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Found 15 papers, 8 papers with code
Phase discovery with active learning: Application to structural phase transitions in equiatomic NiTi
Here, we train four machine-learned force fields for equiatomic NiTi based on the LDA, PBE, PBEsol, and SCAN DFT functionals.
Learning Interatomic Potentials at Multiple Scales
When running MD, the MTS integrator then evaluates the smaller model for every time step and the larger model less frequently, accelerating simulation.
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.
Anomalous thermoelectric transport phenomena from interband electron-phonon scattering
The Seebeck coefficient and electrical conductivity are two critical quantities to optimize simultaneously in designing thermoelectric materials, and they are determined by the dynamics of carrier scattering.
Materials Science
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.
Bayesian Force Fields from Active Learning for Simulation of Inter-Dimensional Transformation of Stanene
We present a way to dramatically accelerate Gaussian process models for interatomic force fields based on many-body kernels by mapping both forces and uncertainties onto functions of low-dimensional features.
Charge density and redox potential of LiNiO2 using ab initio diffusion quantum Monte Carlo
Electronic structure of layered LiNiO2 has been controversial despite numerous theoretical and experimental reports regarding its nature.
Materials Science
Fast Neural Network Approach for Direct Covariant Forces Prediction in Complex Multi-Element Extended Systems
Neural network force field (NNFF) is a method for performing regression on atomic structure-force relationships, bypassing expensive quantum mechanics calculation which prevents the execution of long ab-initio quality molecular dynamics simulations.
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
Accelerated screening of thermoelectric materials by first-principles computations of electron-phonon scattering
Recent discovery of new materials for thermoelectric energy conversion is enabled by efficient prediction of materials' performance from first-principles, without empirically fitted parameters.
Materials Science
