Research

Despite the remarkable progress made in the areas of energy technology and advanced manufacturing, there still exist roadblocks to ‘excellent functional performance’ of materials along with ‘efficient processing’, making systematic design of both materials and processes a necessity. The understanding of the material physics embodied by chemistry-processing-structure-property correlation falls short due to the rapid developments in these fields. Multiscale computational modeling is a powerful tool for the fundamental understanding of material behavior in such applications which transcends length and time scales. It can second experiments by not only objectively dissecting the physics at different scales but also integrating them (atomistic, meso and continuum), to understand the overall mechano-thermo-electrochemical behavior of the materials and their performance. 

• Microstructural evolution during sintering and Hot Isostatic Pressing of water atomized Binder Jet Additive Manufactured 316L SS feedstock.

• Process Modeling for blown powder Direct Energy Deposition of C103 refractory alloy.

• Cellular Automata based microstructure prediction for non-equilibrium cooling conditions during Additive Manufacturing of multi-component Al alloys.

• Design of high-entropy Co-Cr-Fe-Mn-Mo-Ni alloys for corrosion resistance through high-throughput computation and machine learning.

• Design and discovery of room temperature superconductive carbonaceous materials using Generative Adversarial Networks.

• DFT based high-throughput framework, ‘CRADLE’ to screen and rank microstructures for improved corrosion resistancein Mg-Al-RE alloys.

• CFD based solidification and microstructural prediction model for laser based additive manufacturing of Co-Cr-Mo alloys using OpenFOAM. 

• Screening 2D materials for sea water desalination using machine learning and molecular dynamics.

• Multi-physics multiscale framework for prediction of electrochemical characteristics of oxide and proton conducting Solid Oxide Fuel Cells/Electrolyzer Cells (SOFC/SOEC). 

• High-throughput screening of all elements in the periodic table to identify stable perovskites for high proton conductivity, and classification for the temperature and environment dependent type of charge carrier were performed.

• Data driven continuum analysis for characterizing electrokinetics in low permeability geomaterials.

• Design of self-healing polymers through machine learning.

• Microstructural model for OpenCAST, a diecasting solidification software forAl-Si alloys.

• Cellular Automata based three phase solid-solid phase transformation model to simulate microstructural evolution in the SDAS length scale helpful for heat treatment process optimization and alloy design for 6XXX and 7XXX alloys.

• First principles atomistic study of bulk and defect properties of Al3Sc1-xZrx type precipitates to explain the strengthening mechanisms in Al-Sc-Zr alloys. 

• B.Goh, Y. Wang, P. Nelaturu, M. Moorehead, T. Duong, P. Priya, D. Thoma, and S. Chaudhuri, J. Hattrick-Simpers, K. Sridharan, A. Couet, Nobility vs Mobility: Unlocking New Insights Into Molten Salt Corrosion Mechanisms of High Entropy Alloys with High-Throughput Experiments and Machine Learning-Enabled Analysis. Matter 7, (2024) 2313: doi: 10.1016/j.matt.2024.05.004

• P. Priya, K. Fezi, D.R. Johnson and M.J.M. Krane, Effect of Composition on Microstructural Evolution during Homogenization of 7XXX Alloys. arXiv:2308.13800 (2023). doi: 10.48550/arXiv.2308.13800.

• P. Priya, Y. Sun, D.R. Johnson, K. P. Trumble and M.J.M. Krane, Radial Variation of Microstructure in a Direct- Chill Cast AA7050 Billet on Homogenization. arXiv:2308.13799 (2023). doi: 10.48550/arXiv.2308.13799.

• P. Priya, C. T. Nguyen, A. Saxena, N. Aluru, Machine Learning Assisted Screening of 2D Materials for Water Desalination. ACS Nano 16 (2022).

• P. Priya and N. Aluru, Accelerated Design and Discovery of Perovskites with High Conductivity for Energy Applications through Machine Learning. npj Computational Materials 7 (2021).

• P. Priya, K. Kuhlman, and N. Aluru, Pore-scale Modeling of Electrokinetics in Geomaterials. Transport in Porous Media 137 (2021) 651-666. doi: 10.1007/s11242-021-01581-7 .

• P. Priya and N. Aluru, A multiscale framework to predict electrochemical characteristics of Yttrium doped Barium Zirconate based Solid Oxide Cells. Journal of Power Sources 481 (2021) 228969. doi: 10.1016/j.jpowsour.2020.228969.

• P. Priya, B. Mercer, S. Huang, M. Aboukhatwa, L. Yuan, and S. Chaudhuri, Towards Prediction of Microstructure during Laser Based Additive Manufacturing Process of Co-Cr-Mo Powder Beds. Material and Design 196 (2020) 109117. doi: 10.1016/j.matdes.2020.109117.

• P. Priya, X. Yan, and S. Chaudhuri, Study of Intermetallics for Corrosion and Creep Resistant Microstructure in Mg-RE and Mg-Al-RE Alloys through a Data-Centric High-Throughput DFT Framework. Computational Materials Science 175 (2020) 109541. doi: 10.1016/j.commatsci.2020.109541.

• S. Shahane, S. Mujumdar, N. Kim, P. Priya, N. Aluru, P. Ferreira, S. G. Kapoor and S. Vanka. Simulations of Die Casting with Uncertainty Quantification. Journal of Manufacturing Science and Engineering 141(2019) 041003. doi: 10.1115/1.4042583

• L. Ma, P. Priya and N. R. Aluru, A Multiscale Model for Electrochemical Reactions in LSCF Based Solid Oxide Cells, Journal of Electrochemical Society. 165 (2018): F1232-F1241. doi: 10.1149/2.0921814jes

• P. Priya, D. R. Johnson, and M. J. M. Krane, Precipitation during cooling in 7XXX alloys. Computational Materials Science 139 (2017): 273-284. doi: 10.1016/j.commatsci.2017.08.008

• P. Priya, D. R. Johnson, and M. J. M. Krane, Modeling phase transformation kinetics during homogenization of 7XXX aluminum alloys. Computational Materials Science 138 (2017): 277-287. doi: 10.1016/j.commatsci. 2017.06.043.

• P. Priya, D. R. Johnson, and M. J. M. Krane, Numerical study of microstructural evolution during homogenization of Al-Si-Mg-Fe-Mn alloys. Metallurgical and Materials Transactions A 47 (2016): 4625–4639. doi: 10.1007/s11661-016-3610-8.

• X. Yan, A. Samei, B. Mercer, P. Priya and S. Chaudhuri, Corrosion-Resistance Microstructure Design using Mesoscale Modeling Environment for Additive Manufacturing for Co-Cr Alloys. Microscopy and Microanalysis 25(2019): 2580-2581. doi: 10.1017/S1431927619013631

• S. Shahane, S. Mujumdar, N. Kim, P. Priya, N. Aluru, P. Ferreira, S. G. Kapoor and S. Vanka. Virtually-Guided Certification with Uncertainty Quantification Applied to Die Casting. ASME. Verification and Validation, ASME 2018 Verification and Validation Symposium: V001T03A003. doi:10.1115/VVS2018-9323.

• P. Priya, M.J.M. Krane, D.R. Johnson and K.P. Trumble, Precipitation of Al3Zr dispersoids during homogenization of Al-Zn-Cu-Mg-Zr alloys. Light Metals 2016, 213-218. doi: 10.1002/9781119274780.ch36.

• P. Priya, M.J.M. Krane and D.R. Johnson, A numerical and experimental study of homogenization of Al-Si-Mg alloys. Light Metals 2014, 213-218. doi: 10.1002/9781118888438.ch72.

• Y. Sun, D.R. Johnson, K.P. Trumble, P.Priya and M.J.M. Krane, Effect of Mg2Si phase on extrusion of AA6005 aluminum alloy. Light Metals 2014, 213-218. doi: 10.1002/9781118888438.ch73.

• HEA 2021, Data Driven Design of High Entropy Alloys, Charlotte, NC.

• TMS 2016, Precipitation of Al3Zr dispersoids during homogenization of Al-Zn-Cu-Mg-Zr alloys, Nashville, TN.

• MS&T 2015, Numerical Study of Phase Transformations during Homogenization of Al-Si-Mg-Fe-Mn alloys,         Columbus, OH.

• ASME 2014, Numerical Study of the Microstructural Evolution during Homogenization of 6XXX and 7XXX series Aluminum alloys, Montreal, Canada.

• TMS 2014, A Numerical and Experimental study of Homogenization of Al-Si-Mg alloys, San Diego, CA.