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Meeting 2021 TMS Annual Meeting & Exhibition
Symposium AI/Data Informatics: Applications and Uncertainty Quantification at Atomistics and Mesoscales
Presentation Title Mining Structure-property Linkages in Nonporous Materials Using Interpretative Deep Learning Approach
Author(s) Haomin Liu, Niaz Abdolrahim
On-Site Speaker (Planned) Haomin Liu
Abstract Scope Relating the role of the microstructure to mechanical properties of nanoporous (NP) materials is a complicated problem. Deep Learning methods have shown strong performance in the mechanical design of materials by providing high learning capability. In this study, a deep learning approach is designed to model an elastic homogenization structure-property relationship of NP materials. Our model predicts the stiffness of the NP structure with a wide range of microstructures while exhibiting high accuracy and low computational cost. The main drawback of deep learning algorithms is poor interpretability. Thus, a novel interpretation method is developed to unravel the salient features of the microstructure that lead to better stiffness. Our interpretation method identifies the effective microstructure parameters that strongly impact stiffness, including surface area, dangling ligaments, and connectivity. Our interpretative deep learning framework can be transferred to build other structure-property relationships such as chemo-mechanical properties of nanomaterials in the future.
Proceedings Inclusion? Planned:

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

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Harnessing Materials Data and Simulation Capabilities for the Accelerated Discovery of Photocathode Materials
Inverse Design of Energy Storage Materials via Active Learning
Machine Learning Approach of Molecular Dynamics Simulations for Body-Centered Cubic Zirconium
Machine Learning for Predicting Grain Boundary Properties
Machine Learning Guided Discovery of Novel Oxide Perovskites for Scintillator Applications
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Microstructure-driven Parameter Calibration for Mesoscale Simulation
Mining Structure-property Linkages in Nonporous Materials Using Interpretative Deep Learning Approach
Model Comparison and Uncertainty Prediction for ML Models of Crystalline Solids Material Properties
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Quantifying RAMPAGE Interatomic Potentials for Metal Alloys
Simultaneous Development and Robust Optimization of a Microstructure Dependent Material Model: Leveraging Sequential Monte-Carlo Methods to Enhance Symbolic Regression Analysis
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