Work to build on.

Taheri-Mousavi, S. M., and Ni, B. (2026). AlloyGen: A Physics-grounded Self-adaptive Multi-agent Framework for Autonomous Alloy Design Workflows in Additive Manufacturing. Research Square, version 1, August 4, 2026. Preprint.

Ni, B. and Buehler, M.J., 2026. VibeGen: Agentic end-to-end de novo protein design for tailored dynamics using a language diffusion model. Matter, 9, 102706.

Ni, B., Glaser, B., Taheri-Mousavi, S. M., 2025. End-to-end prediction and design of additively manufacturable alloys using a generative AlloyGPT model. npj Computational Materials, 11(1), 294.

Shin, B.*, Ni, B.*, Toh C.*, Steinbach, D., Yang, Z., Sassi, L., Ai, Q., Niu, K., Lin, J., Suenaga, K., Han, Y., Buehler, M.J., Özyilmaz, B. and Lou, J., 2025. Intrinsic toughening in monolayer amorphous carbon nanocomposites. Matter. (*Co-first authors)

Ni, B. and Buehler, M.J., 2024. MechAgents: Large language model multi-agent collaborations can solve mechanics problems, generate new data, and integrate knowledge. Extreme Mechanics Letters, 67, p.102131.

Ni, B., Kaplan, D.L. and Buehler, M.J., 2024. ForceGen: End-to-end de novo protein generation based on nonlinear mechanical unfolding responses using a protein language diffusion model. Science Advances 10(6): eadl4000.

Ni, B., Kaplan, D.L. and Buehler, M.J., 2023. Generative design of de novo proteins based on secondary-structure constraints using an attention-based diffusion model. Chem, 9(7), pp.1828–1849. (Selected as the issue cover)

Liu, F.Y., Ni, B. and Buehler, M.J., 2022. PRESTO: Rapid protein mechanical strength prediction with an end-to-end deep learning model. Extreme Mechanics Letters, 55, p.101803.

Yang, Y., Song, Z., Lu, G., Zhang, Q., Zhang, B., Ni, B., Wang, C., Li, X., Gu, L., Xie, X. and Gao, H., 2021. Intrinsic toughening and stable crack propagation in hexagonal boron nitride. Nature, 594(7861), pp.57-61.

Ni, B. and Gao, H., 2021. A deep learning approach to the inverse problem of modulus identification in elasticity. MRS Bulletin, 46, pp.19-25.

Ni, B. and Gao, H., 2020. Engineer energy dissipation in 3D graphene nanolattice via reversible snap-through instability. Journal of Applied Mechanics, 87(3), p.031012.

Guo, K.*, Ni, B.* and Gao, H., 2020. Tuning crack-inclusion interaction with an applied T-stress. International Journal of Fracture, 222(1-2), pp.13-23. (*Co-first authors)

Li, J., Ni, B., Zhang, T. and Gao, H., 2018. Phase field crystal modeling of grain boundary structures and growth in polycrystalline graphene. Journal of the Mechanics and Physics of Solids, 120, pp.36-48.

Hacopian, E.F.*, Yang, Y.*, Ni, B.*, Li, Y., Li, X., Chen, Q., Guo, H., Tour, J.M., Gao, H. and Lou, J., 2018. Toughening graphene by integrating carbon nanotubes. ACS Nano, 12(8), pp.7901-7910. (*Co-first authors)

Luu, R. K., Arevalo, S., Lu, W., Ni, B., Yang, Z., Shen, S. C., Berkovich, J., Hsu, Y.-C., Zan, S., & Buehler, M. J. (2024). Learning from Nature to Achieve Material Sustainability: Generative AI for Rigorous Bio-inspired Materials Design. An MIT Exploration of Generative AI, March.

Ni, B.*, Steinbach, D.*, Yang, Z., Lew, A., Zhang, B., Fang, Q., Buehler, M.J. and Lou, J., 2022. Fracture at the two-dimensional limit. MRS Bulletin, 47(8), pp.848-862. (*Co-first authors)

Ni, B. and Gao, H., 2021. Breaking two-dimensional polymeric crystals. Matter, 4(3), pp.763-765.

Ni, B. and Gao, H., 2020. Harness the power of fracture: controlled fragmentation of graphene via substrate necking. Matter, 2(3), pp.521-524.

Ni, B.*, Zhang, T.*, Li, J., Li, X., and Gao, H., 2019. Topological design of graphene. Handbook of Graphene, Volume 2: Physics, Chemistry, and Biology, Wiley, DOI: 10.1002/9781119468455.ch19. (*Co-first authors)

Shin, B., Ni, B., Sakai, N., Sasaki, T., Padture, N. P., Sheldon, B. W., Han, Y. and Lou, J., 2026. Anisotropic Mechanical Behaviors in Monolayer 2D Titanium Oxide. Manuscript under review.

Manuscript under review · Public link not yet verified.

Ni, B., and Taheri-Mousavi, S. M., 2025. Accelerate materials discovery and design via generative artificial intelligence, under review.

Manuscript under review · Public link not yet verified.