Exploring Thermoset Fracture with a Quantum Chemically Accurate Model of Bond Scission

Abstract

A molecular understanding of thermoset fracture is crucial for enhancing the performance and durability across applications. However, achieving accurate atomistic modeling of thermoset fracture remains computationally prohibitive due to the high cost associated with quantum mechanical methods for describing bond breaking. In this work, we introduce an active learning (AL) framework for our recently developed machine learning-based adaptable bond topology (MLABT) model that uses data sets generated via density functional theory (DFT) calculations that are both minimalistic and informative. Employing MLABT integrated with AL and DFT, we explore fracture behavior in highly cross-linked thermosets, assessing the variations in fracture induced by system temperature, temperature fluctuations, strain rate, cooling rate, and degree of cross-linking. Notably, we discover that while fracture is minimally affected by temperature, it is strongly influenced by the strain rate. Furthermore, while the structural disparities introduced by different network annealing rates influence the elastic properties, they are inconsequential for thermoset fracture. In contrast, network topology emerges as the dominant determinant of fracture, influencing both the ultimate strain and stress. Particularly, MLABT with AL-DFT achieving near quantum-chemical bond breaking accuracy still leads to ductile failures, emphasizing the necessity of modeling polymer networks at larger length scales for bridging the gap between the experiment and simulation. Nevertheless, the integration of MLABT with the AL framework paves the way for efficient and DFT-accurate modeling of thermoset fracture, providing an affordable and accurate approach for calculating polymer network fracture across chemical space.

Accessing the electronic structure of liquid crystalline semiconductors with bottom-up electronic coarse-graining

“…Recently, data-driven approaches have emerged for the “bottom-up” prediction of electronic properties of soft materials at the CG resolution. 76–84 These electronic CG (ECG) models leverage ML to establish a mapping from AA electronic structure to CG representation, eliminating the complexities and computational costs associated with back-mapping processes and ad nauseam QC. A fundamental insight driving the development of ECG models is the recognition that a single CG configuration encompasses a range of AA configurations, resulting in a “one-to-many” mapping (Fig.…”