Physics > Applied Physics
arXiv:2605.21083 (physics)
Authors:D.-M. Mei, K. Acharya, C. M. Adhikari, M. Adhikari, S. Aryal, B. V. Benson, K. Bhatta, S. Bhattarai, N. Budhathoki, A. M. Castillo, D. Chakraborty, S. Chhetri, S. Choudhury, T. A. Chowdhury, R. D. Cruz, B. Cui, S. Dhital, K.-M. Dong, R. Gapuz, A. Ghasemi, E. Z. Gnimpieba, B. D. S. Gurung, H. A. Hashim, R. I. Harry, K.-E. Hasin, M. K. Hassanzadeh, M. K. Jha, D. Kim, K.-C. Kong, B. Lama, A. Mahat, N. Maharjan, A. Majeed, J. Mammo, M. M. Masud, K. S. Moore, A. Nawaz, H. Oli, S. A. Panamaldeniya, L. Pandey, R. Pandey, Z. Peng, A. Prem, M. M. Rana, K. Rana Magar, R. Rizk, C. S. Tadi, L.-W. Wang, Y. Yang, G.-L. Yin, C.-X. Yu, D. Zeng, M. Zhou, Q. Zhou
Abstract:Materials discovery and biomedical translation increasingly require models that can reason across composition, processing, structure, biological response, manufacturability, safety, and governance constraints. Existing materials and biomedical data ecosystems are powerful but remain poorly coupled for AI-guided discovery. Here we present AIMBio, a conceptual framework for an AI-native, FAIR, and governance-aware decision layer that links materials provenance, biomedical context, knowledge graphs, uncertainty-aware machine learning, and human-in-the-loop active learning. The framework formulates biomedical-materials discovery as constrained multi-objective optimization under uncertainty and introduces practical requirements for metadata, model documentation, risk-tiered governance, evaluation metrics, and phased implementation. To make the roadmap testable, we add a minimum viable prototype specification and a worked pilot for AI-guided nanomaterials for drug delivery. AIMBio is positioned as exploratory and preclinical discovery infrastructure, not as clinical decision-support software; any clinical or regulated-device use would require separate validation, change control, and regulatory review. The central contribution is a publishable platform blueprint for converting fragmented materials and biomedical records into auditable, experimentally actionable, and translationally responsible discovery workflows.
| Comments: | 35 pages, 4 figures, and 12 tables |
| Subjects: | Applied Physics (physics.app-ph); Machine Learning (cs.LG); Biological Physics (physics.bio-ph); Medical Physics (physics.med-ph) |
| Cite as: | arXiv:2605.21083 [physics.app-ph] |
| (or arXiv:2605.21083v1 [physics.app-ph] for this version) | |
| https://doi.org/10.48550/arXiv.2605.21083 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Dongming Mei [view email]
[v1]
Wed, 20 May 2026 12:18:49 UTC (79 KB)
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