An Improved Penalty-Based Excited-State Variational Monte Carlo Approach with Deep-Learning Ansatzes-Reference-Cited by-同舟云学术

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An Improved Penalty-Based Excited-State Variational Monte Carlo Approach with Deep-Learning Ansatzes

Published:2024-08-30 Issue: Volume: Page:
ISSN:1549-9618
Container-title:Journal of Chemical Theory and Computation
language:en
Short-container-title:J. Chem. Theory Comput.

Author:

Szabó P. Bernát¹^ORCID,Schätzle Zeno¹,Entwistle Michael T.¹^ORCID,Noé Frank¹²³⁴

Affiliation:

1. Department of Mathematics and Computer Science, FU Berlin, Arnimallee 6, Berlin 14195, Germany

2. Microsoft Research AI4Science, Karl-Liebknecht Str. 32, Berlin 10178, Germany

3. Department of Physics, FU Berlin, Arnimallee 14, Berlin 14195, Germany

4. Department of Chemistry, Rice University, Houston, Texas 77005, United States

Funder

Deutsche Forschungsgemeinschaft

Norddeutscher Verbund f?r Hoch- und H?chstleistungsrechnen

Publisher

American Chemical Society (ACS)

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jctc.4c00678

Reference61 articles.

1. Introduction: Theoretical Modeling of Excited State Processes

2. Wavelength-selective light-matter interactions in polymer science

3. Visible Light-Induced Excited-State Transition-Metal Catalysis

4. Photosynthesis: basics, history and modelling

5. The Usefulness of Time-Dependent Density Functional Theory to Describe the Electronic Spectra of Ti-Containing Catalysts

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