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The purpose of this work is to lower the computational cost of predicting charge mobilities in organic semiconductors, which will benefit the screening of candidates for inexpensive solar power generation. We characterize efforts to minimize the number of expensive quantum chemical calculations we perform by training machines to predict electronic couplings between monomers of poly‐(3‐hexylthiophene). We test five machine learning techniques and identify random forests as the most accurate, information‐dense, and easy‐to‐implement approach for this problem, achieving mean‐absolute‐error of 0.02 [× 1.6 × 10⁻¹⁹J],R²= 0.986, predicting electronic couplings 390 times faster than quantum chemical calculations, and informing zero‐field hole mobilities within 5% of prior work. We discuss strategies for identifying small effective training sets. In sum, we demonstrate an example problem where machine learning techniques provide an effective reduction in computational costs while helping to understand underlying structure–property relationships in a materials system with broad applicability.

 

The objective of this work is to predict the morphology and material properties of crosslinking polymers used in aerospace applications. We extend the open-source dybond plugin for HOOMD-Blue to implement a new coarse-grained model of reacting epoxy thermosets and use the 44DDS/DGEBA/PES system as a case study for calibration and validation. We parameterize the coarse-grained model from atomistic solubility data, calibrate reaction dynamics against experiments, and check for size-dependent artifacts. We validate model predictions by comparing glass transition temperatures measurements at arbitrary degree of cure, gel-points, and morphology predictions against experiments. We demonstrate for the first time in molecular simulations the cure-path dependence of toughened thermoset morphologies. 

The predictive capabilities of computational materials science today derive from overlapping advances in simulation tools, modeling techniques, and best practices. We outline this ecosystem of molecular simulations by explaining how important contributions in each of these areas have fed into each other. The combined output of these tools, techniques, and practices is the ability for researchers to advance understanding by efficiently combining simple models with powerful software. As specific examples, we show how the prediction of organic photovoltaic morphologies have improved by orders of magnitude over the last decade, and how the processing of reacting epoxy thermosets can now be investigated with million-particle models. We discuss these two materials systems and the training of materials simulators through the lens of cognitive load theory. For students, the broad view of ecosystem components should facilitate understanding how the key parts relate to each other first, followed by targeted exploration. In this way, the paper is organized in loose analogy to a coarse-grained model: The main components provide basic framing and accelerated sampling from which deeper research is better contextualized. For mentors, this paper is organized to provide a snapshot in time of the current simulation ecosystem and an on-ramp for simulation experts into the literature on pedagogical practice. 

Evaluating new, promising organic molecules to make next-generation organic optoelectronic devices necessitates the evaluation of charge carrier transport performance through the semi-conducting medium. In this work, we utilize quantum chemical calculations (QCC) and kinetic Monte Carlo (KMC) simulations to predict the zero-field hole mobilities of ∼100 morphologies of the benchmark polymer poly(3-hexylthiophene), with varying simulation volume, structural order, and chain-length polydispersity. Morphologies with monodisperse chains were generated previously using an optimized molecular dynamics force-field and represent a spectrum of nanostructured order. We discover that a combined consideration of backbone clustering and system-wide disorder arising from side-chain conformations are correlated with hole mobility. Furthermore, we show that strongly interconnected thiophene backbones are required for efficient charge transport. This definitively shows the role “tie-chains” play in enabling mobile charges in P3HT. By marrying QCC and KMC over multiple length- and time-scales, we demonstrate that it is now possible to routinely probe the relationship between molecular nanostructure and device performance.