Literature by the same author
plus at Google Scholar

Bibliografische Daten exportieren
 

Machine-Learning Coupled Cluster Properties through a Density Tensor Representation

Title data

Peyton, Benjamin G. ; Briggs, Connor ; D'Cunha, Ruhee ; Margraf, Johannes T. ; Crawford, T. Daniel:
Machine-Learning Coupled Cluster Properties through a Density Tensor Representation.
In: The Journal of Physical Chemistry A. Vol. 124 (2020) Issue 23 . - pp. 4861-4871.
ISSN 1520-5215
DOI: https://doi.org/10.1021/acs.jpca.0c02804

Abstract in another language

The introduction of machine-learning (ML) algorithms to quantum mechanics enables rapid evaluation of otherwise intractable expressions at the cost of prior training on appropriate benchmarks. Many computational bottlenecks in the evaluation of accurate electronic structure theory could potentially benefit from the application of such models, from reducing the complexity of the underlying wave function parameter space to circumventing the complications of solving the electronic Schrödinger equation entirely. Applications of ML to electronic structure have thus far been focused on learning molecular properties (mainly the energy) from geometric representations. While this line of study has been quite successful, highly accurate models typically require a “big data” approach with thousands of training data points. Herein, we propose a general, systematically improvable scheme for wave function-based ML of arbitrary molecular properties, inspired by the underlying equations that govern the canonical approach to computing the properties. To this end, we combine the established ML machinery of the t-amplitude tensor representation with a new reduced density matrix representation. The resulting model provides quantitative accuracy in both the electronic energy and dipoles of small molecules using only a few dozen training points per system.

Further data

Item Type: Article in a journal
Refereed: Yes
Institutions of the University: Faculties > Faculty of Biology, Chemistry and Earth Sciences > Department of Chemistry > Chair Künstliche Intelligenz in der physiko-chemischen Materialanalytik
Faculties > Faculty of Biology, Chemistry and Earth Sciences > Department of Chemistry > Chair Künstliche Intelligenz in der physiko-chemischen Materialanalytik > Chair Künstliche Intelligenz in der physiko-chemischen Materialanalytik - Univ.-Prof. Dr. Johannes Theo Margraf
Result of work at the UBT: No
DDC Subjects: 500 Science > 540 Chemistry
Date Deposited: 13 Nov 2023 11:43
Last Modified: 13 Nov 2023 11:43
URI: https://eref.uni-bayreuth.de/id/eprint/87676