2 papers
cs.LG2025
Stochastic Taylor Derivative Estimator: Efficient amortization for arbitrary differential operators
Zekun Shi, Zheyuan Hu, Min Lin +1
Optimizing neural networks with loss that contain high-dimensional and high-order differential operators is expensive to evaluate with back-propagation due to …
physics.chem-ph2024
Diagonalization without Diagonalization: A Direct Optimization Approach for Solid-State Density Functional Theory
Tianbo Li, Min Lin, Stephen Dale +4
We present a novel approach to address the challenges of variable occupation numbers in direct optimization of density functional theory (DFT). By parameterizing both the eigenfunc…