Conference Tools for MS&T21: Materials Science & Technology

Help

About this Abstract
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.

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

Questions about ProgramMaster? Contact programming@programmaster.org