Ground State Energy Functional with Hartree-Fock Efficiency and Chemical Accuracy.

@article{Chen2020GroundSE,
  title={Ground State Energy Functional with Hartree-Fock Efficiency and Chemical Accuracy.},
  author={Yixiao Chen and Linfeng Zhang and Han Wang and E Weinan},
  journal={The journal of physical chemistry. A},
  year={2020}
}
We introduce the Deep Post-Hartree-Fock (DeePHF) method, a machine learning based scheme for constructing accurate and transferable models for the ground-state energy of electronic structure problems. DeePHF predicts the energy difference between results of highly accurate models such as the coupled cluster method and low accuracy models such as the the Hartree-Fock (HF) method, using the ground-state electronic orbitals as the input. It preserves all the symmetries of the original high accuracy… 

Figures and Tables from this paper

Molecular-orbital-based Machine Learning for Open-shell and Multi-reference Systems with Kernel Addition Gaussian Process Regression

We introduce a novel machine learning strategy, kernel addition Gaussian process regression (KA-GPR), in molecular-orbital-based machine learning (MOB-ML) to learn the total correlation energies of

Implicit step-truncation integration of nonlinear PDEs on low-rank tensor manifolds

TLDR
A new class of implicit rank-adaptive algorithms for temporal integration of nonlinear evolution equations on tensor manifolds based on performing one time step with a conventional time-stepping scheme, followed by an implicit point iteration step involving a rank- Adaptive truncation operation onto a tensor manifold.

DeePKS+ABACUS as a Bridge between Expensive Quantum Mechanical Models and Machine Learning Potentials

TLDR
This work demonstrates that this issue can be largely alleviated with Deep Kohn-Sham (DeePKS), a ML-based DFT model, which can serve as a bridge between expensive QM models and ML potentials.

Molecular Dipole Moment Learning via Rotationally Equivariant Gaussian Process Regression with Derivatives in Molecular-orbital-based Machine Learning

This study extends the accurate and transferable molecular-orbital-based machine learning (MOB-ML) approach to modeling the contribution of electron correlation to dipole moments at the cost of

Accurate Molecular-Orbital-Based Machine Learning Energies via Unsupervised Clustering of Chemical Space

We introduce an unsupervised clustering algorithm to improve training efficiency and accuracy in predicting energies using molecular-orbital-based machine learning (MOB-ML). This work determines

Three-Dimensional Convolutional Neural Networks Utilizing Molecular Topological Features for Accurate Atomization Energy Predictions.

TLDR
This project proposes to use quantum-chemically derived molecular topological features, namely, localized orbital locator and electron localization function, as molecular descriptors, which provide a relatively denser input representation in a 3D space and demonstrates the efficacy of the proposed model by applying it to the task of predicting atomization energies for the QM9-G4MP2 data set.

Fast and accurate quantum mechanical modeling of large molecular systems using small basis set Hartree–Fock methods corrected with atom-centered potentials

There has been significant interest in developing fast and accurate quantum mechanical methods for modeling large molecular systems. In this work, by utilizing a machine-learning regression

Inclusion of More Physics Leads to Less Data: Learning the Interaction Energy as a Function of Electron Deformation Density with Limited Training Data.

TLDR
Under a low-data regime, EDDIE-ML outperforms other direct ML schemes and is the only model readily transferrable to larger, more complex systems including base pair trimers and porous cages.

References

SHOWING 1-10 OF 41 REFERENCES

The ANI-1ccx and ANI-1x data sets, coupled-cluster and density functional theory properties for molecules

TLDR
The ANI-1x and ANi-1ccx ML-based general-purpose potentials for organic molecules were developed through active learning; an automated data diversification process, and are provided to aid research and development of ML models for chemistry.

Machine learning accurate exchange and correlation functionals of the electronic density

TLDR
This work proposes a framework to create density functionals using supervised machine learning, termed NeuralXC, designed to lift the accuracy of baseline functionals towards that provided by more accurate methods while maintaining their efficiency.

New Basis Set Exchange: An Open, Up-to-Date Resource for the Molecular Sciences Community

TLDR
The Basis Set Exchange has been rewritten, utilizing modern software design and best practices, and the website updated to use the current generation of web development libraries.

Regression-clustering for Improved Accuracy and Training Cost with Molecular-Orbital-Based Machine Learning

TLDR
A clustering/regression/classification implementation of MOB-ML is introduced, which is found to recapitulate chemically intuitive groupings of the frontier molecular orbitals, and the combined RC/LR/RFC and RC/GPR/RFC implementations are found to provide good prediction accuracy with greatly reduced wall-clock training times.

Gaussian basis sets for use in correlated molecular calculations . Ill . The atoms aluminum through argon

Correlation consistent and augmented correlation consistent basis sets have been determined for the second row atoms aluminum through argon. The methodology, originally developed for the first row

Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning

TLDR
A general-purpose neural network potential is trained that approaches CCSD(T)/CBS accuracy on benchmarks for reaction thermochemistry, isomerization, and drug-like molecular torsions.

Gaussian Error Linear Units (GELUs)

TLDR
An empirical evaluation of the GELU nonlinearity against the ReLU and ELU activations is performed and performance improvements are found across all considered computer vision, natural language processing, and speech tasks.

On the Correlation Problem in Atomic and Molecular Systems. Calculation of Wavefunction Components in Ursell-Type Expansion Using Quantum-Field Theoretical Methods

A method is suggested for the calculation of the matrix elements of the logarithm of an operator which gives the exact wavefunction when operating on the wavefunction in the one‐electron

Note on an Approximation Treatment for Many-Electron Systems

A perturbation theory is developed for treating a system of n electrons in which the Hartree-Fock solution appears as the zero-order approximation. It is shown by this development that the first