arXiv:2607.05559v1 Announce Type: new Abstract: Foundation machine learning force fields (MLFFs) such as MACE-MP-0 and UMA cover broad chemical space at near density functional theory (DFT) accuracy. However, they assume equilibrium ground-state physics and do not natively handle externally induced changes to the electronic state, such as charging, applied fields, or electronic excitation, which limits their use for driven processes such as photoexcitation and charge injection. We propose EquiFiLM, a lightweight extension that adds continuous external conditioning to any equivariant foundation
The continuous development of foundation machine learning force fields necessitates extensions to address complex, non-equilibrium material behaviors, driven by an increasing need for more accurate simulations in advanced materials science.
This breakthrough allows for more accurate and versatile simulations of materials under externally induced conditions like charging or excitation, crucial for designing next-generation materials and devices beyond equilibrium physics.
Machine learning force fields can now model dynamic electronic state changes, expanding their applicability from equilibrium ground states to driven processes such as photoexcitation and charge injection, previously a limitation.
- · Materials scientists
- · Chemical engineers
- · Semiconductor industry
- · Drug discovery
- · Traditional, less adaptable simulation methods
- · Companies reliant solely on empirical material testing
More precise computational design of novel materials with specific electronic properties.
Accelerated development cycles for batteries, catalysts, and quantum computing components due to enhanced simulation capabilities.
The potential for 'in-silico' discovery of materials with entirely new functionalities, reshaping industrial R&D paradigms.
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Read at arXiv cs.LG