Coarse-Graining of Anisotropic Molecules for Energy Materials Simulations

Coarse-graining--simplifying models of molecules by representing a collection of atoms with a simulation element like a sphere or ellipsoid--can significantly increase the timescales accessible to simulations without loss of structural accuracy. Spherical simulation elements are inaccurate representations of flat molecular structures, though, which are better represented with anisotropic shapes like ellipsoids. In this work we debug and extend open source software (GRiTS) for calculating the shapes and orientations of an ellipsoid representing a collection of atoms. These functionalities are useful for both validating the correctness of coarse-grained models and for training advanced anisotropic potentials that can be used in accelerated molecular simulations

Molecular Interactions of Polydimethylsiloxane and Ni-Mn-Ga

Tiny pumps that can deliver microliters of fluid against high back-pressures can be made from Ni-Mn-Ga alloys. The fluid is transported in a movable pocket made between the alloy and a surrounding seal material, enabled by the magnetic shape memory properties of the alloy. The interaction between the sealant and the alloy is therefore critical, in particular how hey adhere and delaminate during pumping. This simulation study aims to quantify the interactions between the sealant poly-dimethylsiloxane (PDMS) and the Ni-Mn-Ga alloy. To study these PDMS-alloy interfaces, molecular dynamics simulations are performed using HOOMD-Blue on graphics processing units. We develop atomistic models for the interface components and investigate how the adhesion of short PDMS chains on Ni-Mn-Ga depend on model parameters, and temperature. We measure simulation speed as a function of system size on different processors to determine limits to computational feasibility and we identify conditions that facilitate adhesion. These simulations provide a first step towards more detailed studies of such organic/alloy interfaces and may help to identify more optimal sealant chemistries in the future

Predicting Perylene Morphology Using Molecular Dynamics

Perylene molecules could be used to make inexpensive solar panels if these flat molecules can be arranged into ordered structures. In this work we investigate how the distribution of electrical charges on perylene molecules influence how they self-assemble using molecular dynamics simulations. Using the HOOMD-blue simulation package and leveraging Boise State’s supercomputer cluster “Kestrel”, we perform hundreds of simulations of perylene to evaluate how the structure of 10, 100, and 1000-molecule aggregates change with temperature and partial charge. We observe the differences in self-assembly of perylene using the 3-D molecular visualization program VMD and we quantify simulation equilibration using the potential energy. The equilibrated morphologies from our simulations are compared against perylene arrangements from scattering experiments. We find that a positively charged rim and negatively charged center make the molecules less likely to form columnar stacks, which explains the differences between experimental perylene measurements and simulations of uncharged molecules

Developing Neural Networks to Represent Anisotropic Molecular Interactions

Efficiency of electricity generation, for instance in solar cells, is determined by the structure of organic molecules in the solar cell material. To determine such defining characteristics molecular dynamic computer simulations are performed, but only to run on simplified models of the molecular structure in order to conserve computational time. With these simulations we are now applying machine learning (ML) models, specifically artificial neural networks, to encode the molecular interactions between anisotropic rigid bodies. This way polymer and macromolecular systems can be predicted, while lowering computational cost with minimal loss of structural accuracy for these equilibrium systems. We then test the network structure of these machine learning models and the training datasets they are learning off of. Doing so in order to demonstrate the challenges that arise when moving from spherically-symmetric systems to those requiring orientation specific torque calculations

High-throughput Molecular Simulations into the Morphology of P3HT:PCBM Blends

The goal of this research is to understand how temperature, solvent quality, solvent amount, and the concentrations of organic photovoltaic (OPV) components determine active layer morphology. This understanding will improve techniques for engineering OPV devices, which can be inexpensively processed from abundant materials but presently suffer from low photoconversion efficiencies. We perform molecular dynamics (MD) simulations using HOOMD-Blue accelerated with graphics processing units (GPUs) to quantify how individual molecules self-assemble into structures that influence power conversion efficiency. We simulate blends of poly(3-hexylthiophene-2,5-diyl) (P3HT) with [6,6]-phenyl-C61-butyric acid methyl ester (PC61BM) and [6,6]-phenyl-C71-butyric acid methyl ester (PC71BM), three of the most important molecules in OPVs. By screening hundreds of combinations of concentration, temperature, and solvent properties, we can identify the conditions that optimize their self-organization. We quantify the degree of order in the predicted morphologies with radial distribution functions, structure factors, and simulated diffraction patterns. We find morphologies in agreement with prior experimental and theoretical work, and offer suggestions for future combinatorial studies

Simplified Simulations for Studying Self-Assembling Terminal Structures

We develop models of patchy particles for use in molecular dynamics simulations of self-assembly. These models are inspired by virus capsids, whose building blocks reliably arrange themselves into terminal structures - configurations that have many copies of identical building blocks but which stop growing once they are the right size. We specify the shapes of modeled building blocks and which components of them are attracted to which other components and perform molecular dynamics simulations to test our hypotheses about which structures are thermodynamically stable. Our results show how sensitive the assembly of two-dimensional triangular capsids are to attractive site placement and strength. This work is a first step towards building more complex models of smart particle assembly that can be used to test hypotheses about the theoretical limits of self-assembly

Computational Investigations of Surface Adsorption for Improving Catalysts

In this work we develop computational tools for initializing and performing molecular simulations of organic molecules in the presence of catalysts. Specifically, our work enables the adsorption of organic molecules including ethane and ethylene on the M1 catalyst to be investigated as a function of temperature. This work provides capabilities for understanding which surface modifiers may energetically stabilize the catalyst while not hindering catalyst stability

Molecular Simulations for Organic Photovoltaic Self-Assembly

The goal of this project is to help experimentalists choose ingredients and conditions for synthesizing solar cells made with organic molecules.The packing of molecules in organic photovoltaics (OPVs) influences charge transport and overall solar cell efficiency. We use molecular dynamics (MD) simulations to predict equilibrium morphologies of new candidate OPV ingredients. We develop new software in python for initializing, simulating, and analyzing these new compounds. The MD simulations provide predictions of molecular structure that can be used to infer model correctness and electronic properties. Optimal conditions for the self-assembly of ordered systems of ITIC and derivatives are identified. Conditions of interest for self assembly include temperatures around 300 K and densities in the range of 0.9 to 1.1. Overall we find the new models we create of ITIC, ITIC-F4, and CZTPTZ8FITIC to show promise in predicting structure of experimentally-relevant length scales and time scales, which should help to inform how charges move through materials made with these new organic semiconductors

Understanding Packing of New Compounds for Inexpensive Solar Panels

We perform molecular dynamics simulations to investigate the structure of two kinds of molecules that could be used in high-efficiency organic solar cells. We consider a branched molecule (ITIC) and polymer (PTB7) and measure their spatial correlations and dynamics as a function of temperature and density. Using radial distribution functions to measure local correlations we identify a density threshold above which both types of molecules become too entangled to rearrange. We find increased spatial correlations at lower temperatures when densities are below the entanglement threshold and fundamentally different molecular packings of the branched ITIC molecules versus the more linear PTB7 molecules that can more easily align. Our findings provide insight into the molecular packings that could correlate with better charge transport in organic solar cells and inform strategies for more efficient and informative future simulations

Predicting Glass Transition Temperatures of Epoxies with Coarse Grained Molecular Dynamics

We perform coarse-grained molecular dynamics simulations of epoxies to study the way their microstructure depends on how they are processed. We then perform additional simulations to calculate the glass transition temperatures of our systems and compare against similar experimental systems. We find that the glass transition temperatures of our un-toughened epoxy systems match up well with experiments. This work enables the structure of experimentally-relevant volumes of epoxies to be predicted, a problem that is intractable for many more detailed representations of epoxies