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Gregory J. O. Beran

Publications and source records attributed to Gregory J. O. Beran.

2 recordsLinked to original sources
arXiv2026

Machine-Learned NMR Shieldings in Molecular Solids with Built-In Hybrid-Functional Molecular Corrections

Fast and accurate chemical shielding estimators are essential for shielding-driven Nuclear Magnetic Resonance (NMR) crystallography. Machine-learning models for shielding predictions have matured significantly and today are primarily limited by the electronic structure reference data they are trained on. Here, we introduce ShiftML4, a shielding-tensor model trained directly on monomer-corrected calculations that approximate PBE0, rather than the PBE reference targeted by earlier ShiftML models. ShiftML4 is trained on a diverse set of structures containing 12 of the most common NMR nuclei in molecular organic solids. On experimental benchmark sets, the 13C isotropic RMSE against experiment is 1.67 ppm, compared with 2.34 ppm for GIPAW-PBE on the same geometries. ShiftML4 gives a similar 1H prediction RMSE to ShiftML3 (0.5 ppm) and improves the 15N RMSE from 7.24 to 6.08 ppm. The model also reduces errors in the shielding-tensor anisotropy, with an RMSE of 4.63 ppm on 13C CSA principal components against 5.85 ppm for GIPAW. The improvements in prediction accuracy are retained on better geometries. Basing shift predictions on structures relaxed with PET-MOLS, a recent machine-learned interatomic potential that reaches approximate hybrid-DFT geometries in seconds, lowers the ShiftML4 errors further to 0.48 ppm (1H), 1.49 ppm (13C) and 3.66 ppm (15N).

physics.chem-ph↗
arXiv2026

FastCSP: Accelerated Molecular Crystal Structure Prediction with Universal Model for Atoms

Molecular crystal structure prediction (CSP) is essential for applications in pharmaceuticals and organic electronics. However, CSP remains challenging and computationally intensive due to the need to explore a large search space with sub-kJ/mol accuracy to distinguish between competing polymorphs. While dispersion-inclusive density functional theory (DFT) offers the necessary precision, its computational cost is impractical for a large number of putative structures. Here, we present FastCSP, an open-source, end-to-end CSP workflow driven entirely by a single pretrained universal machine learning interatomic potential (MLIP), the Universal Model for Atoms (UMA), without any system-specific fine-tuning or DFT calculations. FastCSP integrates conformer generation, random structure generation via Genarris 3, geometry optimization, free energy evaluation, and conformer energy corrections, all powered by UMA. Benchmarked on 28 semi-rigid and 10 flexible molecules spanning 74 experimental polymorphs, FastCSP reliably recovers all known structures, ranking them within 9 kJ/mol of the global minimum. UMA reproduces dispersion-inclusive DFT results with high fidelity across chemically diverse compounds. Conformer corrections are particularly beneficial for flexible compounds with conformational polymorphism, such as ROY. UMA's accuracy, transferability, and computational cost thus eliminate the need for classical force fields in early-stage screening and DFT-based re-ranking in CSP workflows. The open-source release of the entire FastCSP workflow lowers the barrier to accessing CSP, enabling both pharmaceutical-grade and high-throughput polymorph screening within practical computational reach.

physics.chem-ph↗