Symposium CH04-Accelerating Materials Characterization, Modeling, and Discovery by Physics-Informed Machine Learning

Machine learning methods are making rapid inroads into all fields of science, driven by data volumes, computational resources, and utility of the methods at finding correlations in high dimensional spaces. Both methods (algorithms) and infrastructure (combinatorial and high-throughput experimentation, high-performance computing, databases, data-management systems, workflows, repositories) are being developed to both create and tackle the large increase in data volume, facilitating the leap from single lab-based experiment-computation-output models to one where researchers can utilize available public materials data infrastructure, leading to new materials discoveries, enhanced predictive capabilities, and accelerated scientific understanding.

This symposium will bring together the latest advances in the development and application of machine learning and related data analytics methodologies to enhance the characterization of materials, extract relevant features for improving theory-experiment comparisons, and assist in solving inverse problems relevant to structural and functional characterization.Further, it will encompass robust uncertainty quantification approaches, such as the use of Bayesian methods for challenging characterization and prediction tasks and enabling automated experimentation, as well as discuss methods that extend beyond traditional correlative machine learning methods towards causal inference to better understand drivers of materials’ behaviors.

Topics will include:

High-throughput materials synthesis for the generation of large and consistent datasets
High-throughput characterization and computations for materials discovery
New techniques and methods enabled by machine learning approaches for probing and characterizing the structural, chemical and/or electronic nature of materials
Bridging computation and experimental data via machine learning and statistical methods, including solving inverse problems, feature extraction and selection, and materials design
Materials data infrastructure – databases, data-management systems, workflows and best practices for 21st century materials science
Causal inference and Bayesian models for incorporating prior information, model selection, and uncertainty quantification
Reinforcement learning and Gaussian process methods for automated experimentation, materials design, synthesis and characterization

Invited Speakers:

Alan Aspuru-Guzik (University of Toronto, Canada)
Keith Butler (Rutherford Appleton Laboratory, United Kingdom)
Stefano Curtarolo (Duke University, USA)
Claudia Draxl (Humboldt-Universität zu Berlin, Germany)
Nicola Ferrier (Argonne National Laboratory, USA)
Adam Forster (Aalto University, Finland)
John Gregoire (California Institute of Technology, USA)
Elizabeth Holm (Carnegie Mellon University, USA)
Sergei Kalinin (The University of Tennessee, Knoxville, USA)
Julia Ling (Citrine Informatics, USA)
Elsa Olivetti (Massachusetts Institute of Technology, USA)
Kristin Persson (University of California, Berkeley, USA)
Rampi Ramprasad (Georgia Institute of Technology, USA)
Matthias Scheffler (Fritz Haber Institute of the Max Planck Society, Germany)
Lenka Zdeborova (Commissariat à l’énergie atomique et aux énergies alternatives, France)