You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
PauliNet builds upon HF or CASSCF orbitals as a physically meaningful baseline and takes a neural network approach to the SJB wavefunction in order tocorrect this baseline towards a high-accuracy solution.
JAX-DFT implements one-dimensional density functional theory (DFT) in JAX. It uses powerful JAX primitives to enable JIT compilation, automatical differentiation, and high-performance computation on GPUs.
ML-DFT: Machine learning for density functional approximations This repository contains the implementation for the kernel ridge regression based density functional approximation method described in the paper "Quantum chemical accuracy from density functional approximations via machine learning".
this work proposed a deep-learning approach to KS-DFT. First, in contrast to the conventional SCF loop, directly minimizing the total energy by reparameterizing the orthogonal constraint as a feed-forward computation. They prove that such an approach has the same expressivity as the SCF method yet reduces the computational complexity from O(N^4) to O(N^3)
A molecular orbital based machine learning model for predicting accurate CCSD(T) correlation energies. The model, named as PairNet, shows excellent transferability on several public data sets using features inspired by pair natural orbitals(PNOs).
GradDFT is a JAX-based library enabling the differentiable design and experimentation of exchange-correlation functionals using machine learning techniques.
A JAX implementation of the algorithm and calculations described in Forward Laplacian: A New Computational Framework for Neural Network-based Variational Monte Carlo.
92
+
75
93
-[M-OFDFT](https://zenodo.org/records/10616893)
76
94
M-OFDFT is a deep-learning implementation of orbital-free density functional theory that achieves DFT-level accuracy on molecular systems but with lower cost complexity, and can extrapolate to much larger molecules than those seen during training
<br>we present a parameterized representation for learning the mapping from a molecular configuration to its corresponding density matrix using the Atomic Cluster Expansion (ACE) framework, which preserves the physical symmetries of the mapping, including isometric equivariance and Grassmannianity.
79
98
80
99
<!-- markdown-link-check-disable-next-line -->
81
100
-[ANN for Schrodinger](https://doi.org/10.26434/chemrxiv-2024-2qw5x)
82
101
Artificial neural networks (NN) are universal function approximators and have shown great ability in computing the ground state energy of the electronic Schrödinger equation, yet NN has not established itself as a practical and accurate approach to solving the vibrational Schrödinger equation for realistic polyatomic molecules to obtain vibrational energies and wave functions for the excited states
The accuracy of density functional theory hinges on the approximation of nonlocal contributions to the exchange-correlation (XC) functional. To date, machine-learned and human-designed approximations suffer from insufficient accuracy, limited scalability, or dependence on costly reference data. To address these issues, we introduce Equivariant Graph Exchange Correlation (EG-XC), a novel non-local XC functional based on equivariant graph neural network
Machine learning methods are promising in significantly accelerating charge density prediction, yet existing approaches either lack accuracy or scalability. They propose a recipe that can achieve both. In particular, they identify three key ingredients: (1) representing the charge density with atomic and virtual orbitals (spherical fields centered at atom/virtual coordinates); (2) using expressive and learnable orbital basis sets (basis function for the spherical fields); and (3) using high-capacity equivariant neural network architecture
We have developed an approach for physics-informed training of flexible empirical density functionals. In this approach, the “physics knowledge” is transferred from PBE, or any other exact-constraints-based functional, using local exchange−correlation energy density regularization, i.e., by adding its local energies into the training set
SchrödingerNet offers a novel approach to solving the full electronic-nuclear Schrödinger equation (SE) by defining a custom loss function designed to equalize local energies throughout the system.
This package provides a python realization of the multi-task EGNN (equivariant graph neural network) for molecular electronic structure described in the paper "Multi-task learning for molecular electronic structure approaching coupled-cluster accuracy".
We propose adaptive hybrid functionals, generating optimal exact exchange admixture ratios on the fly using data- efficient quantum machine learning models with negligible overhead. The adaptive Perdew-Burke-Ernzerhof hybrid density functional (aPBE0) improves energetics, electron densities, and HOMO- LUMO gaps in QM9, QM7b, and GMTKN55 benchmark datasets.
0 commit comments