Speakers’ Abstracts

For centuries, researchers have sought out ways to connect disparate areas of knowledge. With the advent of Artificial Intelligence (AI), we can now rigorously explore relationships that span across distinct areas – such as, mechanics and biology, or science and art – to deepen our understanding, to accelerate innovation, and to drive scientific discovery. However, many existing AI methods have limitations when it comes to physical intuition, and often hallucinate. To address these challenges, we present research that blurs the boundary between physics-based and data-driven modeling through a series of physics-inspired multimodal graph-based generative AI models, set forth in a hierarchical multi-agent mixture-of-experts framework. The design of these models follows a biologically inspired approach where we re-use neural structures and dynamically arrange them in different patterns and utility, implementing a manifestation of the universality-diversity-principle that forms a powerful principle in bioinspired materials. This new generation of models is applied to the analysis and design of materials, specifically to mimic and improve upon biological materials. Applied specifically to protein engineering, the talk will cover case studies covering distinct scales, from silk, to collagen, to biomineralized materials, as well as applications to medicine, food and agriculture where materials design is critical to achieve performance targets.

We have adopted methods to establish recommender systems useful for efficient discovery of currently unknown chemically relevant compositions (CRCs) of inorganic ionic compounds from vast candidates. Using information of compounds registered in inorganic crystal structure database (ICSD) and tensor-based recommender system, we systematically calculated recommendation scores for chemical compositions of up to 23 billion five-element ionic systems. The high success rate, despite not using any prior knowledge or first principles theoretical results, proves the powerfulness of current recommendation systems. Synthesis experiments were made at the chemical compositions with high recommendation scores. Novel oxides and nitrides were efficiently discovered accordingly. Recommender system was also used to predict successful processing conditions for new compounds based on our parallel experiment-dataset.

The first milestone of AI for Science is to create a comprehensive data platform. However, the development of foundational database in polymer material science has significantly lagged behind. To overcome this barrier, we have constructed a massive computational database encompassing a vast chemical space of polymers, using RadonPy, a fully automated pipeline tool for all-atom classical molecular dynamics and first-principles-based computational experiments. The foundation models pre-trained on this computational database have demonstrated scalable Sim2Real transfer in a wide range of real-world predictive tasks through fine-tuning. This presentation will discuss several aspects of scaling laws for Sim2Real transfer learning and their practical applications for the discovery of new polymers.

I will discuss the emerging need to massively use and deploy Artificial Intelligence (AI) techniques in materials and devices design and engineering, by overviewing our journey into the research of innovative advanced materials using AI in the field of interconnects technologies. I will point towards the revolution of fully describing as-grown materials with AI-driven reconstruction of digital models/twins with atomistic modelling, hence bringing the accuracy of first principles methods to unprecedented multimillions atoms models. This opens a tremendous capability to correlate in-depth atomistic-scale features of as-grown materials with resulting local and global chemical, physical and devices properties, offering reverse engineering and AI-driven co-piloting for industrial innovation strategies.

I will discuss the progress we have made towards building a new generation of AI-powered infrastructure for scientific research.