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Michael Scherbela
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Solving the electronic Schrödinger equation for multiple nuclear geometries with weight-sharing deep neural networks
M Scherbela, R Reisenhofer, L Gerard, P Marquetand, P Grohs
Nature Computational Science 2 (5), 331-341, 2022
462022
Structure prediction for surface-induced phases of organic monolayers: overcoming the combinatorial bottleneck
V Obersteiner, M Scherbela, L Hörmann, D Wegner, OT Hofmann
Nano Letters 17 (7), 4453-4460, 2017
352017
SAMPLE: Surface structure search enabled by coarse graining and statistical learning
L Hörmann, A Jeindl, AT Egger, M Scherbela, OT Hofmann
Computer Physics Communications 244, 143-155, 2019
342019
Charge Transfer into Organic Thin Films: A Deeper Insight through Machine‐Learning‐Assisted Structure Search
AT Egger, L Hörmann, A Jeindl, M Scherbela, V Obersteiner, M Todorović, ...
Advanced Science 7 (15), 2000992, 2020
322020
Gold-standard solutions to the Schrödinger equation using deep learning: How much physics do we need?
L Gerard, M Scherbela, P Marquetand, P Grohs
Advances in Neural Information Processing Systems 35, 10282-10294, 2022
242022
Charting the energy landscape of metal/organic interfaces via machine learning
M Scherbela, L Hörmann, A Jeindl, V Obersteiner, OT Hofmann
Physical Review Materials 2 (4), 043803, 2018
242018
Towards a transferable fermionic neural wavefunction for molecules
M Scherbela, L Gerard, P Grohs
Nature Communications 15 (1), 120, 2024
13*2024
Variational Monte Carlo on a Budget—Fine-tuning pre-trained Neural Wavefunctions
M Scherbela, L Gerard, P Grohs
Advances in Neural Information Processing Systems 36, 2024
22024
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