Integrating Mathematics, Artificial Intelligence, and Chemistry for Digital Materials Discovery

The discovery of digital materials is extending the traditional materials research fast in the application of mathematics, artificial intelligence (AI), and chemistry into the coherent data-driven frameworks. Mathematics provides the primary language of describing materials, modeling structure-property-process relationships and optimizing synthesis pathways. These mathematical interfaces make it easy to learn AI‑based predictions regarding the data and thus allow poor to independently experiment, reverse design and forecasting. These virtual techniques rely on physical reality, thus being chemically readable, viable and experimentally practicable. This review is a full survey of merging of mathematical models, AI algorithms and the chemistry and all combine to facilitate the discovery of materials on digital platforms. Amongst the significant topics which have been described, there are mathematical material model, optimization and inverse design, machine learning and physics-inspired model, digital twin and closed-loop synthesis platforms. The emergent uses of functional materials, nanomaterials, polymers, and sustainable materials are discussed and the existing challenges and future directions identified. The review is aimed at crossing the disciplinary boundaries and providing a coherent roadmap to the next-generation intelligent materials discovery.