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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 Refinements to the Production of Machine Learning Interatomic Potentials
Author(s) Jared Stimac, Jeremy Mason
On-Site Speaker (Planned) Jared Stimac
Abstract Scope Machine learning potentials (MLP) have the potential to allow dramatically accelerated simulations of atomic systems with the accuracy of quantum mechanical techniques through the use of supervised regression algorithms. The price to be paid is that MLPs have a higher computational expense than empirical potentials, both during construction and for every evaluation of the potential energy. In pursuit of reducing these costs and alleviating the necessity for enormous datasets, our framework for producing MLPs combines an efficient implementation of a sparse Gaussian process algorithm with a novel set of descriptors for atomic environments. It is intended that the descriptors be an injective embedding that imposes minimal distortion and that the sparse Gaussian process selects as few inducing points—which dominate the computational complexity in all respects—as necessary. To this end, we aim to produce better performing potentials with less training and data than competing frameworks.
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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