IMX Talks - Accelerating Development of Materials with Artificial Intelligence

The development of novel materials and manufacturing processes can be time consuming and expensive due to the costs of experiments and the complexity of hierarchical process-structure-property-performance (PSPP) relationships that are unique across materials classes (e.g., polymers, metals, ceramics). Artificial intelligence (AI)-driven materials design is particularly useful on 1) optimization exercises that are high-dimensional, 2) problems where PSPP cause-and-effect are unknown, and 3) projects with resource constraints. While a researcher using traditional methods typically estimate which inputs are the most important to vary and develop experiments accordingly, AI can systematically create machine learning models using all input combinations and then act as a copilot for the researcher, suggesting a series of experiments to explore the design space in the most efficient manner. The talk will introduce the field of Material Informatics and describe the state-of-the-art methodology developed by Citrine Informatics and used in the Citrine Platform, a cloud-based enterprise-level software that combines smart materials data infrastructure and AI. Citrine’s experience in materials development will be illustrated with case studies.
Bio: Dr. Marco Musto is a Senior Research Scientist at Citrine Informatics, where he focuses on publicly funded Material Informatics and digitalisation projects.
Before joining Citrine in 2022, Marco held a variety of industrial R&D roles spanning analytical modelling, numerical simulation and machine learning at Dunlop, Ansys, Osram, Element Materials Technology among others.
Dr. Musto earned his B.S in Aerospace Engineering from the University of Pisa in 2006, and his Ph.D. in Computational Mechanics at Brunel University West London in 2013,
where he focused on the application of fractional calculus to fracture mechanics. His industrial research covered diverse topics such as data-driven approaches to OLED devices' reliability estimation, optimization of adhesive interfaces and characterization of long-term degradation in polymeric materials.