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Thermal Conductivity Predictions with Foundation Atomistic Models
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Advances in machine learning have led to the development of foundation models for atomistic materials chemistry, enabling quantum-accurate descriptions of interatomic forces across chemically diverse compounds at reduced computational cost. Hitherto, the accuracy and utility of these models have been assessed relying on descriptors based on formation energies or idealized harmonic atomic vibrations. Yet, the rigorous and physically interpretable quantification of their capability to describe both realistic anharmonic atomic dynamics and technologically relevant observables remains a pressing problem. Here, we address this problem, leveraging the Wigner formulation of heat transport and the Gr\"uneisen approach to thermal expansion to connect the atomic-physics awareness of foundation models to their utility in predicting experimentally observable thermomechanical properties, presenting standards and fine-tuning protocols needed to achieve first-principles accuracy. We apply our framework to a database of 103 solids with diverse compositions and structures, demonstrating that it overcomes the major bottlenecks of current methods for designing heat-management materials -- high cost, limited transferability, or lack of physics awareness -- and its potential to discover materials for next-gen technologies ranging from thermal insulation to neuromorphic computing.
Forward citations
Cited by 8 Pith papers
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Pushing the limits of unconstrained machine-learned interatomic potentials
Unconstrained non-equivariant and direct-force neural interatomic potentials scale to 730M parameters and match or beat equivariant state-of-the-art models on several atomistic benchmarks.
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Dyna-Mat: End-to-end benchmarking of foundation machine learning interatomic potentials in finite-temperature ensembles
Dyna-Mat-v1.0 benchmarks 15 foundation MLIPs on finite-T MD observables, finding average force-error correlation with RDF/VDOS but systematic pressure failures and near-Pareto optimality of latest cross-trained models.
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Transformer Atomic Cluster Expansion: TRACE
A no-message-passing, attention-based local potential reproduces a perovskite phase transition, liquid-water O–O structure, and an organic rearrangement barrier with a single architecture.
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Performance of universal machine learning potentials in global optimization of inorganic crystal structures
Nine universal machine-learning potentials were run in unconstrained evolutionary searches for the ground states of twelve inorganic compounds; performance ranges from near-DFT accuracy (eSEN) to essentially non-predi...
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From Evaluation to Design: Using Potential Energy Surface Smoothness Metrics to Guide Machine Learning Interatomic Potential Architectures
A bond-deformation benchmark plus a force-smoothness metric is proposed to detect PES artifacts and guide MLIP architecture design, with improvements shown on a new Transformer-style model.
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Enhancing Materials Discovery with Valence Constrained Design in Generative Modeling
CrysVCD generates valence-balanced compositions with an elemental language model and then constructs their crystal structures with a diffusion model, reporting improved stability and functional property targeting.
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Distillation of atomistic foundation models across architectures and chemical domains
Distillation of atomistic foundation models via synthetic data yields 10x-100x faster student potentials with near-teacher accuracy.
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Fast and Accurate Foundation Models for Equivariant Machine-Learned Interatomic Potentials
Fast NequIP/Allegro foundation MLIPs reach leading MD inference speeds and strong benchmark accuracy; materials-discovery gains need better chemical diversity and consistent transition-metal energy surfaces.
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