Machine Learning Interatomic Potentials
PulseAugur coverage of Machine Learning Interatomic Potentials — every cluster mentioning Machine Learning Interatomic Potentials across labs, papers, and developer communities, ranked by signal.
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New benchmark reveals MLIPs struggle with unseen molecular compositions
A new benchmark has been developed to evaluate the compositional generalization capabilities of machine learning interatomic potentials (MLIPs). These potentials are crucial for various scientific applications, includin…
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New AI methods enhance materials science simulations with improved force fields
Researchers are developing new methods to improve the accuracy and transferability of machine learning force fields used in materials science simulations. One approach focuses on analyzing errors in multicomponent envir…
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New framework improves MLIPs for electrostatic interactions
Researchers have developed a new framework for machine learning interatomic potentials (MLIPs) that better accounts for electrostatic effects. This framework views existing models as coarse-grained approximations of den…
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New fine-tuning method enhances materials foundation models with sparsity
Researchers have developed a novel fine-tuning method for E(3)-equivariant materials foundation models, which are used to approximate potential energy surfaces. This sparsity-promoting technique selectively updates a sm…
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New MLIP methods improve accuracy and automate research
Researchers are developing advanced machine learning interatomic potentials (MLIPs) to improve atomistic simulations. New methods like Stein Kernelized Molecular Dynamics (SKMD) enhance data acquisition for active learn…
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Mixture of Experts framework speeds up atomistic simulations
Researchers have developed a new Mixture-of-Experts (MoE) framework for Machine Learning Interatomic Potentials (MLIPs) to accelerate atomistic simulations. This approach divides simulation domains into regions of varyi…