schrödinger equation using deep learning: How much physics do we need? CoRR abs/2205.09438 (2022) - 2021
- [i1]Michael Scherbela
, Rafael Reisenhofer
, Leon Gerard
, Philipp Marquetand, Philipp Grohs
:
Solving the electronic Schrödinger equation for multiple nuclear geometries with weight-sharing deep neural networks. CoRR abs/2105.08351 (2021)
2010 – 2019
- 2019
- [j1]Lukas Hörmann
, Andreas Jeindl
, Alexander T. Egger, Michael Scherbela
, Oliver T. Hofmann:
SAMPLE: Surface structure search enabled by coarse graining and statistical learning. Comput. Phys. Commun. 244: 143-155 (2019)
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