Academic Profile : Faculty

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Assoc Prof Kedar Hippalgaonkar
Associate Professor, School of Materials Science & Engineering
Associate Professor Kedar Hippalgaonkar’s research interests are in AI-driven solid-state materials-by-design. He holds a joint appointment as an associate professor with the Materials Science and Engineering Department at NTU, and as a Principal Scientist at IMRE, A*STAR. He is also the Scientific Director of the Multi-PI S$25 Million Accelerated Materials Development for Manufacturing (AMDM) program from 2018 – 2024. Leading a group of >30 members, he has demonstrated clear areas of advancement in the discovery of new functional materials, AI and robotics for accelerated materials discovery, and advancing fundamental knowledge in inequilibrium charge and phonon scattering. His scientific contributions in the materials-by-design space have established a framework for the rapid discovery of materials and new physics, which is now being utilised globally in data-driven research. His commitment to translating scientific research into tangible real-world applications is exemplified by his role as the Co-founder and Senior Scientific Advisor of a startup – Xinterra, Inc. As a contributing member of the newly established Acceleration Consortium at the University of Toronto, Kedar collaborates with an international community of scientists dedicated to the creation of materials acceleration platforms. These platforms are pivotal in unlocking new discoveries in molecules and materials, further expanding the horizon of scientific understanding.
Associate Professor Kedar Hippalgaonkar’s interests are in designing functional materials, especially for energy applications. He has fundamental knowledge in solid state physics, 1D (nanowires), 2D (TMDCs) as well as inorganic-organic (hybrid) materials. His approach to materials by design is built on creating and utilizing materials data by high-performance computing and high-throughput experiments to synthesize and characterize materials for optical and electronic properties. Specifically, he is leading projects on the application of high-throughput experimentation, optimization and machine learning on industry and academic projects on batteries, thermoelectrics and catalysis. In addition, he is interested in the use of material descriptors, machine learning and data science for materials discovery. His background is in transport properties of materials specifically in understanding their thermal, optical and thermoelectric properties. He is keen on developing tools such as process optimization, design of experiments and materials and process fingerprinting from materials development to device applications.
  • "Design Beyond What You Know”: Material-Informed Differential : Generative AI (MIDGAI) for Light-Weight High-Entropy Alloys and Multi- functional Composites (Stage 1a)
  • D4Thermo: Database of Defects and Dopants through DFT simulations for high-performing Thermoelectrics
  • Development and Optimization of Ternary Non-Fullerene Acceptors
  • Ferroelectric Aluminum Scandium Nitride (Al1-xScxN) Thin Films and Devices for mm-Wave and Edge Computing
  • Foundational Research Capabilities (FRC) Study on AI for Science
  • Intercalated 2D Materials for Enhanced Thermoelectric Transport
  • Learning lineage of physics-informed predictive models of complex processes for knowledge distillation and novel design in material and quantum sciences
  • Memristive Halide Perovskites for Next Generation Embedded Neuromorphic Computing
  • MS0003-LLM: A Generative Chatbot for Personalised Learning in Data Science and Artificial Intelligence
  • Rational design of halide perovskite-based quantum dots for photonic applications (DesperQD)
  • Self driven materials discovery
  • Self-Assembly and Transient Laser Heating Experiments of Ordered Mesoporous High-Entropy Metals (STEM2) as Efficient Stable Electrocatalysts
  • Theory guided Accelerated Discovery of printable P-type transparent conductors
  • Thermoelectric Materials by Design: Understanding and Extrapolating from Non-Equilibrium Charge and Heat Transport
  • WP5a:"Design Beyond What You Know”: Material-Informed Differential : Generative AI (MIDGAI) for Light-Weight High-Entropy Alloys and Multi- functional Composites (Stage 1a)