Publications

Journal Publications 

1. Y. Li and G.D. Seidel, “The Multiscale Modeling of the Effects of Nanoscale Load Transfer on the Effective Elastic Properties of Unfunctionalized Carbon Nanotube-Polyethylene Nanocomposites”, Modelling Simul. Mater. Sci. Eng., Vol. 22, pp 025-023, 2014.

2. Y. Li and G.D. Seidel, “Multiscale modeling of functionalized interface effects on the effective elastic material properties of CNT-polyethylene nanocomposites”, Computational Materials Science, Vol. 107 pp. 216-234, 2015.

3. Y. Li and G.D. Seidel, “Multiscale modeling of the interface effects in CNT-epoxy nanocomposites”, Computational Materials Science, Vol. 153 pp. 363-381, 2018, Editor’s Choice

4. V. Phan, X. Zhang, Y. Li, C. Oskay, “Microscale modeling of creep deformation and rupture in Nickel-based superalloy IN 617 at high temperature”, Mechanics of Materials, Vol. 114, pp. 215-227, 2017.

5. Zheng, X. Chen, Y. Li, "Numerical study on mechanisms of soy protein as a functional modifier for polymer materials", Modelling and Simulation in Materials Science and Engineering, Vol. 27, pp. 085010, 2019.

6. X. Chen, H. Zhou, Y. Li, "Effective design space exploration of gradient nanostructured materials using active learning based surrogate models", Materials & Design, Vol. 183, pp. 108085, 2019.

7. Z. Zheng, Z. Liu, P. Wang and Y. Li, "Numerical Modeling on the Delamination-Induced Capacity Degradation of Silicon Anode", Journal of Energy Storage, Vol. 43, pp. 103190, 2021

8. D. Zhou, M. Fuentes-Cabrera, A. Singh, R. R. Unocic, J.M. Carrillo, K. Xiao, Y. Li, and B. Li, “Atomic Edge-Guided Polyethylene Crystallization on Monolayer Two-Dimensional Materials”, Macromolecules, Vol.55(2), pp. 559-567, 2022

9. P. Bansal, Z. Zheng, C. Shao, J. Li, M. Banu, B. Carlson, and Y. Li, "Physics-Informed Machine Learning Assisted Uncertainty Quantification for the Corrosion of Dissimilar Material Joints", Reliability Engineering & System Safety, Vo. 227, pp. 108711, 2022

10. Y. Zhang, J. Cui, K. Chen, S.H. Kuo, J. Sharma, R. Bhatta, Z. Liu, A. Ellis-Mohr, F. An, J. Li, Q. Chen, K. D. Foss, H. Wang, Y. Li, A. M. McCoy, G. W. Lau and Q. Cao. “A Smart Coating with Integrated Physical-Antimicrobial and Strain-Mapping Functionalities for Orthopedic Implants”, Science Advances, Vol. 9, pp. 7397, 2023

11. P. Bansal, Z. Zheng, B. Pan, Y. Meng, W. Wen, M. Banu, J. Li, B. E Carlson, C. Shao, P. Wang, Y. Li, Corrosion of Al-Fe self-pierce riveting joints with multiphysics-based modeling and experiments, Journal of Manufacturing Process, Vo. 95, Pages 434-445, 2023

12. A. Singh, Y. Li, Reliable Machine Learning Potentials based on Artificial Neural Network for Graphene, Computational Material Science, Volume 227, pp. ,112272, 2023

13. Z. Liu, J. Sederholm, K. Lan, E. Cho, M. Dipto, Y. Gurumukhi, K. Rabbi, M. Hatzell, N. Perry, N. Miljkovic, P. Braun, P. Wang, Y. Li. "Life cycle assessment of hydrometallurgical recycling for cathode active materials." Journal of Power Sources, Vol. 580, pp. 233345, 2023

14. H. Wu, Y. Xu, Z. Liu, Y. Li, and P. Wang. "Adaptive Machine Learning with Physics-based Simulations for Mean Time to Failure Prediction of Engineering Systems." Reliability Engineering & System Safety, Vol. 109553, 2023

15. B. Pan, H. Sun, D. Xie, S. Shang, N. Li, B. E. Carlson, Y. Li, Z. Liu, J. Li, Influence of accelerated corrosion on Al/steel RSW joints by in situ compression tests, Materials Science and Engineering: A, Volume 889, pp. 145851, 2024

16. Y. Xu, H. Wu, Z. Liu, P. Wang, and Y. Li, "Multi-Task Learning for Design under Uncertainty with Multi-Fidelity Partially Observed Information", Journal of Mechanical Design, J. Mech. Des. Vol. 146(8), pp. 081704, 2024

17. A. Singh, M. Sun, J. Chen, B. Li, and Y. Li, " Templating effect of MoSe2 on crystallization of polyethylene : A Molecular Dynamics Simulation Study", Journal of Physical Chemistry C, J. Phys. Chem. C Vol. 128(5), pp. 2147–2162, 2024

18. Z. Liu, H. Wu, P. Wang, Y. Li, “Reliability-Based Design Optimization of Additive Manufacturing for Lithium Battery Silicon Anode”, ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering, Vol. 10(3), pp. 031104, 2024

19. Hao Wu, Parth Bansal, Zheng Liu, Pingfeng Wang and Yumeng Li, "Uncertainty Quantification of Mechanical Behavior of Corroded Al-Fe Self-Pierce Riveting Joints with Statistical Shape Modeling”, Journal of Manufacturing Processes, Vol. 124, pp. 909-917, 2024

20. Zheng Liu, Pouya Kabirzadeh, Hao Wu, Wuchen Fu, Haoyun Qiu, Nenad Miljkovic, Yumeng Li, Pingfeng Wang, “Machine learning enhanced control co-design optimization of an immersion cooled battery thermal management system”. J. Appl. Phys. Vol. 136 (2), pp. 025001, 2024

21. Xu, Y., Bansal, P., Wang, P., & Li, Y. Physics-informed machine learning for system reliability analysis and design with partially observed information. Reliability Engineering & System Safety, 254, 110598. 2025

22. Jiang, Y., Liu, Z., Kabirzadeh, P., Wu, Y., Li, Y., Miljkovic, N. and Wang, P., Multi-fidelity physics-informed convolutional neural network for heat map prediction of battery packs. Reliability Engineering & System Safety, 256, p.110752. 2025

23. Liu, Z., Xu, Y., Jiang, Y., Renteria, A., Bansal, P., Xu, C., Wang, P. and Li, Y., Uncertainty quantification of additively manufactured architected cellular materials for energy absorption applications. ASCE-ASME J Risk and Uncert in Engrg Sys Part B Mech Engrg, 11(3). 2025

Conference Publications and Presentations

1. Y. Li, W. Xiao and P. Wang, “Uncertainty Quantification of Artificial Neural Network based Machine Learning Potentials”, IMECE2018-88071, Proceedings of the ASME 2018 International Mechanical Engineering Congress and Exposition ,IMECE 2018, November 9-15, 2018, Pittsburgh, PA, USA.

2. Y. Li, P. Wang, and W. Xiao. "Uncertainty Quantification of Atomistic Materials Simulation with Machine Learning Potentials", 2018 AIAA Non-Deterministic Approaches Conference, AIAA SciTech Forum, AIAA 2018-2166.

3. Z. Zheng, A. Singh and Y. Li, "Numerical Study on the Interfacial Modification Effects OF Soy Protein on POLY(VINYLIDENE FLUORIDE)", IMECE2019-11694, Proceedings of the ASME 2019 International Mechanical Engineering Congress and Exposition, November 11-14, 2019, Salt Lake City, UT, USA

4. X. Chen, H. Zhou and Y. Li, "Design of Gradient Nanotwinned Metal Materials using Adaptive Gaussian Process Based Surrogate Models, DETC2019-97659", Proceedings of the 2019 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference, August 18-21, 2019, Anaheim, California, USA

5. Singh, X. Chen, Y. Li, S. Koric and E. Guleryuz, "Development of Artificial Neural Network Potential for Graphene", AIAA Scitech 2020 Forum, Session: Applications of Artificial Intelligence and Machine Learning to Problems in Structures and Materials II, AIAA 2020-1861.

6. Z. Zheng, P. Bansal, P. Wang and Y. Li, "Simulation Assisted Design of LCO Cathode Materials With High Performance Stability", ASME 2020 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, Volume 11A: 46th Design Automation Conference (DAC), August 17–19, 2020.

7. Z. Zheng, A. Singh and Y. Li, "Molecular Dynamic Simulation Study on Soy Protein As Drug Delivery Vehicle", ASME 2020 International Mechanical Engineering Congress and Exposition, Volume 3: Advanced Materials: Design, Processing, Characterization, and Applications, November 16–19, 2020

8. Z. Zheng, P. Bansal and Y. Li, "Numerical Study on Antibacterial Effects of Bio-Inspired Nanostructured Surface", ASME 2020 International Mechanical Engineering Congress and Exposition, Volume 12: Mechanics of Solids, Structures, and Fluids, November 16–19, 2020

9. Z. Zheng, P. Bansal, P. Wang, C. Shao and Y. Li, "Corrosion Modeling and Prognosis of the Al-Fe Self-Pierce Riveting Joints", ASME 2020 International Mechanical Engineering Congress and Exposition, Volume 14: Safety Engineering, Risk, and Reliability Analysis, November 16–19, 2020

10. S. Yong , Z. Zheng , P. Wang and Y. Li, "Machine Learning Assisted Design for Active Cathode Materials", ASME 2020 International Mechanical Engineering Congress and Exposition, Volume 3: Advanced Materials: Design, Processing, Characterization, and Applications, November 16–19, 2020

11. Singh, Y. Li, “Guided self-assembly of polyethene on graphene”, AIAA SciTech 2022 Forum, Session: Applications of Artificial Intelligence and Machine Learning to Problems in Structures and Materials II, AIAA 2022-2143

12. Z. Liu, A. Renteria, Z. Zheng, P. Wang and Y. Li , Design of Additively Manufactured Functionally Graded Cellular Structures, 2022 IISE Manufacturing and Design (M&D) division conference, Finalist of the IISE M&D Division Best Track Paper Competition

13. Singh, Y. Li, "Antibacterial Effects of Bio-Inspired Nanoarchitectured Surface: A Coarse-Grained Simulation Study ", ASME 2022 International Mechanical Engineering Congress and Exposition (IMECE2022-95325)

14. P. Bansal, Z. Zheng, Y. Li, "Uncertainty Quantification on Galvanic Corrosion Based on Adaptive Based Surrogate Modeling", ASME 2022 International Mechanical Engineering Congress and Exposition (IMECE2022-95333)

15. Singh, Y. Li, "Machine Learning Potentials for Atomic Disorder Effects in Graphene", ASME 2022 International Mechanical Engineering Congress and Exposition (IMECE2022-95341)

16. Z. Zheng, Z. Liu, P. Wang, and Y. Li, "Design of Three-Dimensional Bi-Continuous Silicon Based Electrode Materials for High Energy Density Batteries", ASME 2022 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference (IDETC/CIE2022-89652)

17. P. Bansal, Z. Zheng, Y. Li, "Uncertainty Quantification for Dissimilar Material Joints Under Corrosion Environment", ASME 2022 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference (IDETC/CIE2022-89654)

18. P. Bansal, Z. Zheng, P. Wang, Y. Li, "Multiphysics Modeling on the Capacity Degradation of Silicon Anode", AIAA SCITECH 2023 Forum, 0772

19. Singh, Y. Li, "2D Materials Guided Self-assembly of Polymer: Molecular Dynamics Simulation Study", AIAA SCITECH 2023 Forum, 0142

20. Z. Liu, J. Wu, W. Fu, P. Kablrzadeh, I. Chung, M. Dipto, N. Miljkovic, P. Wang, Y. Li, "Control Co-Design of Battery Packs With Immersion Cooling", ASME 2023 International Mechanical Engineering Congress and Exposition (IMECE2023-112873)

21. Z. Liu, A. Singh, Y. Li, "Feature Importance and Uncertainty Quantification of Machine Learning Model in Materials Science", ASME 2023 International Mechanical Engineering Congress and Exposition (IMECE2023-112990)

22. P. Bansal, Y. Li, "Multi-Physics Simulation for Morphology Design of Si Anode", ASME 2023 International Mechanical Engineering Congress and Exposition (IMECE2023-113107)

23. Singh, Y. Li, "Understanding Governing Physical Mechanism of Bio-Inspired Nanostructured Antifouling Coating", ASME 2023 International Mechanical Engineering Congress and Exposition (IMECE2023-113115)

24. P. Bansal, Y. Li, "Multiphysics-Informed Machine Learning for Mechanical-Induced Degradation of Silicon Anode", ASME 2023 International Mechanical Engineering Congress and Exposition (IMECE2023-113404)

25. S. Sun, Y. Li, "Machine Learning Accelerated Atomistic Simulations for 2D Materials With Defects", ASME 2023 International Mechanical Engineering Congress and Exposition (IMECE2023-113427)

26. Wu, Y, & Li, Y. "Physics-Informed Multitask Learning for Material Development." Proceedings of the ASME 2023 Aerospace Structures, Structural Dynamics, and Materials Conference. ASME 2023 Aerospace Structures, Structural Dynamics, and Materials Conference. San Diego, California, USA. June 19–21, 2023.

27. H. Wu, P. Bansal, Z. Liu, Y. Li, & P. Wang, "Uncertainty Quantification on Mechanical Behavior of Corroded Plate With Statistical Shape Modeling." Proceedings of the ASME 2023 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. Volume 3B: 49th Design Automation Conference (DAC). Boston, Massachusetts, USA. August 20–23, 2023. V03BT03A051.

28. P. Bansal, , & Y. Li, "Multiphysics-Informed Machine Learning for Battery Design and Health Monitoring." Proceedings of the ASME 2023 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. Volume 3A: 49th Design Automation Conference (DAC). Boston, Massachusetts, USA. August 20–23, 2023. V03AT03A037.

29. Y. Wu, & Y. Li, "How to Encode Microstructure in Machine Learning: A Comparison Study." Proceedings of the ASME 2023 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. Volume 3B: 49th Design Automation Conference (DAC). Boston, Massachusetts, USA. August 20–23, 2023. V03BT03A032.

30. Singh, Y. Li, "2D materials guided interface in polymer based nanocomposites", 2024 AIAA SciTech, AIAA-2024-0766

31. Y. Wu, Y. Li, "Adaptive Surrogate Models with Unbalanced Data for Material Design", 2024 AIAA SciTech, AIAA-2024-0036

32. P. Bansal, Y. Li, "Multiphysics-informed Machine Learning for Uncertainty Quantification on Si Anode Based Battery Performance", 2024 AIAA SciTech, AIAA-2024-0038

33. P. Bansal, Y. Li, "Multiphysics Modeling and Simulation of Gas Sensor for No2 Detection", ASME 2024 International Mechanical Engineering Congress and Exposition, IMECE2024-145663

34. P. Bansal, Y. Li, "Multi-Physics Simulation Study of 3-D Bi-Continuous Silicon Anode for Li-Ion Batteries", ASME 2024 International Mechanical Engineering Congress and Exposition, IMECE2024-145633

35. Y. Wu, Y. Li, "Fusing Imbalanced Data via Physical Condition-Aware Surrogate Modeling", AIAA SCITECH 2025 Forum,AIAA 2025-0635