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Found 10 papers, 6 papers with code
Generative Hierarchical Materials Search
We confirm that GenMS is able to generate common crystal structures such as double perovskites, or spinels, solely from natural language input, and hence can form the foundation for more complex structure generation in near future.
Predicting emergence of crystals from amorphous matter with deep learning
Crystallization of the amorphous phases into metastable crystals plays a fundamental role in the formation of new matter, from geological to biological processes in nature to synthesis and development of new materials in the laboratory.
Accurate Surface and Finite Temperature Bulk Properties of Lithium Metal at Large Scales using Machine Learning Interaction Potentials
The properties of lithium metal are key parameters in the design of lithium ion and lithium metal batteries.
Scaling the leading accuracy of deep equivariant models to biomolecular simulations of realistic size
This work brings the leading accuracy, sample efficiency, and robustness of deep equivariant neural networks to the extreme computational scale.
Fast Uncertainty Estimates in Deep Learning Interatomic Potentials
This incurs a large computational overhead in both training and prediction that often results in order-of-magnitude more expensive predictions.
The Design Space of E(3)-Equivariant Atom-Centered Interatomic Potentials
The rapid progress of machine learning interatomic potentials over the past couple of years produced a number of new architectures.
Learning Local Equivariant Representations for Large-Scale Atomistic Dynamics
This work introduces Allegro, a strictly local equivariant deep learning interatomic potential that simultaneously exhibits excellent accuracy and scalability of parallel computation.
E(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentials
This work presents Neural Equivariant Interatomic Potentials (NequIP), an E(3)-equivariant neural network approach for learning interatomic potentials from ab-initio calculations for molecular dynamics simulations.
Multitask machine learning of collective variables for enhanced sampling of rare events
Computing accurate reaction rates is a central challenge in computational chemistry and biology because of the high cost of free energy estimation with unbiased molecular dynamics.
On-the-Fly Bayesian Active Learning of Interpretable Force-Fields for Atomistic Rare Events
Machine learning based interatomic potentials currently require manual construction of training sets consisting of thousands of first principles calculations and are often restricted to single-component and nonreactive systems.
Computational Physics Materials Science
