International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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Volume 8 Issue 3
May-June 2026

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Artificial Intelligence in the Discovery of Functional Solid-State Materials: Opportunities, Challenges, and Future Perspectives

Author(s) Narendra Singh Lakhera
Country India
Abstract Data-rich, closed-loop enterprise is transforming functional solid-state materials discovery from a sequential process of intuition, synthesis, characterization, and delayed theory to an artificial intelligence (AI) enabled process. This review discusses the application of machine learning, graph neural networks, active learning, high-throughput density functional theory (DFT), universal machine-learned interatomic potentials and autonomous laboratories for discovering inorganic crystals, solid electrolytes, semiconductors, magnets, thermoelectric and other functional materials. The paper claims that the most promising opportunity is not merely to replace physics-based modelling, but to couple AI with DFT, thermodynamics, synthesis knowledge and uncertainty-aware experimental validation. Reported demonstrations, such as GNoME-scale screening, graph-based property prediction, charge-informed potentials, and autonomous powder synthesis show that AI can increase candidate spaces by orders of magnitude and decrease wasted experiments. Yet, data bias, failure to report failed experiments, synthesis complexity, benchmark leakage, out-of-distribution errors, weak interpretability, and lack of negative-data reporting still remain substantial barriers to SCI-level reproducibility. Future advances will rely on FAIR data infrastructures, prospective benchmarks, hybrid physics-AI models, human-auditable autonomous laboratories and application-specific validation pipelines. The discovery of functional solid-state materials should therefore be considered as an integrated cyber-physical system and not merely as a computational ranking exercise.
Keywords Artificial intelligence; Functional materials; Solid-state materials; Graph neural networks; Materials informatics; Autonomous laboratory; Machine-learned interatomic potentials
Field Physics
Published In Volume 8, Issue 3, May-June 2026
Published On 2026-06-17
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E-ISSN 2582-2160
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