arXiv subjects

Jacob B. Holmes

Publications and source records attributed to Jacob B. Holmes.

3 recordsLinked to original sources

arXiv2026

Quantum-corrected NMR crystallography at scale

Structure determination by chemical-shift-driven NMR crystallography relies on comparing chemical shieldings measured in solid-state NMR experiments with simulations. However, computational cost limits the accuracy of shielding predictions, that usually rely on low-level electronic-structure approximations and neglect thermal and quantum mechanical nuclear motion, leading to large errors, especially for highly informative hydrogen-bonded protons. To address these limitations, we introduce a quantum-nuclei-corrected (QNC-NMR) approach. We generate inexpensively quantum ensembles using PET-MOLS, a novel machine-learning learning model of the interatomic potential transferable across molecular crystals. Using them as inputs to a chemical-shift model results in a 33 % improvement of the agreement with experiments for hydrogen-bonded protons, without the need for empirical corrections. The ability to sample structures consistent with the experimental conditions enables further refinement of the shielding model by finetuning it against measured shieldings. The favorable scaling with system size allows similar improvements for amorphous materials that are otherwise inaccessible to explicit DFT simulations.

physics.chem-ph↗

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↗

arXiv2025

A deep learning model for chemical shieldings in molecular organic solids including anisotropy

Nuclear Magnetic Resonance (NMR) chemical shifts are powerful probes of local atomic and electronic structure that can be used to resolve the structures of powdered or amorphous molecular solids. Chemical shift driven structure elucidation depends critically on accurate and fast predictions of chemical shieldings, and machine learning (ML) models for shielding predictions are increasingly used as scalable and efficient surrogates for demanding ab initio calculations. However, the prediction accuracies of current ML models still lag behind those of the DFT reference methods they approximate, especially for nuclei such as $^{13}$C and $^{15}$N. Here, we introduce ShiftML3.0, a deep-learning model that improves the accuracy of predictions of isotropic chemical shieldings in molecular solids, and does so while also predicting the full shielding tensor. On experimental benchmark sets, we find root-mean-squared errors with respect to experiment for ShiftML3.0 that approach those of DFT reference calculations, with RMSEs of 0.53 ppm for $^{1}$H, 2.4 ppm for $^{13}$C, and 7.2 ppm for $^{15}$N, compared to DFT values of 0.49 ppm, 2.3 ppm, and 5.8 ppm, respectively.

physics.chem-ph↗