arXiv:2606.04279v1 Announce Type: new Abstract: Machine-learned (ML) exchange-correlation (XC) functionals aim to replace human-designed density functional approximations by learning directly from reference data, but they still do not consistently outperform traditional $\mathcal{O}(N^4)$-scaling hybrid functionals. We study a hybrid-distillation setting in which $\mathcal{O}(N^3)$-scaling ML-XC functionals are trained to reproduce B3LYP/def2-SVP targets. We introduce Derivative Informed XC-Loss (DI-Loss), a loss that incorporates additional information from the reference hybrid functional by
The continuous drive to enhance the efficiency and accuracy of computational chemistry methods, particularly in fields like materials science and drug discovery, propels research into advanced AI-driven solutions.
Improving the accuracy and reducing the computational cost of exchange-correlation functionals is crucial for accelerating scientific discovery in chemistry, materials science, and pharmaceuticals.
Machine-learned exchange-correlation functionals could consistently outperform traditional methods, potentially lowering computational barriers for complex simulations.
- · Computational Chemists
- · Materials Science Researchers
- · Pharmaceutical Industry
- · AI/ML Research in Science
- · Developers of less accurate, higher-cost traditional density functional theory m
More accurate and faster computational simulations become accessible to a broader range of researchers and industries.
Accelerated discovery of new materials, catalysts, and drug candidates due to enhanced simulation capabilities.
Potentially democratizes advanced quantum chemistry computations, fostering innovation in areas previously limited by computational resources.
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