Conference Logo ProgramMaster Logo
Conference Tools for Materials Science & Technology 2020
Login
Register as a New User
Help
Submit An Abstract
Propose A Symposium
Presenter/Author Tools
Organizer/Editor Tools

About this Abstract
Meeting Materials Science & Technology 2020
Symposium Machine Learning for Discovery of Structure-Process-Property Relations in Electronic Materials
Presentation Title Introductory Comments: Machine Learning for Discovery of Structure-Process-Property Relations in Electronic Materials
Author(s) B. Reeja Jayan
On-Site Speaker (Planned) B. Reeja Jayan
Abstract Scope Introductory Comments

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

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
Questions about ProgramMaster? Contact programming@programmaster.org | TMS Privacy Policy | Accessibility Statement