Towards modeling molecular crystals with the accuracy of diffusion Quantum Monte Carlo

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• Flaviano Della Pia, University of Cambridge
• Wednesday 21 May 2025, 15:00-15:30
• Unilever Lecture Theatre, Yusuf Hamied Department of Chemistry.

If you have a question about this talk, please contact Lisa Masters.

Computational modelling plays a central role in molecular crystal discovery, fundamental to a wide range of applications, including pharmaceuticals and renewable energy. However, a reliable description of these systems requires both a high-accuracy description of the potential energy surface and a fully anharmonic quantum description of the nuclear motion. [1,2] In this talk, I will first show that reliable lattice energies can be obtained with quantum diffusion Monte Carlo. [3,4] Then, I will demonstrate the generation of machine learning interatomic potentials capable of describing molecular crystals at finite temperature and pressure with sub-chemical accuracy, using as few as ∼ 200 data structures, an order of magnitude improvement over the current state-of-the-art. [5] Our models successfully reproduce experiments for a diverse range of molecular crystals and open up the prospects of reliable modeling for drug discovery and beyond.

[1] G. J. O. Beran, Chem. Rev. 2016, 116, 9, 5567–5613 (2016) [2] V. Kapil and E. A. Engel, Proc. Natl. Acad. Sci. U.S.A. 119 (6) e2111769119 (2022) [3] F. Della Pia, A. Zen, D. Alfè, A. Michaelides, J. Chem. Phys. 157, 134701 (2022) [4] F. Della Pia, A. Zen, D. Alfè, A. Michaelides, Phys. Rev. Lett. 133, 046401 (2024) [5] F. Della Pia, B. X. Shi, V. Kapil, A. Zen, D. Alfè, A. Michaelides, arXiv:2502.15530 (2025)

This talk is part of the Theory - Chemistry Research Interest Group series.

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