Deep-learning approach for the atomic configuration interaction problem on large basis sets

[2209.05867] Deep-learning approach for the atomic configuration interaction problem on large basis sets Skip to main content Search Submit Donate Log in Physics > Atomic Physics arXiv:2209.05867 (physics) [Submitted on 13 Sep 2022 ( v1 ), last revised 21 Jun 2023 (this version, v2)] Title: Deep-learning approach for the atomic configuration interaction problem on large basis sets Authors: Pavlo Bilous , Adriana Pálffy , Florian Marquardt View PDF HTML (experimental) Abstract: High-precision atomic structure calculations require accurate modelling of electronic correlations typically addressed via the configuration interaction (CI) problem on a multiconfiguration wave function expansion. The latter can easily become challenging or infeasibly large even for advanced supercomputers. Here we develop a deep-learning approach which allows to preselect the most relevant configurations out of large CI basis sets until the targeted energy precision is achieved. The large CI computation is thereby replaced by a series of smaller ones performed on an iteratively expanding basis subset managed by a neural network. While dense architectures as used in quantum chemistry fail, we show that a convolutional neural network naturally accounts for the physical structure of the basis set and allows for robust and accurate CI calculations. The method was benchmarked on basis sets of moderate size allowing for the direct CI calculation, and further demonstrated on prohibitively large sets where the direct computation is not possible. Subjects: Atomic Physics (physics.atom-ph) ; Computational Physics (physics.comp-ph); Data Analysis, Statistics and Probability (physics.data-an) Cite as: arXiv:2209.05867 [physics.atom-ph] (or arXiv:2209.05867v2 [physics.atom-ph] for this version) https://doi.org/10.48550/arXiv.2209.05867 Focus to learn more arXiv-issued DOI via DataCite Related DOI : https://doi.org/10.1103/PhysRevLett.131.133002 Focus to learn more DOI(s) linking to related resources Submission history From: Pavlo Bilous [ view email ] [v1] Tue, 13 Sep 2022 10:29:38 UTC (40 KB) [v2] Wed, 21 Jun 2023 08:47:34 UTC (242 KB) Full-text links: Access Paper: View PDF HTML (experimental) TeX Source view license Current browse context: physics.atom-ph < prev | next > new | recent | 2022-09 Change to browse by: physics physics.comp-ph physics.data-an References & Citations INSPIRE HEP SciX Google Scholar Semantic Scholar export BibTeX citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab)

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