Physics-Based Machine Learning Parametrization of Force Fields with in-situ TEM Experimental Validation

Project: Research project

Project Details

Description

Families of newly synthesize 2D materials are emerging with numerous applications including battery electrodes, catalysts, sensors, pollution treatments, electro-magnetic shields, and structural materials. However, knowledge of their mechanical properties, integrity, and durability is limited. This is the case because current progress in force field development, for atomistic modeling, falls noticeably behind that of the synthesis of new 2D materials. A larger gap, in the discovery process, exists in terms of mechanical characterization using nanoscale experimentation. In this project we propose to address both shortcoming by (1) establishing a physics-based machine learning (ML) framework to select and parametrize force fields, (2) develop novel in situ HRTEM experiments (from RT to 500oC), and (3) explore the deformation and failure, including fracture, of a MXene material, Ti3C2Tx, as a case study. We hypothesize that existing force fields, such as REBO, Tersoff, and ReaxFF are inaccurate when applied to mechanical properties of 2D materials, involving large deformations, e.g., in bending or at crack tips, because their parameterization has not been optimized for such atomic configurations. We propose to establish ML frameworks, requiring much less domain knowledge and prior experience in force field development, to parametrize these interatomic potentials using ab initio equilibrium and non-equilibrium data as ground truth. We will use genetic algorithms in the optimization step and evaluate accuracy using correlation and principal component analyses. These analyses will not only identify correlation relationships between properties and their redundancy but also provide information to guide the selection of training and screening properties, as well as to quantify the parameterization flexibility of selected interatomic potentials. To validate the parameterized force fields, we will establish in situ TEM experiments using
StatusActive
Effective start/end date5/1/224/30/26

Funding

  • Office of Naval Research (N00014-22-1-2133)

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