Publications

Whitney, B.C., Rodgers, T.M., Spangenberger, A.G., Rezwan, A.A., de Zapiain, D.M., Lados, D.A., & Lados, D.A. (2024). Solidification and crystallographic texture modeling of laser powder bed fusion Ti-6Al-4V using finite difference-monte carlo method. Materialia, 38(1). 10.1016/j.mtla.2024.102279

de Zapiain, D.M., 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). 10.1038/s41529-024-00476-4

de Zapiain, D.M., Venkatraman, A., Maestas, D., Melia, M.A., Noell, P., Katona, R.M., & Katona, R.M. (2024). Integrating Machine Learning into the Computational Calculations of Galvanic Corrosion [Presentation]. 10.2172/2586307

de Zapiain, D.M., Aragon, N., Lane, J.M.D., Carroll, J.D., Casias, Z., Battaile, C.C., Fensin, S., & Fensin, S. (2024). Characterizing the complex deformation behavior of Tin using genetic programming to perform symbolic regression [Presentation]. 10.2172/2586117

Rezwan, A.A., de Zapiain, D.M., Moser, D.R., Heiden, M.J., Rodgers, T.M., & Rodgers, T.M. (2024). Data Driven Unsupervised Clustering of Metal Additive Manufacturing Crystallographic Texture Data [Presentation]. 10.2172/2586080

Aragon, N., de Zapiain, D.M., Rothchild, E., Lim, H., & Lim, H. (2024). Developing data-driven dislocation mobility laws for BCC metals [Presentation]. 10.2172/2586044

Rezwan, A.A., de Zapiain, D.M., Moser, D.R., Heiden, M.J., Rodgers, T.M., & Rodgers, T.M. (2024). Unveiling Metal Additive Manufacturing Microstructure Through Data-Driven Unsupervised Clustering of Crystallographic Texture [Presentation]. 10.2172/2586317

Katona, R.M., Venkatraman, A., Roop, M., Noell, P., Melia, M.A., de Zapiain, D.M., Schaller, R.F., & Schaller, R.F. (2024). Finite Element Modeling of Atmospheric Corrosion: Boundary Conditions, Model Fidelities, and Computational Advances with Machine Learning [Conference Presentation]. 10.2172/2563985

Noell, P., de Zapiain, D.M., Venkatraman, A., Wilson, M.A., Melia, M.A., Katona, R.M., Khan, R.M., Delrio, F.W., Kacher, J., Merrill, L.C., Stavila, V., Allendorf, M., Brown, N.K., & Brown, N.K. (2024). A&L Presentation [Conference Poster]. 10.2172/2563961

de Zapiain, D.M., Katona, R.M., Roop, M., Noell, P., Maestas, D., & Maestas, D. (2024). Active learning protocols integrate corrosion experiments and simulations & accelerate predictions [Conference Poster]. 10.2172/2563948

de Zapiain, D.M., Foulk, J.W., Moore, N.W., Rodgers, T.M., & Rodgers, T.M. (2024). Calibration of Thermal Spray Microstructures Using Bayesian Optimization [Conference Poster]. 10.2172/2563962

Buzzy, M., Lim, H., de Zapiain, D.M., Kalidindi, S., Generale, A., & Generale, A. (2024). Active Learning for the Design of Polycrystalline Materials [Conference Presentation]. 10.2172/2540376

Katona, R.M., Venkatraman, A., Roop, M., Maestas, D., Melia, M.A., Noell, P., Schaller, R.F., de Zapiain, D.M., & de Zapiain, D.M. (2024). Active Learning Framework and Probabilistic Exploration of Finite Element Method Corrosion Models [Conference Presentation]. 10.2172/2540357

Rezwan, A.A., de Zapiain, D.M., Moser, D.R., Heiden, M.J., Rodgers, T.M., & Rodgers, T.M. (2024). Unveiling Metal Additive Manufacturing Microstructure Through Data-Driven Unsupervised Clustering of Crystallographic Texture [Conference Presentation]. 10.2172/2540354

Aragon, N., de Zapiain, D.M., Battaile, C.C., Lim, H., & Lim, H. (2024). Developing data-driven strength models incorporating temperature and strain-rate dependencies [Conference Presentation]. 10.2172/2540334

de Zapiain, D.M., Wilson, M.A., & Wilson, M.A. (2024). Accelerated predictions of charge density evolution in corrosive environments with machine learning [Conference Presentation]. 10.2172/2540446

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

de Zapiain, D.M., 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., de Zapiain, D.M., Romero, V.J., & Romero, V.J. (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). 10.1017/dce.2023.17

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

Foulk, J.W., Robbe, P., Lim, H., Wildey, T., de Zapiain, D.M., & de Zapiain, D.M. (2023). The multifaceted nature of uncertainty in structure-property linkage with crystal plasticity finite element model [Conference Presentation]. 10.2172/2540528

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

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

de Zapiain, D.M., Aragon, N., Lim, H., Carroll, J.D., Casias, Z., Fensin, S., Battaile, C.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 Presentation]. 10.2172/2430874

Ao, T., Morgan, D.V., Lane, J.M.D., Brown, N.P., Donohoe, B.D., Knudson, M.D., Martinez, C., de Zapiain, D.M., 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 Presentation]. 10.2172/2431265

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

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

de Zapiain, D.M., Maestas, D., Melia, M.A., Noell, P., Katona, R.M., & Katona, R.M. (2023). Leveraging Machine Learning to Increase Computational Efficiency in Electrochemical Systems: An Application to Galvanic Corrosion [Conference Presentation]. 10.2172/2540459

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

Foulk, J.W., Robbe, P., de Zapiain, D.M., Wildey, T., Lim, H., & Lim, H. (2023). The multifaceted nature of uncertainty in structure-property linkage with crystal plasticity finite element model [Conference Presentation]. 10.2172/2431686

de Zapiain, D.M., Foulk, J.W., Lim, H., & Lim, H. (2023). Development of structure-property linkages for damage in crystalline microstructures using Bayesian inference and unsupervised learning [Conference Presentation]. 10.2172/2431659

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

de Zapiain, D.M. (2023). Materials Innovation: Making the Future Possible [Conference Presentation]. 10.2172/2540423

de Zapiain, D.M., Greene, B., Lim, H., & Lim, H. (2023). Materials Data Driven Design (MAD3) [Conference Presentation]. 10.2172/2540456

Foulk, J.W., Robbe, P., Wildey, T., de Zapiain, D.M., Lim, H., & Lim, H. (2022). The multifaceted nature of uncertainty in structure-property linkage with crystal plasticity finite element model [Conference Proceeding]. https://www.osti.gov/biblio/2432135

de Zapiain, D.M., 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

Foulk, J.W., Robbe, P., Wildey, T., de Zapiain, D.M., Lim, H., & Lim, H. (2022). The multifaceted uncertainty nature of structure-property linkage with crystal plasticity finite element model [Conference Paper]. 10.2514/6.2023-0525

Lim, H., Park, T., de Zapiain, D.M., Pourboghrat, F., & Pourboghrat, F. (2022). Investigating plastic anisotropy using crystal plasticity and deep learning models [Conference Presentation]. 10.2172/2006148

Ao, T., Donohoe, B.D., Martinez, C., Knudson, M.D., de Zapiain, D.M., 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. 10.2172/1891594

de Zapiain, D.M., Bergel, G.L., Lim, H., Romero, V.J., & Romero, V.J. (2022). Estimating Uncertainty of Neural Network Predictions for Inelastic Mechanical Deformation using Coupled FEM-NN Approach [Conference Presentation]. 10.2172/2005250

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

de Zapiain, D.M., 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]. 10.2172/2003844

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

de Zapiain, D.M., Lim, H., Park, T., Pourboghrat, F., & Pourboghrat, F. (2022). Predicting plastic anisotropy using crystal plasticity and Bayesian neuralnetwork surrogate models [Conference Presentation]. 10.2172/2002951

Lim, H., de Zapiain, D.M., Park, T., Pourboghrat, F., & Pourboghrat, F. (2022). Investigating plastic anisotropy using CP-FEM and ML techniques [Conference Presentation]. 10.2172/2002526

Lim, H., de Zapiain, D.M., & de Zapiain, D.M. (2022). MADDD NM Technology Showcase Presentation [Conference Presentation]. 10.2172/2001904

Lane, J.M.D., Thurston, B., Ao, T., de Zapiain, D.M., 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 Presentation]. 10.2172/2002034

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

Rodgers, T.M., de Zapiain, D.M., Foulk, J.W., Murray, S.E., Mahaffey, J.T., & Mahaffey, J.T. (2022). Mesoscale Simulation of Cold Spray Microstructure Formation [Conference Presentation]. 10.2172/2001897

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

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

de Zapiain, D.M. (2021). Establishing a data-driven deformation model for Tin using symbolic regression and genetic programming [Conference Presentation]. 10.2172/1878275

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

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

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

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

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

Guziewski, M., de Zapiain, D.M., Dingreville, R., 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. 10.1021/acsami.0c15980

de Zapiain, D.M., Stewart, J.A., Dingreville, R., & Dingreville, R. (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

de Zapiain, D.M. (2020). Calibration of Thermal Spray Microstructure Simulations to Experimental Data using Bayesian Optimization [Presentation]. https://www.osti.gov/biblio/1840829

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

Dingreville, R., de Zapiain, D.M., 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., de Zapiain, D.M., 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

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