Relating Chromophoric and Structural Disorder in Conjugated Polymers

The optoelectronic properties of amorphous conjugated polymers are sensitive to conformational disorder and spectroscopy provides the means for structural characterization of the fragments of the chain which interact with light - "chromophores". A faithful interpretation of spectroscopic conformational signatures, however, presents a key challenge. We investigate the relationship between the ground state optical gaps, the properties of the excited states, and the structural features of chromophores of a single molecule poly(3-hexyl)-thiophene (P3HT), using quantum-classical atomistic simulations. Our results demonstrate that chromophoric disorder reflects an interplay between excited state de-localization and electron-hole polarization, and is controlled by torsional disorder that is specifically associated with the presence of side chains. Within this conceptual framework, we predict and explain a counter-intuitive spectral signature of P3HT: a red-shifted absorption, despite shortening of chromophores, with increasing temperature

Solving the Wigner Equation for Chemically Relevant Scenarios: Dynamics in 2D

Signed Particle Monte Carlo (SPMC) approach has been used in the past to model steady-state and transient dynamics of the Wigner quasi-distribution for electrons in low dimensional semiconductors. Here we make a step towards high-dimensional quantum phase-space simulation in chemically relevant scenarios by improving the stability and memory demands of SPMC in 2D. We do so by using an unbiased propagator for SPMC to improve trajectory stability and by applying machine learning to reduce memory demands for storage and manipulation of the Wigner potential. We perform computational experiments on a 2D double-well toymodel of proton transfer and demonstrate stable pico-second-long trajectories that require only a modest computational effort

Simulations of Disordered Matter in 3D with the Morphological Autoregressive Protocol (MAP) and Convolutional Neural Networks

Disordered molecular systems such as amorphous catalysts, organic thin films, electrolyte solutions, and water are at the cutting edge of computational exploration today. Traditional simulations of such systems at length-scales relevant to experiments in practice require a compromise between model accuracy and quality of sampling. To remedy the situation, we have developed an approach based on generative machine learning called the Morphological Autoregressive Protocol (MAP) which provides computational access to mesoscale disordered molecular configurations at linear cost at generation for materials in which structural correlations decay sufficiently rapidly. The algorithm is implemented using an augmented PixelCNN deep learning architecture that we previously demonstrated produces excellent results in 2 dimensions (2D) for mono-elemental molecular systems. Here, we extend our implementation to multielemental 3D and demonstrate performance using water as our test system in two scenarios: 1. liquid water, and 2. a sample conditioned on the presence of a rare motif. We trained the model on small-scale samples of liquid water produced using path-integral molecular dynamics simulation including nuclear quantum effects under ambient conditions. MAP-generated water configurations are shown to accurately reproduce the properties of the training set and to produce stable trajectories when used as initial conditions in classical and quantum dynamical simulations. We expect our approach to perform equally well on other disordered molecular systems while offering unique advantages in situations when the disorder is quenched rather than equilibrated

Solving the Wigner Equation with Signed Particles Monte Carlo for Chemically Relevant Potentials

This paper presents the Signed Particles Monte Carlo algorithm for the solution of the transient Wigner equation for potentials relevant in chemical physics. Benchmarks include the harmonic and the double well potentials

Inside the Black Box: A Physical Basis for the Effectiveness of Deep Generative Models of Amorphous Materials

We have recently demonstrated an effective protocol for the simulation of amorphous molecular configurations using the PixelCNN generative model (J. Phys. Chem. Lett. 2020, 11, 20, 8532). The morphological sampling of amorphous materials via such an autoregressive generation protocol sidesteps the high computational costs associated with simulating amorphous materials at scale, enabling practically unlimited structural sampling based on only small-scale experimental or computational training samples. An important question raised but not rigorously addressed in that report was whether this machine learning approach could be considered a physical simulation in the conventional sense. Here we answer this question by detailing the inner workings of the underlying algorithm that we refer to as the Morphological Autoregression Protocol or MAP. </p