AI and Advanced Materials Discovery: Accelerating Sustainable Solutions for Energy Storage and Resource Efficiency

Mark Z. Jacobson

AI and Advanced Materials Discovery: Accelerating Sustainable Solutions for Energy Storage and Resource Efficiency

Author(s)	Mark Z. Jacobson
Country	United States
Abstract	The transition towards sustainable energy and resource-efficient industrial systems requires the development of advanced materials with improved performance, durability, affordability, and environmental compatibility. Conventional materials discovery is often constrained by lengthy experimentation cycles, high costs, complex synthesis pathways, and the enormous number of possible material compositions. Artificial Intelligence (AI), machine learning, high-throughput computation, and advanced simulation techniques are transforming this process by enabling researchers to predict material properties, identify promising compositions, optimise synthesis conditions, and accelerate experimental validation. This paper examines the convergence of AI and advanced materials discovery, with particular emphasis on energy storage and resource efficiency. A qualitative and conceptual methodology based on secondary literature is adopted to examine AI-enabled materials screening, machine-learning-assisted property prediction, generative materials design, autonomous laboratories, battery materials, hydrogen technologies, supercapacitors, catalysts, and circular-resource applications. The study proposes an integrated AI-driven materials discovery framework connecting data generation, materials databases, machine learning, computational simulation, candidate ranking, laboratory experimentation, and continuous feedback. Applications in lithium-ion, sodium-ion, solid-state and next-generation batteries demonstrate the potential of AI to accelerate the identification of materials with desirable electrochemical and structural properties. Beyond energy storage, AI can support resource efficiency through catalyst optimisation, lightweight materials, recycling technologies, corrosion-resistant materials, and low-carbon manufacturing. However, challenges remain concerning data quality, limited experimental datasets, model interpretability, generalisation, reproducibility, computational requirements, intellectual property, and the gap between predicted and experimentally achievable materials. The paper concludes that AI should be regarded not as a replacement for materials science expertise but as an enabling technology that integrates computational prediction with experimental knowledge. The combination of AI, advanced simulation, automation, and sustainable materials engineering could significantly reduce the time and resources required to develop technologies supporting the global transition towards sustainable energy and circular resource systems.
Keywords	Artificial Intelligence, Materials Discovery, Machine Learning, Advanced Materials, Energy Storage, Batteries, Resource Efficiency, Sustainable Materials, Autonomous Laboratories, Materials Informatics.
Field	Engineering
Published In	Volume 5, Issue 6, November-December 2023
Published On	2023-12-24

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American Journal of Advanced Multidisciplinary Research and Innovation

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AI and Advanced Materials Discovery: Accelerating Sustainable Solutions for Energy Storage and Resource Efficiency

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