Welcome to the new version of CaltechAUTHORS. Login is currently restricted to library staff. If you notice any issues, please email coda@library.caltech.edu
Published September 11, 2018 | Submitted
Journal Article Open

Transferability in Machine Learning for Electronic Structure via the Molecular Orbital Basis

Abstract

We present a machine learning (ML) method for predicting electronic structure correlation energies using Hartree–Fock input. The total correlation energy is expressed in terms of individual and pair contributions from occupied molecular orbitals, and Gaussian process regression is used to predict these contributions from a feature set that is based on molecular orbital properties, such as Fock, Coulomb, and exchange matrix elements. With the aim of maximizing transferability across chemical systems and compactness of the feature set, we avoid the usual specification of ML features in terms of atom- or geometry-specific information, such atom/element-types, bond-types, or local molecular structure. ML predictions of MP2 and CCSD energies are presented for a range of systems, demonstrating that the method maintains accuracy while providing transferability both within and across chemical families; this includes predictions for molecules with atom-types and elements that are not included in the training set. The method holds promise both in its current form and as a proof-of-principle for the use of ML in the design of generalized density-matrix functionals.

Additional Information

© 2018 American Chemical Society. Received: June 24, 2018; Published: July 24, 2018. This work was supported by AFOSR award no. FA9550-17-1-0102. The authors additionally acknowledge support from the Resnick Sustainability Institute postdoctoral fellowship (M.W.), a Caltech Chemistry graduate fellowship (L.C.), and the Camille Dreyfus Teacher-Scholar Award (T.F.M.). The authors declare no competing financial interest.

Attached Files

Submitted - 1806.00133.pdf

Submitted - ct8b00636_si_001.pdf

Files

1806.00133.pdf
Files (8.4 MB)
Name Size Download all
md5:35561e62096a2f35a1103c586d3eba4f
6.5 MB Preview Download
md5:7233996bd2f0cd793dfe298c0e8dbd64
1.9 MB Preview Download

Additional details

Created:
August 19, 2023
Modified:
October 18, 2023