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Meeting MS&T21: Materials Science & Technology
Symposium Accelerating Materials Science with Big Data and Machine Learning
Presentation Title Topology Optimization for Two-phase Composites Using Active Learning Based Gaussian Process Regression
Author(s) Tanu Pittie, Suresh Bishnoi, N. M. Anoop Krishnan
On-Site Speaker (Planned) Tanu Pittie
Abstract Scope Designing new composite materials is a challenging problem consisting of composition and property prediction, and structure optimization. Recently, machine learning (ML) has emerged as a promising solution for developing design optimization algorithms. However, extensive input data, which is computationally and experimentally expensive, is required to train a reliable ML model for composites. Herein, we use active learning to develop a robust design process for target-specific two-phase composites using sparse data. Finite element analysis is carried out on a handful of randomly generated microstructures to establish the ‘ground truth’. Active learning is used to train a Gaussian process regression model with selective points from the sample space to predict the effective elastic modulus and fracture energy. Promising results close to the ground truth are obtained using very sparse data. It is also shown that the model can learn underlying physics appropriately and hence, can be used for topology optimization of composites.
Proceedings Inclusion? Undecided

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Characterization of Microscopic Deformation of Materials Using Deep Learning Methods
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Deep Learning-enabled Prediction of Mechanical Properties of Metallic Microlattice Structures Using Uniaxial Compression Videos
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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
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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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