Kavli Affiliate: Chiara Daraio
| First 5 Authors: Vahe Gharakhanyan, Vahe Gharakhanyan, , ,
| Summary:
Crystal Structure Prediction (CSP) of molecular crystals plays a central role
in applications, such as pharmaceuticals and organic electronics. CSP is
challenging and computationally expensive due to the need to explore a large
search space with sufficient accuracy to capture energy differences of a few
kJ/mol between polymorphs. Dispersion-inclusive density functional theory (DFT)
provides the required accuracy but its computational cost is impractical for a
large number of putative structures. We introduce FastCSP, an open-source,
high-throughput CSP workflow based on machine learning interatomic potentials
(MLIPs). FastCSP combines random structure generation using Genarris 3.0 with
geometry relaxation and free energy calculations powered entirely by the
Universal Model for Atoms (UMA) MLIP. We benchmark FastCSP on a curated set of
28 mostly rigid molecules, demonstrating that our workflow consistently
generates known experimental structures and ranks them within 5 kJ/mol per
molecule of the global minimum. Our results demonstrate that universal MLIPs
can be used across diverse compounds without requiring system-specific tuning.
Moreover, the speed and accuracy afforded by UMA eliminate the need for
classical force fields in the early stages of CSP and for final re-ranking with
DFT. The open-source release of the entire FastCSP workflow significantly
lowers the barrier to accessing CSP. CSP results for a single system can be
obtained within hours on tens of modern GPUs, making high-throughput crystal
structure prediction feasible for a broad range of scientific applications.
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