Energy Derivatives in Real-Space Diffusion Monte Carlo

Jesse Van Rhijn*, Claudia Filippi, Stefania De Palo, Saverio Moroni

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

6 Citations (Scopus)
4 Downloads (Pure)


We present unbiased, finite-variance estimators of energy derivatives for real-space diffusion Monte Carlo calculations within the fixed-node approximation. The derivative dλE is fully consistent with the dependence E(λ) of the energy computed with the same time step. We address the issue of the divergent variance of derivatives related to variations of the nodes of the wave function both by using a regularization for wave function parameter gradients recently proposed in variational Monte Carlo and by introducing a regularization based on a coordinate transformation. The essence of the divergent variance problem is distilled into a particle-in-a-box toy model, where we demonstrate the algorithm.

Original languageEnglish
Pages (from-to)118-123
Number of pages6
JournalJournal of chemical theory and computation
Issue number1
Early online date20 Dec 2021
Publication statusPublished - 11 Jan 2022


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