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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 Prediction of Dynamic Properties of LiF and FLiBe Molten Salts with DeepPot Network Potentials
Author(s) Alejandro Rodriguez, Hu Ming
On-Site Speaker (Planned) Alejandro Rodriguez
Abstract Scope Molten salts have attracted interest as a potential heat carrier and/or fuel dissolver in developments of new Gen IV reactor designs. Those containing lithium and fluoride-based compounds are of particular interest due to their affinity to lower melting points of mixtures and their compatibility with alloys. A molecular dynamics study is performed on two popular molten salts, namely LiF (50% Li) and FliBe (66% LiF - 33% BeF2), to predict properties, namely density, specific heat, thermal conductivity, and shear viscosity. Due to the large possibilities of atomic environments, we employ training using the Deep Potential (DeepPot) neural networks to learn from large DFT datasets of 118,115 structures with 70 atoms each for LiF and 222,903 structures with 91 atoms each for FLiBe molten salts. These networks are then deployed in fast molecular dynamics to predict dynamic properties that become apparent at longer time-scales, and are otherwise difficult to achieve with man-made potentials, ab-initio, or with experiments.
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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