Publications

Montes de Zapiain, D., Noell, P., Katona, R.M., Maestas, D., Roop, M., & Roop, M. (2024). An active learning framework for the rapid assessment of galvanic corrosion. npj Materials Degradation, 8(1). https://doi.org/10.1038/s41529-024-00476-4

Montes de Zapiain, D., Bachman, W.B., Moore, N.W., Rodgers, T., & Rodgers, T. (2024). Calibration of thermal spray microstructure simulations using Bayesian optimization. Computational Materials Science, 235(15). https://doi.org/10.1016/j.commatsci.2024.112845

Montes de Zapiain, D., Maestas, D., Roop, M., Noell, P., Melia, M.A., Katona, R.M., & Katona, R.M. (2024). Accelerating FEM-Based Corrosion Predictions Using Machine Learning. Journal of the Electrochemical Society, 171(1). https://doi.org/10.1149/1945-7111/ad1e3c

Bergel, G.L., Montes de Zapiain, D., Romero, V., & Romero, V. (2023). Neural network ensembles and uncertainty estimation for predictions of inelastic mechanical deformation using a finite element method-neural network approach. Data-Centric Engineering, 4(4). https://doi.org/10.1017/dce.2023.17

Montes de Zapiain, D., Bergel, G.L., Romero, V., & Romero, V. (2023). Estimating Uncertainty of Neural Network Predictions for Inelastic Mechanical Deformation using Coupled FEM-NN Approach [Conference Presenation]. https://doi.org/10.2172/2430680

Katona, R.M., Maestas, D., Roop, M., Schaller, R.F., Wilson, M.A., Montes de Zapiain, D., Melia, M.A., Noell, P., & Noell, P. (2023). Modeling the Stochastic Nature of Corrosion Reactions in Atmospheric Conditions [Conference Presenation]. https://doi.org/10.2172/2430651

Knudson, M.D., Ao, T., Rodriguez, M.A., Montes de Zapiain, D., Lane, J.M.D., Morgan, D., & Morgan, D. (2023). Transformation mechanisms for the pressure-induced phase transition in single crystal CdS [Conference Presenation]. https://doi.org/10.2172/2430963

Montes de Zapiain, D., Aragon, N.K., Lim, H., Carroll, J.D., Casias, Z., Fensin, S., Battaile, C., Lane, J.M.D., & Lane, J.M.D. (2023). Establishing a data-driven strength model for 𝜷-tin by performing symbolic regression using genetic programming [Conference Presenation]. https://doi.org/10.2172/2430874

Ao, T., Morgan, D.V., Lane, J.M.D., Brown, N.P., Donohoe, B., Knudson, M.D., Martinez, C., Montes de Zapiain, D., Rodriguez, M.A., Valdez, N.R., & Valdez, N.R. (2023). Dynamic x-ray diffraction of materials under ramp compression on the Thor pulsed-power generator [Conference Presenation]. https://doi.org/10.2172/2431265

Montes de Zapiain, D., Lim, H., Park, T., Pourboghrat, F., & Pourboghrat, F. (2023). Data-driven plastic anisotropy predictions using crystal plasticity and deep learning models [Conference Presenation]. https://doi.org/10.2172/2431603

Maestas, D., Wilson, M.A., Montes de Zapiain, D., Melia, M.A., Schaller, R.F., & Schaller, R.F. (2023). Understanding and Modeling the Stochastic Nature of Corrosion Reactions in Atmospheric Conditions [Conference Presenation]. https://doi.org/10.2172/2431444

Montes de Zapiain, D., Wood, M.A., Sema, D., Thompson, A.P., & Thompson, A.P. (2023). Optimal Development of Transferable Machine Learning Interatomic Potentials using Active Learning [Poster]. https://doi.org/10.2172/2431809

Lim, H., Montes de Zapiain, D., Park, T., Pourboghrat, F., & Pourboghrat, F. (2023). Data-driven plastic anisotropy predictions using crystal plasticity and deep learning models [Conference Presenation]. https://doi.org/10.2172/2431726

Bachman, W.B., Robbe, P., Montes de Zapiain, D., Wildey, T., Lim, H., & Lim, H. (2023). The multifaceted nature of uncertainty in structure-property linkage with crystal plasticity finite element model [Conference Presenation]. https://doi.org/10.2172/2431686

Montes de Zapiain, D., Bachman, W.B., Lim, H., & Lim, H. (2023). Development of structure-property linkages for damage in crystalline microstructures using Bayesian inference and unsupervised learning [Conference Presenation]. https://doi.org/10.2172/2431659

Bachman, W.B., Robbe, P., Wildey, T., Montes de Zapiain, D., Lim, H., & Lim, H. (2023). The multifaceted nature of uncertainty in structure-property linkage with crystal plasticity finite element model [Conference Proceeding]. AIAA SciTech Forum and Exposition, 2023. https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85200316050&origin=inward

Montes de Zapiain, D., Wood, M.A., Lubbers, N., Pereyra, C.Z., Thompson, A.P., Perez, D., & Perez, D. (2022). Training data selection for accuracy and transferability of interatomic potentials. npj Computational Materials, 8(1). https://doi.org/10.1038/s41524-022-00872-x

Bachman, W.B., Robbe, P., Wildey, T., Montes de Zapiain, D., Lim, H., & Lim, H. (2022). The multifaceted uncertainty nature of structure-property linkage with crystal plasticity finite element model [Conference Paper]. https://doi.org/10.2514/6.2023-0525

Lim, H., Park, T., Montes de Zapiain, D., Pourboghrat, F., & Pourboghrat, F. (2022). Investigating plastic anisotropy using crystal plasticity and deep learning models [Conference Presenation]. https://doi.org/10.2172/2006148

Ao, T., Donohoe, B., Martinez, C., Knudson, M.D., Montes de Zapiain, D., Morgan, D., Rodriguez, M.A., Lane, J.M.D., & Lane, J.M.D. (2022). LDRD 226360 Final Project Report: Simulated X-ray Diffraction and Machine Learning for Optimizing Dynamic Experiment Analysis. https://doi.org/10.2172/1891594

Montes de Zapiain, D., Bergel, G., Lim, H., Romero, V., & Romero, V. (2022). Estimating Uncertainty of Neural Network Predictions for Inelastic Mechanical Deformation using Coupled FEM-NN Approach [Conference Presenation]. https://doi.org/10.2172/2005250

Montes de Zapiain, D., Morgan, D., Thurston, B., Ao, T., Donohue, B., Martinez, C., Rodriguez, M.A., Knudson, M.D., Lane, J.M.D., & Lane, J.M.D. (2022). Simulated X-ray Diffraction and Machine Learning for Interpretation of Dynamic Compression Experiments [Conference Poster]. https://doi.org/10.2172/2003844

Knudson, M.D., Morgan, D.V., Ao, T., Rodriguez, M.A., Montes de Zapiain, D., Lane, J.M.D., & Lane, J.M.D. (2022). Transformation mechanisms for the pressure-induced phase transition in single crystal CdS [Conference Presenation]. https://doi.org/10.2172/2003988

Lim, H., Montes de Zapiain, D., Park, T., Pourboghrat, F., & Pourboghrat, F. (2022). Data-driven plastic anisotropy predictions using crystal plasticity and deep learning models [Conference Presenation]. https://doi.org/10.2172/2003833

Lim, H., Montes de Zapiain, D., Park, T., Pourboghrat, F., & Pourboghrat, F. (2022). Investigating plastic anisotropy using CP-FEM and ML techniques [Conference Presenation]. https://doi.org/10.2172/2002526

Lim, H., Montes de Zapiain, D., & Montes de Zapiain, D. (2022). MADDD NM Technology Showcase Presentation [Conference Presenation]. https://doi.org/10.2172/2001904

Lane, J.M.D., Thurston, B., Ao, T., Montes de Zapiain, D., Rodriguez, M.A., Knudson, M.D., & Knudson, M.D. (2022). Phase Transition Mechanisms in Cadmium Sulfide from X-ray Diffraction Comparisons of High-pressure Experiments and MD Simulation [Conference Presenation]. https://doi.org/10.2172/2002034

Hu, C., Montes de Zapiain, D., Martin, S., Stewart, J.A., Dingreville, R.P.M., & Dingreville, R.P.M. (2022). Accelerating Phase-field Predictions
Via Surrogate Models Trained By
Machine Learning Methods [Conference Presenation]. https://doi.org/10.2172/2001792

Rodgers, T., Montes de Zapiain, D., Bachman, W.B., Murray, S.E., Mahaffey, J.T., & Mahaffey, J.T. (2022). Mesoscale Simulation of Cold Spray Microstructure Formation [Conference Presenation]. https://doi.org/10.2172/2001897

Montes de Zapiain, D., Lim, H., Park, T., Pourboghrat, F., & Pourboghrat, F. (2022). Predicting plastic anisotropy using crystal plasticity and Bayesian neural network surrogate models [Conference Presenation]. Materials Science and Engineering: A. https://doi.org/10.2172/2002951

Littlewood, D.J., Wood, M.A., Montes de Zapiain, D., Rajamanickam, S., Trask, N.A., & Trask, N.A. (2021). Sandia / IBM Discussion on Machine Learning for Materials Applications [Slides]. https://doi.org/10.2172/1828106

Wood, M.A., Thompson, A.P., Cusentino, M.A., Montes de Zapiain, D., Oleynik, I., & Oleynik, I. (2021). Interatomic Potentials for Materials Science and Beyond; Advances in Machine Learned Spectral Neighborhood Analysis Potentials [Conference Presenation]. https://doi.org/10.2172/1883516

Montes de Zapiain, D. (2021). Establishing a data-driven deformation model for Tin using symbolic regression and genetic programming [Conference Presenation]. https://doi.org/10.2172/1878275

Montes de Zapiain, D. (2021). Advanced Manufacturing through Machine Learning [Presentation]. https://www.osti.gov/biblio/1873071

Lim, H., Montes de Zapiain, D., & Montes de Zapiain, D. (2021). Plastic anisotropy predictions using CP-FEM and ML methods [Presentation]. https://www.osti.gov/biblio/1865255

Montes de Zapiain, D. (2021). Agile Materials Science and Advanced Manufacturing through AI/ML [Presentation]. https://www.osti.gov/biblio/1865254

Montes de Zapiain, D. (2021). Calibration of Thermal Spray Microstructure Simulations to Experimental Data using Bayesian Optimization [Presentation]. https://www.osti.gov/biblio/1854703

Rodgers, T., Montes de Zapiain, D., Bolintineanu, D.S., Moser, D.R., Pokharel, R., & Pokharel, R. (2021). Microstructure-driven Parameter Calibration for Mesoscale Simulation [Conference Poster]. https://doi.org/10.2172/1847468

Guziewski, M., Montes de Zapiain, D., Dingreville, R.P.M., Coleman, S.P., & Coleman, S.P. (2021). Microscopic and Macroscopic Characterization of Grain Boundary Energy and Strength in Silicon Carbide via Machine-Learning Techniques. ACS Applied Materials and Interfaces, 13(2), pp. 3311-3324. https://doi.org/10.1021/acsami.0c15980

Montes de Zapiain, D., Stewart, J.A., Dingreville, R.P.M., & Dingreville, R.P.M. (2021). Accelerating phase-field-based microstructure evolution predictions via surrogate models trained by machine learning methods. npj Computational Materials, 7(1). https://doi.org/10.1038/s41524-020-00471-8

Montes de Zapiain, D. (2021). Calibration of Thermal Spray Microstructure Simulations to Experimental Data using Bayesian Optimization [Presentation]. https://www.osti.gov/biblio/1840829

Montes de Zapiain, D., Guziewski, M., Coleman, S.P., Dingreville, R.P.M., & Dingreville, R.P.M. (2020). Characterizing the Tensile Strength of Metastable Grain Boundaries inSilicon Carbide Using Machine Learning [Conference Poster]. https://www.osti.gov/biblio/1821092

Dingreville, R.P.M., Montes de Zapiain, D., Stewart, J.A., & Stewart, J.A. (2020). Accelerating phase-field based predictions via surrogate models trained by machine learning methods [Conference Poster]. https://www.osti.gov/biblio/1812455

Dingreville, R.P.M., Montes de Zapiain, D., Stewart, J.A., & Stewart, J.A. (2020). Accelerating phase-field based predictions via surrogate models trained by machine learning methods [Presentation]. https://www.osti.gov/biblio/1808265

Montes de Zapiain, D., Stewart, J.A., Dingreville, R.P.M., & Dingreville, R.P.M. (2019). Accelerated Microstructure Evolution Predictions Using Machine Learning Based Reduced Order Models [Conference Poster]. https://www.osti.gov/biblio/1643393