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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.

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

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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

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Topology Optimization for Two-phase Composites Using Active Learning Based Gaussian Process Regression

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