arXiv:2605.24073v1 Announce Type: cross Abstract: Machine learning interatomic potentials (MLIPs) require generating computationally expensive, large-scale training datasets to accurately simulate materials and molecules. Incorporating electronic structure information using multitask learning improves sample efficiency, however, training on full Hamiltonian matrices, which scale quadratically with the number of atoms, is intractable for large datasets. In this work, we show that multitask learning utilizing orbitally resolved semiempirical charges significantly improves sample efficiency and a
The continuous drive to reduce the computational cost and data requirements for training advanced AI models in scientific domains makes sample-efficient methods highly relevant.
Improving sample efficiency in machine learning interatomic potentials (MLIPs) accelerates material science and molecular dynamics simulations, impacting drug discovery, battery design, and industrial chemistry.
The ability to develop accurate MLIPs with significantly less training data means faster discovery cycles and lower computational resource demands for material scientists and chemists.
- · Material Science Researchers
- · Pharmaceutical Companies
- · Chemical Industry
- · AI/ML Platform Providers
- · Traditional high-cost simulation methods
- · Organizations without access to advanced ML tools
Accelerated discovery and optimization of new materials and molecules.
Reduced R&D timelines and costs in industries reliant on material science and chemistry.
The development of novel materials with unprecedented properties, driving new technological paradigms across various sectors.
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