| Multi-objective materials bayesian optimization with active learning of design constraints: Design of ductile refractory multi-principal-element alloys D Khatamsaz, B Vela, P Singh, DD Johnson, D Allaire, R Arróyave Acta Materialia 236, 118133, 2022 | 61 | 2022 |
| Bayesian optimization with active learning of design constraints using an entropy-based approach D Khatamsaz, B Vela, P Singh, DD Johnson, D Allaire, R Arróyave npj Computational Materials 9 (1), 49, 2023 | 52 | 2023 |
| Adaptive active subspace-based efficient multifidelity materials design D Khatamsaz, A Molkeri, R Couperthwaite, J James, R Arróyave, ... Materials & Design 209, 110001, 2021 | 34 | 2021 |
| Efficiently exploiting process-structure-property relationships in material design by multi-information source fusion D Khatamsaz, A Molkeri, R Couperthwaite, J James, R Arróyave, D Allaire, ... Acta Materialia, 116619, 2021 | 32 | 2021 |
| A perspective on Bayesian methods applied to materials discovery and design R Arróyave, D Khatamsaz, B Vela, R Couperthwaite, A Molkeri, P Singh, ... MRS communications 12 (6), 1037-1049, 2022 | 27 | 2022 |
| Materials design through batch bayesian optimization with multisource information fusion R Couperthwaite, A Molkeri, D Khatamsaz, A Srivastava, D Allaire, ... Jom 72, 4431-4443, 2020 | 26 | 2020 |
| On the importance of microstructure information in materials design: PSP vs PP A Molkeri, D Khatamsaz, R Couperthwaite, J James, R Arróyave, D Allaire, ... Acta Materialia 223, 117471, 2022 | 25 | 2022 |
| Data-augmented modeling for yield strength of refractory high entropy alloys: A bayesian approach B Vela, D Khatamsaz, C Acemi, I Karaman, R Arróyave Acta Materialia 261, 119351, 2023 | 23 | 2023 |
| Bayesian optimization of multiobjective functions using multiple information sources D Khatamsaz, L Peddareddygari, S Friedman, D Allaire AIAA Journal 59 (6), 1964-1974, 2021 | 20 | 2021 |
| A physics informed bayesian optimization approach for material design: application to NiTi shape memory alloys D Khatamsaz, R Neuberger, AM Roy, SH Zadeh, R Otis, R Arróyave npj Computational Materials 9 (1), 221, 2023 | 19 | 2023 |
| Efficient multi-information source multiobjective bayesian optimization D Khatamsaz, L Peddareddygari, S Friedman, DL Allaire AIAA Scitech 2020 Forum, 2127, 2020 | 17 | 2020 |
| Towards inverse microstructure-centered materials design using generative phase-field modeling and deep variational autoencoders V Attari, D Khatamsaz, D Allaire, R Arroyave Acta Materialia 259, 119204, 2023 | 15 | 2023 |
| A comparison of reification and cokriging for sequential multi-information source fusion D Khatamsaz, DL Allaire AIAA Scitech 2021 Forum, 1477, 2021 | 10 | 2021 |
| Illustrating an Effective Workflow for Accelerated Materials Discovery M Mulukutla, AN Person, S Voigt, L Kuettner, B Kappes, D Khatamsaz, ... Integrating Materials and Manufacturing Innovation, 1-21, 2024 | 6 | 2024 |
| The BAREFOOT optimization framework R Couperthwaite, D Khatamsaz, A Molkeri, J James, A Srivastava, ... Integrating Materials and Manufacturing Innovation 10, 644-660, 2021 | 6 | 2021 |
| Multi-objective Bayesian alloy design using multi-task Gaussian processes D Khatamsaz, B Vela, R Arróyave Materials Letters 351, 135067, 2023 | 5 | 2023 |
| Materials design using an active subspace-based batch bayesian optimization approach D Khatamsaz, R Arroyave, DL Allaire AIAA SCITECH 2022 Forum, 0075, 2022 | 5 | 2022 |
| Hierarchical Gaussian Process-Based Bayesian Optimization for Materials Discovery in High Entropy Alloy Spaces SMAA Alvi, J Janssen, D Khatamsaz, D Perez, D Allaire, R Arroyave arXiv preprint arXiv:2410.04314, 2024 | 2 | 2024 |
| Data-driven study of composition-dependent phase compatibility in NiTi shape memory alloys SH Zadeh, C Cakirhan, D Khatamsaz, J Broucek, TD Brown, X Qian, ... Materials & Design, 113096, 2024 | 2 | 2024 |
| An Interoperable Multi Objective Batch Bayesian Optimization Framework for High Throughput Materials Discovery T Hastings, M Mulukutla, D Khatamsaz, D Salas, W Xu, D Lewis, N Person, ... arXiv preprint arXiv:2405.08900, 2024 | 2 | 2024 |
Raymundo ArroyaveProfessor of Materials Science and Engineering, Mechanical Engineering. Texas A&MEmail verificata su tamu.edu