| Predicting density functional theory-quality nuclear magnetic resonance chemical shifts via δ-machine learning PA Unzueta, CS Greenwell, GJO Beran Journal of Chemical Theory and Computation 17 (2), 826-840, 2021 | 46 | 2021 |
| Improving the accuracy of solid-state nuclear magnetic resonance chemical shift prediction with a simple molecular correction M Dračínský, P Unzueta, GJO Beran Physical Chemistry Chemical Physics 21 (27), 14992-15000, 2019 | 46 | 2019 |
| Polarizable continuum models provide an effective electrostatic embedding model for fragment‐based chemical shift prediction in challenging systems PA Unzueta, GJO Beran Journal of Computational Chemistry 41 (26), 2251-2265, 2020 | 13 | 2020 |
| Prediction of Photodynamics of 200 nm Excited Cyclobutanone with Linear Response Electronic Structure and Ab Initio Multiple Spawning D Hait, D Lahana, OJ Fajen, ASP Paz, PA Unzueta, B Rana, L Lu, Y Wang, ... arXiv preprint arXiv:2402.10710, 2024 | | 2024 |
| Single-Point Extrapolation to the Complete Basis Set Limit through Deep Learning S Holm, PA Unzueta, K Thompson, TJ Martínez Journal of Chemical Theory and Computation 19 (14), 4474-4483, 2023 | | 2023 |
| Low-Cost Strategies for Predicting Accurate Density Functional Theory-Based Nuclear Magnetic Resonance Chemical Shifts PA Unzueta University of California, Riverside, 2022 | | 2022 |
Gregory BeranDepartment of Chemistry, University of California at RiversideVerified email at ucr.edu
