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About this Abstract
Meeting	MS&T21: Materials Science & Technology
Symposium	Accelerating Materials Science with Big Data and Machine Learning
Presentation Title	Data Science as Bridge – Materials Characterization and Modeling
Author(s)	Maria Chan
On-Site Speaker (Planned)	Maria Chan
Abstract Scope	In materials and chemical science, the combination of high throughput computational modeling and experimentation has given rise to significant challenges and opportunities. Data science techniques such as machine learning, artificial intelligence, and computer vision have made a significant impact in the ease, scope, and speed of understanding of known materials and discovery of new ones. In this talk, we will discuss how we use data science approaches in conjunction with theory-based modeling to interpret experimental characterization data (such as x-ray scattering, spectroscopy, scanning probe microscopy, and electron microscopy) and carry out materials design (such as in the space of classic and hybrid perovskite optoelectronic materials). The role of computer vision and pattern recognition in the analysis of microscopy data will also be discussed. In addition, we will discuss computer vision and natural language processing approaches to gather and harness microscopy data from scientific literature.

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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