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Meeting	MS&T22: Materials Science & Technology
Symposium	Uncertainty Quantification in Data-Driven Materials and Process Design
Presentation Title	Data-driven Structure-property Mapping in Small Data Regime: Towards Increasing Generalizability
Author(s)	Baskar Ganapathysubramanian, Hao Liu, Nirmal Baishnab, Balaji Pokuri, Olga Wodo
On-Site Speaker (Planned)	Hao Liu
Abstract Scope	Data-driven approaches become the integral approach to establishing reliable microstructure-property mappings. However, materials science datasets are typically small, or the property evaluations are computationally or experimentally demanding, and this requires approaches that integrate the small datasets or seek smart sampling strategies. However, the generalizability of such models beyond one data set remains to be understood. We utilize the problem of constructing structure-property (SP) models for organic photovoltaics applications (OPV) to understand data-driven SP models. This study explores the following questions: Given a few datasets with distinct microstructure annotated with the short circuit current: Can one derive a transferrable model only from a specific microstructure type and use it in another kind. Will the salient features in individual models be consistent across the independent datasets? And how sensitive are the models to the amount of data used to construct the generalizable model?

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Anisotropic Creep Modeling and Uncertainty Quantification of an Electron Beam Melted AM Ni-Based Superalloy

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Data-driven Modeling and Control for Temperature-controlled Shear Assisted Processing and Extrusion (ShAPE) using Koopman Operators

Data-driven Structure-property Mapping in Small Data Regime: Towards Increasing Generalizability

Efficient Phase Diagram Determination via Sequential Learning

Enabling the Fourth Paradigm of Multiscale ICME Models through Versatile Gaussian Process and Bayesian Optimization

Learning from Multi-source Scarce Data via Latent Map Gaussian Processes

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Neural Network Surrogate Predictions with Uncertainties for Materials Science

Quantifying Uncertainty in Atomistic Exploration

Solving Stochastic Inverse Problems for Property–structure Linkages Using Data-consistent Inversion and Machine Learning

Thermodynamic Modeling with Uncertainty Quantification and its Implications for Intermetallic Catalysts Design: Application to Pd-Zn-Based Gamma-Brass Phase

Uncertainty Quantification of a High-throughput Local Plasticity Test: Profilometry-based Indentation Plastometry of Al 7075 T6 Alloy

Uncertainty Quantification of Constitutive Models in Crystal Plasticity Finite Element Method

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