(untitled) (Journal of Advanced Artificial Intelligence Research, 2026)
The prediction of gas adsorption and thermo-physical properties in advanced carbon-based materials, such as carbon foams, graphene derivatives, and hexagonal boron nitride hybrids, has become a central challenge in the design of next-generation sorbents and thermal management systems. Traditional experimental and simulation-based approaches are constrained by high computational costs, limited throughput, and the combinatorial complexity of material space. Machine learning offers a transformative paradigm by enabling rapid, accurate, and scalable property prediction from structural and compositional descriptors. This paper presents a system-level analysis of machine learning-assisted prediction frameworks, focusing on the architectural, infrastructural, and governance dimensions that underpin their deployment. We examine the trade-offs between representation learning techniques, uncertainty quantification, and data quality in the context of gas adsorption on doped and defective carbon surfaces. We further explore the integration of thermophysical property prediction into broader socio-technical infrastructures, including material informatics databases, open science policies, and reproducibility standards. Drawing on recent advances in first-principles calculations and experimental characterization, we discuss how machine learning models can bridge the gap between atomistic simulations and engineering applications. The paper also critically assesses issues of algorithmic fairness, robustness against out-of-distribution data, and the policy implications of using predictive models in safety-critical contexts such as toxic gas capture and thermal protection systems. By synthesizing insights from computational materials science, data engineering, and governance studies, we propose a framework for sustainable, equitable, and reliable deployment of machine learning in carbon-based material design.