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Meeting MS&T21: Materials Science & Technology
Symposium Accelerating Materials Science with Big Data and Machine Learning
Presentation Title Accelerating Discovery in Computational Materials Science Using CAMD
Author(s) Joseph Montoya
On-Site Speaker (Planned) Joseph Montoya
Abstract Scope Artificial intelligence and machine learning are enabling automation of decision-making in various scientific domains, but still face a number of fundamental obstacles in materials science. We provide an overview of one such platform, Computational Autonomy for Materials Discovery (CAMD), designed to help materials scientists simulate and design their discovery processes using machine learning tools. CAMD has specifically been engineered to maximize the likelihood that sequential iterations of an experimental or simulation-based workflow will produce materials data with target properties. To date, CAMD's primary application is in the prediction of new, phase-stable crystal structures from structural prototypes in various chemical spaces. In addition, we have begun designing multi-fidelity sequential learning agents using data streams from experiment and theory. We review these capabilities with a view towards the future of AI-assisted tools for materials discovery.
Proceedings Inclusion? Undecided

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

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

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