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Found 6 papers, 4 papers with code
MatterGen: a generative model for inorganic materials design
We further introduce adapter modules to enable fine-tuning towards any given property constraints with a labeled dataset.
Does AI for science need another ImageNet Or totally different benchmarks? A case study of machine learning force fields
This suite of metrics has demonstrated a better ability to assess a model's performance in real-world scientific applications, in contrast to traditional AI benchmarking methodologies.
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.
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.
