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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- Text
- Journal contribution
- Biochemistry
- Genetics
- Molecular Biology
- Biotechnology
- Evolutionary Biology
- Computational Biology
- Biological Sciences not elsewhere classified
- Mathematical Sciences not elsewhere classified
- Chemical Sciences not elsewhere classified
- Information Systems not elsewhere classified
- exchange matrix elements
- geometry-specific information
- Gaussian process regression
- pair contributions
- ML features
- method
- correlation energy
- ML predictions
- structure correlation energies
- density-matrix functionals
- chemical systems
- chemical families
- Electronic Structure
- term
- transferability
- CCSD energies
- Machine Learning
- Molecular Orbital Basis
- MP 2