arXiv:2605.20581v1 Announce Type: new Abstract: Machine learning interatomic potentials (MLIPs) achieve excellent accuracy when trained on large Density Functional Theory (DFT) data. To be useful in practice, they must often be adapted to target chemistries using small and expensive task-specific datasets. However, MLIPs transfer inconsistently across domains, with representations that often loose accessible composition and structure information. To address this, we present TriForces, a model-agnostic three-stream framework that separates composition and structure information, combined with se
The continuous drive to improve the efficiency and applicability of machine learning in scientific discovery creates immediate opportunities for specialized models like TriForces.
Improving the transferability of atomistic graph neural networks (GNNs) can significantly accelerate materials science research, leading to faster discovery of new chemicals and materials.
Machine learning interatomic potentials (MLIPs) will become more broadly applicable across different material chemistries, reducing the need for extensive, costly retraining on small datasets.
- · Materials scientists
- · Chemical R&D
- · AI/ML research labs
- · Pharmaceuticals
- · Traditional high-throughput screening methods
- · Companies reliant on bespoke DFT calculations
TriForces directly enhances the generalizability of AI models in material science.
This improved generalizability will lower the cost and time barrier for exploring novel material compositions and properties.
Accelerated material discovery could lead to breakthroughs in various industries, from energy storage to drug development.
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Read at arXiv cs.LG