Prudvi Saisaran Ponduru · 2026 · International Journal of Innovative Research in Engineering & Multidisciplinary Physical Sciences
Paper
Materials discovery in physical sciences is being transformed by the convergence of large language models, graph neural networks, foundation atomistic models, high-throughput databases, physics-based simulation, autonomous laboratories, and sustainability-aware decision making. However, many current pipelines remain fragmented: literature mining is separated from crystal-structure learning, machine learning prediction is separated from physics validation, and performance optimization is often separated from cost, toxicity, energy, recyclability, and environmental impact. This paper proposes Matter AI, a multimodal artificial intelligence framework for accelerated and responsible materials discovery. The proposed framework integrates literature mining using large language models, federated collection of materials databases, chemical and crystal-structure feature extraction, machine learning and graph neural network property prediction, physics-informed validation, multi-objective candidate ranking, human or robotic experimental validation, and sustainability scoring. The paper is grounded in recent 2023 to 2026 developments, including structure-aware multimodal language models, pretrained atomistic potentials, generative crystal models, benchmark-driven stability prediction, large open density functional theory datasets, source-tracked literature extraction, and closed-loop autonomous synthesis. No fabricated experiment is reported. Instead, a rigorous framework, mathematical formulation, implementation protocol, evaluation metrics, and ablation plan are presented for future empirical deployment. The main contribution is a unified discovery architecture that treats scientific evidence, atomistic structure, physical laws, uncertainty, experimental validation, and sustainability as coupled components rather than isolated stages.
Analysis
This paper proposes Matter AI, a unified multimodal AI framework designed to accelerate and improve the responsibility of materials discovery by integrating various stages of the process.
Discovery
Yang D; Zheng Z; Wang H; Zhao H; Li Z; Liu Y; Xiong Y; Luo Z
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Bernard Mallia; Rossana Caputo
Mohamed Hamidi; Mohamed Loutou
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Mihail Kolev
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