Project Description
The rapid advancement of Artificial Intelligence (AI) and Materials Informatics has significantly transformed the process of computational materials discovery by enabling the prediction and generation of novel materials with desirable physicochemical properties. Modern AI-driven approaches such as Graph Neural Networks (GNNs), transformer architectures, generative diffusion models, and deep learning frameworks have demonstrated remarkable capabilities in predicting material properties, accelerating high-throughput screening, and identifying candidate structures for applications including energy storage, catalysis, environmental remediation, and electronic materials. However, despite these advancements, most existing computational material discovery systems operate primarily as black-box predictive models that lack semantic understanding, scientific reasoning capability, and explainability. These systems predominantly rely on numerical correlations extracted from large datasets without incorporating domain-specific scientific knowledge, synthesis constraints, causal relationships, or semantic interoperability. As a result, many generated candidate materials may be theoretically promising but scientifically infeasible, experimentally impractical, or inconsistent with known physical and chemical principles.
Simultaneously, Semantic Web technologies and ontology-based knowledge representation frameworks have emerged as powerful tools for modeling complex scientific domains through machine-understandable semantic structures. Ontologies enable formal representation of scientific entities, relationships, constraints, rules, and hierarchical knowledge, thereby facilitating semantic interoperability, knowledge reuse, logical reasoning, and explainable inference. In domains such as bioinformatics, healthcare, and industrial systems, ontology-driven frameworks have proven highly effective in integrating heterogeneous data sources and supporting intelligent decision-making. However, their application in computational materials discovery remains relatively unexplored, particularly in the context of integrating symbolic scientific reasoning with generative AI systems. Existing materials discovery frameworks rarely incorporate ontology-guided reasoning mechanisms capable of semantically validating generated materials, enforcing scientific constraints, or autonomously reasoning over material-property relationships.
To address these limitations, the proposed research introduces an ontology-guided autonomous materials discovery framework that integrates Materials Informatics, Semantic Web technologies, Knowledge Graphs, Generative AI, and Neuro-Symbolic Reasoning into a unified intelligent scientific discovery ecosystem. The central idea of the proposed framework is to combine data-driven AI models with symbolic scientific reasoning in order to create a semantically aware and explainable materials discovery platform. Unlike traditional black-box systems, the proposed approach introduces an ontology-driven semantic intelligence layer that guides the generation, validation, optimization, and interpretation of novel materials based on scientifically defined rules, constraints, and domain knowledge. The framework aims to transform current computational discovery pipelines into intelligent systems capable not only of predicting materials but also of understanding scientific relationships, reasoning about feasibility, and autonomously suggesting scientifically valid candidates.
Apply with Code: CBECSE014
