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Found 8 papers, 6 papers with code
Natural Quantum Monte Carlo Computation of Excited States
We present a variational Monte Carlo algorithm for estimating the lowest excited states of a quantum system which is a natural generalization of the estimation of ground states.
Learned Force Fields Are Ready For Ground State Catalyst Discovery
We present evidence that learned density functional theory (``DFT'') force fields are ready for ground state catalyst discovery.
Thermal half-lives of azobenzene derivatives: virtual screening based on intersystem crossing using a machine learning potential
We use our approach to predict the thermal half-lives of 19, 000 azobenzene derivatives.
Excited state, non-adiabatic dynamics of large photoswitchable molecules using a chemically transferable machine learning potential
For example, photoisomerization can allow a drug with a photo-switchable scaffold such as azobenzene to be activated with light.
Molecular machine learning with conformer ensembles
Here we investigate how the 3D information of multiple conformers, traditionally known as 4D information in the cheminformatics community, can improve molecular property prediction in deep learning models.
Temperature-transferable coarse-graining of ionic liquids with dual graph convolutional neural networks
The potential of mean force is expressed as two jointly-trained neural network interatomic potentials that learn the coupled short-range and the many-body long range molecular interactions.
Computational Physics Materials Science
GEOM: Energy-annotated molecular conformations for property prediction and molecular generation
The Geometric Ensemble Of Molecules (GEOM) dataset contains conformers for 133, 000 species from QM9, and 317, 000 species with experimental data related to biophysics, physiology, and physical chemistry.
Differentiable Molecular Simulations for Control and Learning
From the perspective of engineering, one wishes to control the Hamiltonian to achieve desired simulation outcomes and structures, as in self-assembly and optical control, to then realize systems with the desired Hamiltonian in the lab.
