SOURCE-LINKED INTELLIGENCE

Learning Orbital-Free Density Functional Theory

CORDIS · observation · Publication date unknown

d interatomic potentials are fast, but until now remain unreliable for more complex chemistries. LearningOFDFT aims to close this gap by learning the kinetic energy functional. I will use geometric machine learning to develop an orbital-free spin density energy functional that obeys select exact constraints and that affords fully variational density optimization across charge, spin, and geometry. Steps include leveraging and expanding the OMol25 corpus, adding non-covalent interactions and exotic species to ensure coverage of charged, open-shell, and distorted systems; developing surrogate functionals that relax exactness away from the ground state to accelerate convergence and minimize error only where it matters most; training inference dynamics, by learning vector fields that guide densities toward the ground state and sampling these stochastically to obtain fast uncertainty estimates; hardening and releasing open-source code for immediate community use and further development. If successful, the learned functional will prove an inspiration and a challenge for theoretical chemi

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recordType

award

status

SIGNED

region

EU

value

2498767

unit

EUR