ProgramMaster Logo
Conference Tools for MS&T21: Materials Science & Technology
Login
Register as a New User
Help
Submit An Abstract
Propose A Symposium
Presenter/Author Tools
Organizer/Editor Tools
About this Symposium
Meeting MS&T21: Materials Science & Technology
Symposium Accelerating Materials Science with Big Data and Machine Learning
Sponsorship
Organizer(s) Huan Tran, Georgia Institute Of Technology
Muratahan Aykol, Toyota Research Institute
Scope Materials informatics is gaining significant momentum as a new subfield of materials science and engineering, especially from the launch of Materials Genome Initiative almost a decade ago. From alloys to polymers to ceramics, a central tenet of this new approach is that possible relationships among structure, property and/or processing may be learned from data, which enable large-scale screening of untested candidates or rapid optimization of materials for a target technology. Remarkably, this strategy has led to the development of many novel materials recently synthesized and tested. Within this context, essential topics concerning materials informatics and data-driven research, including experimental and computational generation of materials data, data management and dissemination, material representations, machine learning algorithms and other predictive models for materials, closed-loop and inverse design approaches, as well as infrastructure and software tool development are all covered in this symposium. We expect this symposium to provide a forum where researchers from academia, industry, and national laboratories share recent developments in diverse application areas of materials informatics, and identify the critical areas where future research efforts should be directed to.
Abstracts Due 04/15/2021
Proceedings Plan Undecided
PRESENTATIONS APPROVED FOR THIS SYMPOSIUM INCLUDE

A Data-driven Simulator for High-throughput Prediction of Electromigration-mediated Damage in Polycrystalline Interconnects
Accelerating Discovery in Computational Materials Science Using CAMD
Bridging the Gap between Literature Data Extraction and Domain Specific Materials Informatics
Characterization of Microscopic Deformation of Materials Using Deep Learning Methods
Considerations for Interpretability, Reliability, And Data-efficiency in Machine Learning Properties of Solid-state Materials
Data Science as Bridge – Materials Characterization and Modeling
Deep Learning-enabled Prediction of Mechanical Properties of Metallic Microlattice Structures Using Uniaxial Compression Videos
Designing Alloys with Process-mapping AI Pre-trained on Empirical Knowledge
Developing Physics-based Descriptors for Property Prediction in Oxide Glasses
Learning Synthesis: Engineering Metal Nanoclusters for Specific Material Properties
Machine Learning in 2D Materials: Benchmarking Crystal Graph Based Convolutional Neural Network (CGCNN) for Open Databases
Machine Learning to Predict Mechanical Properties of Steel Alloys Based on Chemical Composition and Heat Treatment Process
Materials Graph Ontology for Improving the Standardization and Utilization of Materials Data
Molecular Dynamics Simulation Using Lagrangian Neural Networks
Multi-target Prediction of Concrete Engineering Properties Based on a Single Deep Learning Model
P3-18: Rashba Spin Splitting and Photocatalytic Properties of GeC−MSSe (M=Mo, W) Van Der Waals Heterostructures
P3-19: Thermo-mechanical Property Prediction of High-temperature Materials Using a Python Based Interface With Quantum Espresso
Predicting Glass Behaviour from Optical Microscopy Images Using Interpretable Machine Learning
Scalable Gaussian Processes for Predicting the Optical, Physical, Thermal, and Mechanical Properties of Inorganic Glasses Using Compositions for Large Datasets
Searching for New Ferroelectric Materials Browsing a High-throughput Phonon Database
Semantic Segmentation of Plasma Transferred Arc Additively Manufactured NiBSi-WC Optical Microscopy Images Using a Convolutional Neural Network
Slip Band Characterization with Microtensile Testing Using Digital Image Processing
There is No Time for Science as Usual
Topology Optimization for Two-phase Composites Using Active Learning Based Gaussian Process Regression


Questions about ProgramMaster? Contact programming@programmaster.org