Topics
Advancing Materials Science Through Data Science
We provide 13 tracks within Material Science to reflect the diverse opportunities and approaches in cross-disciplinary research and innovation. This session aims to highlight state-of-the-art methodologies, technologies, and applications at the intersection of these fields with data science, as an equal partner. We seek to attract contributions that demonstrate how data analytics and AI/ML can unveil new insights, predict material properties, and accelerate materials science as a whole.
AI/ML in Predictive Modeling for Materials Discovery: Techniques for predicting material properties and discovering new materials relying on AI/ML and data.
Data-Driven Characterization of Materials: Approaches for analyzing and interpreting data from experimental, computational, and theoretical studies to characterize materials at various scales.
High-Throughput Computational Materials Science: Strategies and tools for automating the computational design and screening of materials.
Big Data Analytics in Materials Science Data: Methods for managing, analyzing, and extracting insights from large datasets in materials science, including databases of materials properties and simulation results.
Integration of Experimental and Computational Data: Techniques for combining data from experimental measurements and computational models to enhance materials design and discovery.
Data Science for Small Data: Most experiments produce very small data and most traditional AI/ML techiques, like deep learning, target very large corpuses. In this novel section, we will focus how small data can be leveraged to produce big insights.
Quantum Computing for Materials Science: Exploration of how quantum computing can be utilized for materials simulation, discovery, and optimization.
Making Use and Participating in the Materials Genome Initiative and Databases: The MGI is a federal and multi-agency effort to bring the success of the Human Genome Project through its open-source and community approach to materials science.
AI/ML for Smart Manufacturing and Materials Engineering: Use of AI/ML in the development of smart manufacturing processes and the engineering of new materials.
Sustainability and Eco-friendly Materials Discovery: Data science applications in discovering and optimizing materials for sustainability, including renewable energy materials, recycling, and circular economy strategies.
Nanomaterials and 2D Materials: Data-driven approaches for the design, characterization, and application of nanomaterials and two-dimensional materials.
Interoperability and Standards in Materials Data: Issues and solutions related to data sharing, interoperability, and standardization in the materials science community.
Case Studies and Applications: Real-world applications and case studies where data science has significantly contributed to advances in materials science, including energy, aerospace, automotive, and biomedical sectors.