arXiv subjects

S. Aryal

Publications and source records attributed to S. Aryal.

3 recordsLinked to original sources

arXiv2026

AIMBio-Mat: An AI-Native FAIR Platform for Closed-Loop Materials Discovery and Biomedical Translation

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.

physics.app-ph↗

arXiv2026

Building a Regional Data-Centric Materials Science Ecosystem for Processing-Rich Materials Innovation in the Great Plains

Data-centric materials science is changing how materials are discovered, optimized, manufactured, and qualified, yet many deployment-limiting materials problems still depend on experimental, processing-rich, device-level, and field-relevant data that are difficult to capture in conventional materials databases. This perspective argues that the Great Plains and adjacent interior research corridor can make a distinctive national contribution by organizing distributed experimental assets into a trusted regional materials-data ecosystem. The proposed model emphasizes FAIR metadata, provenance, persistent sample identifiers, uncertainty-aware modeling, semi-closed-loop workflows, stackable workforce training, and tiered governance for academic, public, controlled-access, and industry-protected data. We identify five coupled barriers -- fragmented data, weak algorithm--laboratory translation, uneven access to cyberinfrastructure and technical staff, workforce gaps at the materials--data interface, and insufficient incentives for sharing and reuse -- and propose a staged roadmap for addressing them. A high-purity germanium pilot illustrates how regional strengths can be converted into reusable datasets, benchmark models, trained personnel, and decision-improving workflows. The broader message is that regional leadership in data-centric materials science will depend less on geographic concentration than on trustworthy data practices, interoperable infrastructure, cross-trained people, and application-driven materials challenges.

cond-mat.mtrl-sci↗

arXiv2012

New Young Star Candidates in BRC 27 and BRC 34

We used archival Spitzer Space Telescope mid-infrared data to search for young stellar objects (YSOs) in the immediate vicinity of two bright-rimmed clouds, BRC 27 (part of CMa R1) and BRC 34 (part of the IC 1396 complex). These regions both appear to be actively forming young stars, perhaps triggered by the proximate OB stars. In BRC 27, we find clear infrared excesses around 22 of the 26 YSOs or YSO candidates identified in the literature, and identify 16 new YSO candidates that appear to have IR excesses. In BRC 34, the one literature-identified YSO has an IR excess, and we suggest 13 new YSO candidates in this region, including a new Class I object. Considering the entire ensemble, both BRCs are likely of comparable ages, within the uncertainties of small number statistics and without spectroscopy to confirm or refute the YSO candidates. Similarly, no clear conclusions can yet be drawn about any possible age gradients that may be present across the BRCs.

astro-ph.SR↗