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Meeting MS&T21: Materials Science & Technology
Symposium AI for Big Data Problems in Advanced Imaging, Materials Modeling and Automated Synthesis
Presentation Title Discovery of Novel Crystal Structures via Generative Adversarial Networks
Author(s) Taylor D. Sparks, Michael Alverson
On-Site Speaker (Planned) Taylor D. Sparks
Abstract Scope The idea of material discovery has excited and perplexed scientists for centuries. Several methods have been employed to find new types of materials, ranging from replacement of atoms in a crystal structure to advanced machine learning methods for predicting entirely new crystal structures. In this work, we investigate the performance of various Generative Adversarial Network (GAN) architectures to find innovate ways of generating theoretical crystal structures that are synthesizable and stable. Over 300,000 entries from Pearson’s Crystal Database are used for the training of each GAN. The space group number, atomic positions, and lattice parameters are parsed and used to construct an input tensor for each of the network architectures. Several different GAN layer configurations are designed and analyzed, including Wasserstein GANs with weight clipping and gradient penalty, in order to identify a model that can adequately discern symmetry patterns that are present in known material crystal structures.
Proceedings Inclusion? Undecided

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

A Deep Generative Model for Parametric EBSD Pattern Simulation
Aluminum Alloy Design Using Physics Informed Machine Learning
De Novo Inverse Design of Nanoporous Materials by Machine Learning
Deep Learning and Uncertainty Quantification for Automated Experiments
Discovery of Novel Crystal Structures via Generative Adversarial Networks
Improving EBM NIR Image Analysis for Component Qualification a Statistical Learning Approach
Machine-learning Based Algorithms for 4D X-ray Microtomographic Analysis
Machine Learning for Automated Experiment in Scanning Probe and Electron Microscopy
Machine Learning Polymer Property Prediction Models with Polymers Represented as Natural Language
Non-iterative Deep Learning for High-fidelity Microscopic Tomography
Optimizing the Training of Convolutional Neural Networks for Image Segmentation
Prediction of Dynamic Properties of LiF and FLiBe Molten Salts with DeepPot Network Potentials
Refinements to the Production of Machine Learning Interatomic Potentials
Semantic Segmentation of Porosity in In-situ X-ray Tomography Data Using FCNs
Tuning Optoelectronic Properties of Semiconductors with First Principles Modeling and Machine Learning
Understanding the Composition–property Relationship of Glasses Using Interpretable Machine Learning

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