Kedar Hippalgaonkar
Institute of Materials Research and Engineering, Agency for Science Technology and Research Singapore
Materials Science and Engineering, Nanyang Technological University Singapore
EXTENDED ABSTRACT: A combination of AI, high-throughput experiments (robotics) and high performance simulations can be used to accelerated materials development. I will first start by describing our efforts at lab-to-tech, where our technologies have been instrumental in successful formulation discovery, optimization as well as commercialization(both with industry partners and through our startup, Xinterra Inc.) In order to leverage these tools further, the ultimate goal is to achieve AI-driven materials discovery. Here, an important principle is that structure determines property. Therefore, property-driven generative design, driven by machine learning, critically requires understanding of the structure of materials. A deep understanding of crystal structures and their symmetries is essential for accurate invertible feature representations of materials. Next, the development of physics-aware generative models becomes critical to ensure target-oriented learning branches. Further, generated crystal structures require validation, both computationally and experimentally. After validation, the challenge is the experimental synthesis of these materials, which must be paired with data-driven characterization techniques to assess their properties. This is challenging due to the lack of a general method to rapidly synthesize and (optimally) dope bulk materials. In my work, I will discuss the how all of these come together and how we address a key bottleneck by the invention of a rapid self-sintered solid-state synthesis technique (tested on GeTe, Copper, Silver Antimony Telluride), achieving phase-pure crystalline materials synthesized in the milligram scale in as little as 15 seconds. This accelerates the solid-state reaction process by a factor of> 100 relative to the traditional route of mix-and-bake.
Keywords: AI, Robotics, Generative Design, Thermoelectrics, Materials-by-design
Nanyang Assistant Professor Kedar Hippalgaonkar is a NRF Fellow (Class of 2021) and a joint appointee with the Materials Science and Engineering Department at Nanyang Technological University(NTU) and as a Senior Scientist at the Institute of Materials Research and Engineering (IMRE) at the Agency for Science Technology and Research (A*STAR). He is leading the Accelerated Materials Development for Manufacturing (AMDM) program from 2018-2023 focusing on the development of new materials, processes and optimization using Machine Learning, AI and high-throughput computations and experiments in electronic and plasmonic materials and polymers. He was also leading the Pharos Program on Hybrid (inorganic-organic) thermoelectrics for ambient applications from 2016-2020.He has published over 70 research papers, has co-founded a startup (Xinterra, Inc.), won the MOE START Award in 2021 and was nominated as a Journal of Materials Chemistry Emerging Investigator in 2019. He was recognized as a Science and Technology for Society Young Leader in Kyoto in 2015. For his outstanding graduate research, he was awarded the Materials Research Society Silver Medal in 2014. Funded through the A *STAR National Science Scholarships, he graduated with a Bachelor of Science (Distinction) from the Department of Mechanical Engineering at Purdue University in 2003 and obtained his Doctor of Philosophy from the Department of Mechanical Engineering at UC Berkeley in 2014. While pursuing his doctoral studies, he conducted research on fundamentals of heat, charge and light in solid state materials.