Articlenpj computational materials2026
Efficient and accurate spatial mixing of machine learned interatomic potentials for materials science.
Article in npj computational materials, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Surface Neuronal-Like Structuring of High-Strength CFRP for Enhanced Electromagnetic Interference Shielding.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Efficient and accurate spatial mixing of machine learned interatomic potentials for materials science.npj computational materials · 2026Article
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Authors and funding
4 authors.
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Abstract
Machine-learned interatomic potentials can offer near first-principles accuracy but are computationally expensive, limiting their application to large-scale molecular dynamics simulations. Inspired by quantum mechanics/molecular mechanics methods, we present ML-MIX, a CPU- and GPU-compatible LAMMPS package to accelerate simulations by spatially mixing interatomic potentials of different complexities, allowing deployment of modern MLIPs even under restricted computational budgets. We demonstrate our method for ACE, UF3, SNAP and MACE potential architectures and demonstrate how linear 'cheap' potentials can be distilled from a given 'expensive' potential, allowing close matching in relevant regions of configuration space. The functionality of ML-MIX is demonstrated through tests on point defects in Si, Fe and W-He, in which speedups of up to 11× over ~8000 atoms are demonstrated, without sacrificing accuracy. The scientific potential of ML-MIX is demonstrated via two case studies in W, measuring the mobility of
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