Symposium CT05-Artificial Intelligence and Automation for Materials Design

Artificial intelligence (AI) is changing the way we discover, design and optimize materials for a broad range of applications in energy technology, biomedicine, climate adaptation and more sustainable built environments. However, the power and usefulness of AI strongly depends on the type, magnitude and quality of data it receives, and it is vulnerable to biases that are common at the cutting edge of materials science. Particularly when we do not always have “big data”. Thanks to decades of ongoing development of machine and deep learning methods in theoretical computer science, the challenge in AI-driven materials design has shifted from what analysis to do, to deciding what data to collect and how to represent materials in machine friendly formats. Materials manufacturing has also reached the stage where AI is driving process, coupling data analysis with automated synthesis or simulation, and the creation of digital twins. This symposium will bring together artificial intelligence methods including machine learning, deep learning, computer vision, optimization and language processing and automation methods including sensing, process control, high-throughput sampling and robotics. Automated control and monitoring of synthesis and processing using AI and internet of things (IoT) technologies is essential for scaling the development of new materials to industrial levels, just as the creating of realistic and comprehensive digital twins has become the goal of computational materials modelling. This symposium is not restricted to a specific class of materials, or application domain, since digital technologies can be repurposed and re-applied across discipline boundaries to accelerate impact.

Topics will include:

Classification and screening of materials for advanced applications
Data-driven materials informatics and machine learning
Computer vision and deep learning for structure/property relationships
Automation of materials synthetic and simulations, including high-throughput systems and robotics
Process modelling and monitoring, including sensing and internet of things
Automated characterisation and feature extraction of materials
Material digital twins
AI supported certification of materials
Nanoinformatics

Invited Speakers:

Muratahan Akyol (Toyota Research Institute, USA)
Nong Artrith (Columbia University, USA)
Alan Aspuru-Guzak (University of Toronto, Canada)
Mathieu Bauchy (University of California, Los Angeles, USA)
Carmen Constantinescu (Fraunhofer Institute for Industrial Engineering IAO, Germany)
Stefano Curtarolo (Duke University, USA)
Sanket Deshmukh (Virginia Tech, USA)
Claudia Draxl (Humboldt-Universität zu Berlin, Germany)
Alaa Elwany (Texas A&M University, USA)
Adalberto Fazzio (Universidade de São Paulo, Brazil)
Alejandro Franco (Université de Picardie Jules Verne, France)
Luca Ghiringhelli (Fritz-Haber-Institut der Max-Planck-Gesellschaft, Germany)
Brian Giera (Lawrence Livermore National Laboratory, USA)
Thomas Hammerschmidt (Ruhr-Universität Bochum, Germany)
Hendrick Heinz (University of Colorado Boulder, USA)
Elizabeth Holm (Carnegie Mellon University, USA)
Anubhav Jain (Lawrence Berkeley National Laboratory, USA)
Nick Kotov (University of Michigan, USA)
Nicola Marzari (École Polytechnique Fédérale de Lausanne, Switzerland)
Bryce Meredig (Citrine Informatics, USA)
Dane Morgan (University of Wisconsin–Madison, USA)
Elsa Olivetti (Massachusetts Institute of Technology, USA)
Amanda Parker (The Australian National University, Australia)
Kristin Persson (University of California, Berkeley, USA)
Paul Pigram (La Trobe University, Australia)
Rampi Ramprasad (Georgia Institute of Technology, USA)
Prahalada Rao (University of Nebraska–Lincoln, USA)
Kristofer Reyes (University at Buffalo, The State University of New York, USA)
Patrick Rinke (Aalto University, Finland)
Chris Wolverton (Northwestern University, USA)