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Meeting Materials Science & Technology 2020
Symposium Machine Learning for Discovery of Structure-Process-Property Relations in Electronic Materials
Sponsorship ACerS Electronics Division
Organizer(s) B. Reeja Jayan, Carnegie Mellon University
Aarti Singh, Carnegie Mellon University
Scope There is an expanding list of opportunities for employing statistics, machine learning and neural networks, together referred to as Deep Learning, for the discovery of new materials. This symposium will bring together a diverse collection of researchers working who are employing these emerging computational tools for materials like semiconductors and for applications like electronics, memory, and energy devices. Primary focus will be on discussing how such tools can enhance synthesis, characterization, and device fabrication, testing processes. In 2020, MS&T will come back to Pittsburgh which is home to Carnegie Mellon's top Department of Machine Learning in the School of Computer Science. In addition to speakers from the materials and electronics communities, we will have guest speakers from Carnegie Mellon who specialize in design of experiments, optimizations, as well as novel algorithm design especially for working with small datasets like the ones seen in many materials science experiments).
Abstracts Due 05/31/2020
Proceedings Plan Planned: Publication outside of MS&T

3D Printing and Machine Learning
Cycle Life Prediction of Lithium Ion Batteries Based on Data Driven Methods
Expert-guided Learning for Data-constrained Materials Science Problems
Fast and Generalizable Detailed Router Using Attention-based Reinforcement Learning
Introductory Comments: Machine Learning for Discovery of Structure-Process-Property Relations in Electronic Materials
Neural Network Potential for Lattice Dynamics Calculations and Thermal Conductivity Prediction
Parametric Analysis to Quantify Process Input Influence on the Printed Densities of Binder Jetted Alumina Ceramics
SimuLearn: Machine Learning-empowered Fast and Accurate Simulator to Support 4D Printing Design
Uncertainty Quantification and Active Learning of Neural Network Models for Predicting ZrO2 Crystal Energy

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