| Accelerating phase-field-based microstructure evolution predictions via surrogate models trained by machine learning methods D Montes de Oca Zapiain, JA Stewart, R Dingreville npj Computational Materials 7 (1), 3, 2021 | 166 | 2021 |
| FitSNAP: Atomistic machine learning with LAMMPS A Rohskopf, C Sievers, N Lubbers, MA Cusentino, J Goff, J Janssen, ... Journal of Open Source Software 8 (84), 5118, 2023 | 36 | 2023 |
| Training data selection for accuracy and transferability of interatomic potentials D Montes de Oca Zapiain, MA Wood, N Lubbers, CZ Pereyra, ... npj Computational Materials 8 (1), 189, 2022 | 33 | 2022 |
| Microscopic and macroscopic characterization of grain boundary energy and strength in silicon carbide via machine-learning techniques M Guziewski, D Montes de Oca Zapiain, R Dingreville, SP Coleman ACS Applied Materials & Interfaces 13 (2), 3311-3324, 2021 | 28 | 2021 |
| Predicting plastic anisotropy using crystal plasticity and Bayesian neural network surrogate models DM de Oca Zapiain, H Lim, T Park, F Pourboghrat Materials Science and Engineering: A 833, 142472, 2022 | 23 | 2022 |
| Prediction of microscale plastic strain rate fields in two-phase composites subjected to an arbitrary macroscale strain rate using the materials knowledge system framework DM de Oca Zapiain, E Popova, SR Kalidindi Acta Materialia 141, 230-240, 2017 | 21 | 2017 |
| Reduced-order microstructure-sensitive models for damage initiation in two-phase composites D Montes de Oca Zapiain, E Popova, F Abdeljawad, JW Foulk, ... Integrating Materials and Manufacturing Innovation 7, 97-115, 2018 | 19 | 2018 |
| Characterizing the tensile strength of metastable grain boundaries in silicon carbide using machine learning D Montes de Oca Zapiain, M Guziewski, SP Coleman, R Dingreville The Journal of Physical Chemistry C 124 (45), 24809-24821, 2020 | 18 | 2020 |
| Localization models for the plastic response of polycrystalline materials using the material knowledge systems framework DM de Oca Zapiain, SR Kalidindi Modelling and Simulation in Materials Science and Engineering 27 (7), 074008, 2019 | 18 | 2019 |
| Texture-sensitive prediction of micro-spring performance using Gaussian process models calibrated to finite element simulations A Venkatraman, DM de Oca Zapiain, H Lim, SR Kalidindi Materials & Design 197, 109198, 2021 | 12 | 2021 |
| Convolutional neural networks for the localization of plastic velocity gradient tensor in polycrystalline microstructures D Montes de Oca Zapiain, A Shanker, SR Kalidindi Journal of Engineering Materials and Technology 144 (1), 011004, 2022 | 8 | 2022 |
| Establishing a data-driven strength model for β-tin by performing symbolic regression using genetic programming DM de Oca Zapiain, JMD Lane, JD Carroll, Z Casias, CC Battaile, ... Computational Materials Science 218, 111967, 2023 | 7 | 2023 |
| Training data selection for accuracy and transferability of interatomic potentials. npj Comput D Montes de Oca Zapiain, MA Wood, N Lubbers, CZ Pereyra, ... Mater 8 (1), 189, 2022 | 7 | 2022 |
| Reduced-order models for ranking damage initiation in dual-phase composites using Bayesian neural networks A Venkatraman, D Montes de Oca Zapiain, SR Kalidindi JOM 72, 4359-4369, 2020 | 6 | 2020 |
| Accelerating fem-based corrosion predictions using machine learning DM de Oca Zapiain, D Maestas, M Roop, P Noel, M Melia, R Katona Journal of The Electrochemical Society 171 (1), 011504, 2024 | 5 | 2024 |
| Development of a deep learning model for capturing plastic anisotropy–texture linkage T Park, D Montes de Oca Zapiain, F Pourboghrat, H Lim JOM 75 (12), 5466-5478, 2023 | 4 | 2023 |
| Solidification and crystallographic texture modeling of laser powder bed fusion Ti-6Al-4V using finite difference-monte carlo method BC Whitney, TM Rodgers, AG Spangenberger, AA Rezwan, ... Materialia 38, 102279, 2024 | 3 | 2024 |
| Neural network ensembles and uncertainty estimation for predictions of inelastic mechanical deformation using a finite element method-neural network approach GL Bergel, DM de Oca Zapiain, V Romero Data-Centric Engineering 4, e23, 2023 | 3 | 2023 |
| An active learning framework for the rapid assessment of galvanic corrosion A Venkatraman, RM Katona, D Maestas, M Roop, P Noell, ... npj Materials Degradation 8 (1), 54, 2024 | 2 | 2024 |
| Investigating the orientation dependence of local fields around spherical defects using crystal plasticity simulations NK Aragon, AA Rezwan, DM de Oca Zapiain, H Lim Journal of Materials Research and Technology 33, 235-243, 2024 | 1 | 2024 |
Hojun LimSandia National LaboratoriesVerified email at sandia.gov
