2025 MRS Fall Meeting & Exhibit
Symposium MT05-Informed Synthesis of Materials—Data-Driven and In Situ Approaches
Advanced functional materials with optimized properties are critical for next generation applications in energy, sustainability, microelectronics, medicine, and quantum technology. Recently, rapid progress in computational methods has enabled prediction of numerous novel compounds with exceptional functional properties, but such compounds have often gone unrealized owing to missing knowledge regarding their synthesizability. On the experimental side, traditional materials synthesis has generally operated through an “in the dark” ex situ trial-and-error approach. While this approach has yielded tremendous successes in materials development, efficient and sustainable synthesis of new materials with a clear path to scale-up for manufacturing is desired. This symposium will bring together experts in computational materials discovery, design, and synthesis with experimentalists that leverage in situ “panoramic” synthetic approaches to promote a “science of synthesis” feedback loop necessary for future advanced materials development. Research topics of emphasis will include data-driven materials design and discovery, in situ “panoramic” synthesis and characterization approaches, and machine learning methods for materials synthesis. This symposium will provide a platform for discussing the advantages and challenges of synergistic computational-experimental approaches for materials discovery and synthesis, and so we especially encourage contributions that integrate experimental and theoretical or data-driven methods.
Topics will include:
- Data-driven materials design and discovery
- Machine learning methods for materials synthesis
- Calculated phase diagrams
- Panoramic synthesis
- In situ reaction characterization
- Precursor and intermediate roles in synthetic pathways
- Synthetic solid state chemistry
- High throughput, combinatorial synthesis approaches
Invited Speakers:
- Simon Billinge (Columbia University and Brookhaven National Laboratory, USA)
- Gerbrand Ceder (University of California, Berkeley, USA)
- Julia Chan (Baylor University, USA)
- Karena Chapman (Stony Brook University, The State University of New York, USA)
- Kamal Chowdhury (National Institute of Standards and Technology, USA)
- Janine George (Federal Institute for Materials Research and Testing, Germany)
- Yong-Jie Hu (Drexel University, USA)
- Mercouri Kanatzidis (Northwestern University, USA)
- Kirill Kovnir (Iowa State University of Science and Technology, USA)
- Arun Mannodi Kanakkithodi (Purdue University, USA)
- Katherine Page (The University of Tennessee, Knoxville, USA)
- Robert Palgrave (University College London, United Kingdom)
- David Portehault (Sorbonne Universite, France)
- Matthew Rosseinsky (University of Liverpool, United Kingdom)
- Wenhao Sun (University of Michigan, USA)
- Chris Wolverton (Northwestern University, USA)
- Andriy Zakutayev (National Renewable Energy Laboratory, USA)
Symposium Organizers
Jill Wenderott
Drexel University
Materials Science and Engineering
USA
Ricardo Grau-Crespo
University of Reading
Chemistry
United Kingdom
Koushik Pal
Indian Institute of Technology Kanpur
Physics
India
Julia Zaikina
Iowa State University of Science and Technology
Chemistry
USA
Topics
combinatorial
in situ
informatics
machine learning
neutron scattering
phase equilibria
phase transformation
x-ray diffraction (XRD)