Zeno Schätzle

Berlin Mathematical School — Germany

Materials Science · Physical Sciences

3h-index42citations19works2.32yr avg
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Research Topics

Machine Learning in Materials Science(12), Advanced Chemical Physics Studies(6), Electron and X-Ray Spectroscopy Techniques(5), Spectroscopy and Quantum Chemical Studies(3), Seismic Imaging and Inversion Techniques(2)

Publications19 total

Partitioning the electronic wave function using deep variational Monte Carlo
The Journal of Chemical Physics·2025Open Access
Deep quantum Monte Carlo approach for polaritonic chemistry
The Journal of Chemical Physics·2025· 2 citedOpen Access
An ab initio foundation model of wavefunctions that accurately describes chemical bond breaking
ArXiv.org·2025· 1 citedOpen Access
Highly accurate real-space electron densities with neural networks
The Journal of Chemical Physics·2025· 7 cited
Highly Accurate Real-space Electron Densities with Neural Networks
arXiv (Cornell University)·2024Open Access
An Improved Penalty-Based Excited-State Variational Monte Carlo Approach with Deep-Learning Ansatzes
Journal of Chemical Theory and Computation·2024· 6 citedOpen Access
An improved penalty-based excited-state variational Monte Carlo approach with deep-learning ansatzes
arXiv (Cornell University)·2024Open Access
DeepQMC: An open-source software suite for variational optimization of deep-learning molecular wave functions
The Journal of Chemical Physics·2023· 23 citedOpen Access
Electronic excited states in deep variational Monte Carlo
Nature Communications·2023Open Access
DeepQMC: An open-source software suite for variational optimization of deep-learning molecular wave functions
arXiv (Cornell University)·2023Open Access
Data for the study "Electronic excited states in deep variational Monte Carlo"
2022Open Access
Data for the study "Electronic excited states in deep variational Monte Carlo"
Zenodo (CERN European Organization for Nuclear Research)·2022Open Access
Data for the study "Electronic excited states in deep variational Monte Carlo"
Zenodo (CERN European Organization for Nuclear Research)·2022Open Access
Approaching exact solutions of the electronic Schrödinger equation with deep quantum Monte Carlo
Bulletin of the American Physical Society·2021
deepqmc/deepqmc: DeepQMC 0.3.0
Zenodo (CERN European Organization for Nuclear Research)·2021· 1 citedOpen Access
deepqmc/deepqmc: DeepQMC 0.1.1
Zenodo (CERN European Organization for Nuclear Research)·2020· 1 citedOpen Access
Raw data for “Deep neural network solution of the electronic Schrödinger equation”
Figshare·2020Open Access
Raw data for “Deep neural network solution of the electronic Schrödinger equation”
Figshare·2020Open Access
Deep neural network solution of the electronic Schrödinger equation
arXiv (Cornell University)·2019· 1 citedOpen Access

Frequent Co-authors

Frank Noé(18), Jan Hermann(15), P. Bernát Szabó(8), Paolo Andrea Erdman(6), Mike Entwistle(5), Matěj Mezera(4), Lixue Cheng(3), Jonas Köhler(3), Adam Foster(3), Derk P. Kooi(2), Klaas J. H. Giesbertz(2), Paola Gori‐Giorgi(2), Michael Entwistle(1), Yifan Tang(1), Gian Marcello Andolina(1), Alice Cuzzocrea(1), Gino Cassella(1), Nicholas Gao(1), Jiawei Li(1), Huziel E. Sauceda(1)