Machine learning models, especially certain deep neural networks, can predict lattice thermal conductivity with useful accuracy across different generalization tests while being orders of magnitude faster than first-principles calculations.
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Orb: A fast, scalable neural network potential.arXiv preprint arXiv:2410.22570(2024)
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A new benchmark finds that state-of-the-art ML interatomic potentials struggle with compositional generalization, producing errors an order of magnitude higher on unseen molecular combinations than on training-like cases.
CrystalREPA closes the representation gap between crystal generators and universal MLIPs via contrastive alignment, yielding more stable and valid generated crystals while revealing that MLIP teacher quality is better predicted by representation distinguishability than by leaderboard accuracy.
MACE-MP-0 is a general-purpose atomistic ML force field trained on public data that enables stable simulations of diverse chemical systems with qualitative and sometimes quantitative accuracy, serving as a starting point for fine-tuning.
The EDDP machine-learned potential for lead predicts the observed FCC-HCP phase transition at ~15 GPa, unlike EAM and MEAM models, when paired with nested sampling.
OptiMat Alloys is a conversational AI system that maintains a living FAIR database of multi-principal element alloy calculations and enables natural-language, on-demand computations with built-in uncertainty checks.
GRACE MLIPs train faster and predict alloy properties more accurately than NEP, but NEP's 60-fold speed advantage enables reliable million-atom simulations of shock propagation when paired with ensemble uncertainty quantification.
Experiments on QM9 and AFLOW datasets show that static and dynamic batching for GNNs can yield up to 2.7x training speedups depending on data, model, batch size, hardware, and training steps, with occasional differences in learning metrics.
citing papers explorer
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Fast and Accurate Prediction of Lattice Thermal Conductivity via Machine Learning Surrogates
Machine learning models, especially certain deep neural networks, can predict lattice thermal conductivity with useful accuracy across different generalization tests while being orders of magnitude faster than first-principles calculations.
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Benchmarking Compositional Generalisation for Machine Learning Interatomic Potentials
A new benchmark finds that state-of-the-art ML interatomic potentials struggle with compositional generalization, producing errors an order of magnitude higher on unseen molecular combinations than on training-like cases.
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CrystalREPA: Transferring Physical Priors from Universal MLIPs to Crystal Generative Models
CrystalREPA closes the representation gap between crystal generators and universal MLIPs via contrastive alignment, yielding more stable and valid generated crystals while revealing that MLIP teacher quality is better predicted by representation distinguishability than by leaderboard accuracy.
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A foundation model for atomistic materials chemistry
MACE-MP-0 is a general-purpose atomistic ML force field trained on public data that enables stable simulations of diverse chemical systems with qualitative and sometimes quantitative accuracy, serving as a starting point for fine-tuning.
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Benchmarking empirical and machine-learned interatomic potentials using phase diagram predictions for Lead
The EDDP machine-learned potential for lead predicts the observed FCC-HCP phase transition at ~15 GPa, unlike EAM and MEAM models, when paired with nested sampling.
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OptiMat Alloys: a FAIR, living database of multi-principal element alloys enabled by a conversational agent
OptiMat Alloys is a conversational AI system that maintains a living FAIR database of multi-principal element alloy calculations and enables natural-language, on-demand computations with built-in uncertainty checks.
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Machine Learning Interatomic Potentials for Million-Atom Simulations of Multicomponent Alloys
GRACE MLIPs train faster and predict alloy properties more accurately than NEP, but NEP's 60-fold speed advantage enables reliable million-atom simulations of shock propagation when paired with ensemble uncertainty quantification.
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Training speedups via batching for geometric learning: an analysis of static and dynamic algorithms
Experiments on QM9 and AFLOW datasets show that static and dynamic batching for GNNs can yield up to 2.7x training speedups depending on data, model, batch size, hardware, and training steps, with occasional differences in learning metrics.
- Uncertainty-aware Machine Learning Interatomic Potentials via Learned Functional Perturbations
- MatterSim-MT: A multi-task foundation model for in silico materials characterization