Nanoscale Simulation of Plastic Contaminants Migration in Packaging Materials and Potential Leaching into Model Food Fluids.

Polymers are the most commonly used packaging materials for nutrition and consumer products. The ever-growing concern over pollution and potential environmental contamination generated from single-use packaging materials has raised safety questions. Polymers used in these materials often contain impurities, including unreacted monomers and small oligomers. The characterization of transport properties, including diffusion and leaching of these molecules, is largely hampered by the long timescales involved in shelf life experiments. In this work, we employ atomistic molecular simulation techniques to explore the main mechanisms involved in the bulk and interfacial transport of monomer molecules from three polymers commonly employed as packaging materials: polyamide-6, polycarbonate, and poly(methyl methacrylate). Our simulations showed that both hopping and continuous diffusion play important roles in inbound monomer diffusion and that solvent-polymer compatibility significantly affects monomer leaching. These results provide rationalization for monomer leaching in model food formulations as well as bulky industry-relevant molecules. Through this molecular-scale characterization, we offer insights to aid in the design of polymer/consumer product interfaces with reduced risk of contamination and longer shelf life.

Advancing material property prediction: using physics-informed machine learning models for viscosity.

In materials science, accurately computing properties like viscosity, melting point, and glass transition temperatures solely through physics-based models is challenging. Data-driven machine learning (ML) also poses challenges in constructing ML models, especially in the material science domain where data is limited. To address this, we integrate physics-informed descriptors from molecular dynamics (MD) simulations to enhance the accuracy and interpretability of ML models. Our current study focuses on accurately predicting viscosity in liquid systems using MD descriptors. In this work, we curated a comprehensive dataset of over 4000 small organic molecules' viscosities from scientific literature, publications, and online databases. This dataset enabled us to develop quantitative structure-property relationships (QSPR) consisting of descriptor-based and graph neural network models to predict temperature-dependent viscosities for a wide range of viscosities. The QSPR models reveal that including MD descriptors improves the prediction of experimental viscosities, particularly at the small data set scale of fewer than a thousand data points. Furthermore, feature importance tools reveal that intermolecular interactions captured by MD descriptors are most important for viscosity predictions. Finally, the QSPR models can accurately capture the inverse relationship between viscosity and temperature for six battery-relevant solvents, some of which were not included in the original data set. Our research highlights the effectiveness of incorporating MD descriptors into QSPR models, which leads to improved accuracy for properties that are difficult to predict when using physics-based models alone or when limited data is available.

Highly efficient implementation of analytic nonadiabatic derivative couplings within the pseudospectral method.

A pseudospectral implementation of nonadiabatic derivative couplings in the Tamm-Dancoff approximation is reported, and the accuracy and efficiency of the pseudospectral nonadiabatic derivative couplings are studied. Our results demonstrate that the pseudospectral method provides mean absolute errors of 0.2%-1.9%, while providing a significant speedup. Benchmark calculations on fullerenes (Cn, n up to 100) using B3LYP achieved 10- to 15-fold, 8- to 17-fold, and 43- to 75-fold speedups for 6-31G**, 6-31++G**, and cc-pVTZ basis sets, respectively, when compared to the conventional spectral method.

Molecular-Scale Exploration of Mechanical Properties and Interactions of Poly(lactic acid) with Cellulose and Chitin.

Poly(lactic acid) (PLA), one of the pillars of the current overarching displacement trend switching from fossil- to natural-based polymers, is often used in association with polysaccharides to increase its mechanical properties. However, the use of PLA/polysaccharide composites is greatly hampered by their poor miscibility, whose underlying nature is still vastly unexplored. This work aims to shed light on the interactions of PLA and two representative polysaccharide molecules (cellulose and chitin) and reveal structure-property relationships from a fundamental perspective using atomistic molecular dynamics. Our computational strategy was able to reproduce key experimental mechanical properties of pure and/or composite materials, reveal a decrease in immiscibility in PLA/chitin compared to PLA/cellulose associations, assert PLA-oriented polysaccharide reorientations, and explore how less effective PLA-polysaccharide hydrogen bonds are related to the poor PLA/polysaccharide miscibility. The connection between the detailed chemical interactions and the composite behavior found in this work is beneficial to the discovery of new biodegradable and natural polymer composite mixtures that can provide needed performance characteristics.

Development of scalable and generalizable machine learned force field for polymers.

Understanding and predicting the properties of polymers is vital to developing tailored polymer molecules for desired applications. Classical force fields may fail to capture key properties, for example, the transport properties of certain polymer systems such as polyethylene glycol. As a solution, we present an alternative potential energy surface, a charge recursive neural network (QRNN) model trained on DFT calculations made on smaller atomic clusters that generalizes well to oligomers comprising larger atomic clusters or longer chains. We demonstrate the validity of the polymer QRNN workflow by modeling the oligomers of ethylene glycol. We apply two rounds of active learning (addition of new training clusters based on current model performance) and implement a novel model training approach that uses partial charges from a semi-empirical method. Our developed QRNN model for polymers produces stable molecular dynamics (MD) simulation trajectory and captures the dynamics of polymer chains as indicated by the striking agreement with experimental values. Our model allows working on much larger systems than allowed by DFT simulations, at the same time providing a more accurate force field than classical force fields which provides a promising avenue for large-scale molecular simulations of polymeric systems.

Exploring the Effects of Wetting and Free Fatty Acid Deposition on an Atomistic Hair Fiber Surface Model Incorporating Keratin-Associated Protein 5-1.

The complex development of cosmetic and medical formulations relies on an ever-growing accuracy of predictive models of hair surfaces. Hitherto, modeling efforts have focused on the description of 18-methyl eicosanoic acid (18-MEA), the primary fatty acid covalently attached to the hair surface, without explicit modeling of the protein layer. Herein, the molecular details of the outermost surface of the human hair fiber surface, also called the F-layer, were studied using molecular dynamics (MD) simulations. The F-layer is composed primarily of keratin-associated proteins KAP5 and KAP10, which are decorated with 18-MEA on the outer surface of a hair fiber. In our molecular model, we incorporated KAP5-1 and evaluated the surface properties of 18-MEA through MD simulations, resulting in 18-MEA surface density, layer thickness, and tilt angles in agreement with previous experimental and computational studies. Subsequent models with reduced 18-MEA surface density were also generated to mimic damaged hair surfaces. Response to wetting of virgin and damaged hair showed rearrangement of 18-MEA on the surface, allowing for water penetration into the protein layer. To demonstrate a potential use case for these atomistic models, we deposited naturally occurring fatty acids and measured 18-MEA's response in both dry and wet conditions. As fatty acids are often incorporated in shampoo formulations, this work demonstrates the ability to model the adsorption of ingredients on hair surfaces. This study illustrates, for the first time, the complex behavior of a realistic F-layer at the molecular level and opens up the possibility of studying the adsorption behavior of larger, more complex molecules and formulations.

Shearing friction behaviour of synthetic polymers compared to a functionalized polysaccharide on biomimetic surfaces: models for the prediction of performance of eco-designed formulations.

The substitution of natural, bio-based and/or biodegradable polymers for those of petrochemical origin in consumer formulations has become an active area of research and development as the sourcing and destiny of material components becomes a more critical factor in product design. These polymers often differ from their petroleum-based counterparts in topology, raw material composition and solution behaviour. Effective and efficient reformulation that maintains comparable cosmetic performance to existing products requires a deep understanding of the differences in frictional behaviour between polymers as a function of their molecular structure. In this work, we simulate the tribological behaviour of three topologically distinct polymers in solution with surfactants and in contact with hair-biomimetic patterned surfaces. We compare a generic functionalized polysaccharide to two performant polymers used in shampoo formulations: a strongly positively charged polyelectrolyte and a zwitterionic copolymer. Topological differences are expected to affect rheological properties, as well as their direct interaction with structured biological substrates. Using a refined Martini-style coarse-grained model we describe the polymer-dependent differences in aggregation behaviour as well as selective interactions with a biomimetic model hair surface. Additionally, we introduce a formalism to characterize the response of the solution to shear as an initial study on lubrication properties, which define the sensorial performance of these systems in cosmetics (i.e., manageability, touch, etc.). The tools and techniques presented in this work illustrate the strength of molecular simulation in eco-design of formulation as a complement to experiment. These efforts help advance our understanding of how we can relate complex atomic-scale solution behaviour to relevant macroscopic properties. We expect these techniques to play an increasingly important role in advancing strategies for green polymer formulation design by providing an understanding for how new polymers could reach and even exceed the level of performance of existing polymers.

De Novo Design of Molecules with Low Hole Reorganization Energy Based on a Quarter-Million Molecule DFT Screen: Part 2.

Organic semiconductors have many desirable properties including improved manufacturing and flexible mechanical properties. Due to the vastness of chemical space, it is essential to efficiently explore chemical space when designing new materials, including through the use of generative techniques. New generative machine learning methods for molecular design continue to be published in the literature at a significant rate but successfully adapting methods to new chemistry and problem domains remains difficult. These challenges necessitate continual method evaluation to probe method viability for use in alternative applications not covered in the original works. In continuation of our previous work, we evaluate four additional machine-learning-based de novo methods for generating molecules with high predicted hole mobility for use in semiconductor applications. The four generative methods evaluated here are (1) Molecule Deep Q-Networks (MolDQN), which utilizes Deep-Q learning to directly optimize molecular structure graphs for desired properties instead of generating SMILES, (2) Graph-based Genetic Algorithm (GraphGA), which uses a genetic algorithm for optimization where crossovers and mutations are defined in terms of RDKit's reaction SMILES, (3) Generative Tensorial Reinforcement Learning (GENTRL), which is a variational autoencoder (VAE) with a learned prior distribution and optimized using reinforcement learning, and (4) Monte Carlo tree search exploration of chemical space in conjunction with a recurrent neural network (RNN) decoder (ChemTS). The generated molecules were evaluated using density functional theory (DFT) and we discovered better performing molecules with the GraphGA method compared to the other approaches.

High-Dimensional Neural Network Potential for Liquid Electrolyte Simulations.

Liquid electrolytes are one of the most important components of Li-ion batteries, which are a critical technology of the modern world. However, we still lack the computational tools required to accurately calculate key properties of these materials (viscosity and ionic diffusivity) from first principles necessary to support improved designs. In this work, we report a machine learning-based force field for liquid electrolyte simulations, which bridges the gap between the accuracy of range-separated hybrid density functional theory and the efficiency of classical force fields. Predictions of material properties made with this force field are quantitatively accurate compared to experimental data. Our model uses the QRNN deep neural network architecture, which includes both long-range interactions and global charge equilibration. The training data set is composed solely of non-periodic density functional theory (DFT), allowing the practical use of an accurate theory (here, ωB97X-D3BJ/def2-TZVPD), which would be prohibitively expensive for generating large data sets with periodic DFT. In this report, we focus on seven common carbonates and LiPF6, but this methodology has very few assumptions and can be readily applied to any liquid electrolyte system. This provides a promising path forward for large-scale atomistic modeling of many important battery chemistries.

Organic radical emitters: nature of doublet excitons in emissive layers.

Organic radical emitters have received significant attention as a new route to efficient organic light-emitting diodes (OLEDs). The electronic structure of radical emitters allows bypassing the triplet harvesting issue in current OLED devices. However, the nature of doublet excited states remains elusive due to the complex nature of emissive layers. To date, the computational efforts have treated radical carrying materials as isolated entities in the gas phase. However, OLED materials are applied as thin solid films where intermolecular interactions significantly impact optoelectronic properties of the devices. Here, we combine molecular dynamics simulations and quantum chemical calculations to evaluate the effect of emitter-host interactions on the performance of radical-based emissive layers. Results demonstrate that intermolecular interactions remarkably modulate the electronic properties of the radicals in the thin solid films. The doublet excitons of isolated emitters demonstrate a hybrid character of charge-transfer (CT) and local-excitation (LE), while the emitter-host clusters present a significant CT character. Further, the impact of static and dynamic disorders on the hole-electron recombination is studied. Although the host-emitter interactions simultaneously decrease radiative rates and increase non-radiative rates, the latter rates are 100 times smaller than the former rates, allowing internal quantum efficiency to reach 100% for the doublet-based emission process. The results of this study highlight the significant impact of host-emitter interactions on radiative and non-radiative recombination processes and offer guidelines to tune these interactions for advancing radical-based OLEDs.

Pseudospectral implementations of long-range corrected density functional theory.

We have implemented pseudospectral density-functional theory (DFT) with long-range corrected DFT functionals (PS-LRC) in quantum mechanics package Jaguar, and applied it in the calculations of geometry optimizations, dimmer interaction energies, polarizabilities and first-order hyperpolarizabilities, harmonic vibrational frequencies, S1 and T1 excitation energies, singlet-triplet gaps, charge transfer numbers, oscillator strengths, reaction barrier heights, electron-transfer couplings, and charge-transfer excitation energies. From our accuracy benchmark analysis, PS grids, PS dealiasing functions, PS atomic corrections, PS multigrid strategy, PS length scales, and PS cutoff scheme perform well in PS DFT with LRC density functionals with very small and ignorable deviations when compared to the conventional spectral (CS) method. The timing benchmark study of S1 excitation energy calculations of fullerenes (Cn , n up to 540) demonstrates that PS-LRC achieves 1.4-8.4-fold speedups in SCF, 22-32-fold speedups in Tamm-Dancoff approximation, and 6-15-fold speedups in total wall clock time with an average error 0.004 eV of excitation energies compared to the CS method.

De Novo Design of Molecules with Low Hole Reorganization Energy Based on a Quarter-Million Molecule DFT Screen.

Materials exhibiting higher mobilities than conventional organic semiconducting materials such as fullerenes and fused thiophenes are in high demand for applications in printed electronics. To discover new molecules in the heteroacene family that might show improved hole mobility, three de novo design methods were applied. Machine learning (ML) models were generated based on previously calculated hole reorganization energies of a quarter million examples of heteroacenes, where the energies were calculated by applying density functional theory (DFT) and a massive cloud computing environment. The three generative methods applied were (1) the continuous space method, where molecular structures are converted into continuous variables by applying the variational autoencoder/decoder technique; (2) the method based on reinforcement learning of SMILES strings (the REINVENT method); and (3) the junction tree variational autoencoder method that directly generates molecular graphs. Among the three methods, the second and third methods succeeded in obtaining chemical structures whose DFT-calculated hole reorganization energy was lower than the lowest energy in the training dataset. This suggests that an extrapolative materials design protocol can be developed by applying generative modeling to a quantitative structure-property relationship (QSPR) utility function.

Highly efficient implementation of the analytical gradients of pseudospectral time-dependent density functional theory.

The accuracy and efficiency of time-dependent density functional theory (TDDFT) excited state gradient calculations using the pseudospectral method are presented. TDDFT excited state geometry optimizations of the G2 test set molecules, the organic fluorophores with large Stokes shifts, and the Pt-complexes show that the pseudospectral method gives average errors of 0.01-0.1 kcal/mol for the TDDFT excited state energy, 0.02-0.06 pm for the bond length, and 0.02-0.12° for the bond angle when compared to the results from conventional TDDFT. TDDFT gradient calculations of fullerenes (Cn, n up to 540) with the B3LYP functional and 6-31G** basis set show that the pseudospectral method provides 8- to 14-fold speedups in the total wall clock time over the conventional methods. The pseudospectral TDDFT gradient calculations with a diffuse basis set give higher speedups than the calculations for the same basis set without diffuse functions included.

Design of Organic Electronic Materials With a Goal-Directed Generative Model Powered by Deep Neural Networks and High-Throughput Molecular Simulations.

In recent years, generative machine learning approaches have attracted significant attention as an enabling approach for designing novel molecular materials with minimal design bias and thereby realizing more directed design for a specific materials property space. Further, data-driven approaches have emerged as a new tool to accelerate the development of novel organic electronic materials for organic light-emitting diode (OLED) applications. We demonstrate and validate a goal-directed generative machine learning framework based on a recurrent neural network (RNN) deep reinforcement learning approach for the design of hole transporting OLED materials. These large-scale molecular simulations also demonstrate a rapid, cost-effective method to identify new materials in OLEDs while also enabling expansion into many other verticals such as catalyst design, aerospace, life science, and petrochemicals.

Active Learning Accelerates Design and Optimization of Hole-Transporting Materials for Organic Electronics.

Data-driven methods are receiving increasing attention to accelerate materials design and discovery for organic light-emitting diodes (OLEDs). Machine learning (ML) has enabled high-throughput screening of materials properties to suggest new candidates for organic electronics. However, building reliable predictive ML models requires creating and managing a high volume of data that adequately address the complexity of materials' chemical space. In this regard, active learning (AL) has emerged as a powerful strategy to efficiently navigate the search space by prioritizing the decision-making process for unexplored data. This approach allows a more systematic mechanism to identify promising candidates by minimizing the number of computations required to explore an extensive materials library with diverse variables and parameters. In this paper, we applied a workflow of AL that accounts for multiple optoelectronic parameters to identify materials candidates for hole-transport layers (HTL) in OLEDs. Results of this work pave the way for efficient screening of materials for organic electronics with superior efficiencies before laborious simulations, synthesis, and device fabrication.

Characterizing moisture uptake and plasticization effects of water on amorphous amylose starch models using molecular dynamics methods.

Dynamics and thermophysical properties of amorphous starch were explored using molecular dynamics (MD) simulations. Using the OPLS3e force field, simulations of short amylose chains in water were performed to determine force field accuracy. Using well-tempered metadynamics, a free energy map of the two glycosidic angles of an amylose molecule was constructed and compared with other modern force fields. Good agreement of torsional sampling for both solvated and amorphous amylose starch models was observed. Using combined grand canonical Monte Carlo (GCMC)/MD simulations, a moisture sorption isotherm curve is predicted along with temperature dependence. Concentration-dependent activation energies for water transport agree quantitatively with previous experiments. Finally, the plasticization effect of moisture content on amorphous starch was investigated. Predicted glass transition temperature (Tg) depression as a function of moisture content is in line with experimental trends. Further, our calculations provide a value for the dry Tg for amorphous starch, a value which no experimental value is available.

Molecular Design Based on Donor-Weak Donor Scaffold for Blue Thermally-Activated Delayed Fluorescence Designed by Combinatorial DFT Calculations.

Quantum chemical calculations are necessary to develop advanced emitter materials showing thermally-activated delayed fluorescence (TADF) for organic light-emitting diodes (OLEDs). However, calculation costs become problematic when more accurate functionals were used, therefore it is judicious to use a multimethod approach for efficiency. Here we employed combinatorial chemistry in silico to develop the deep blue TADF materials with a new concept of homo-junction design. The homo-junction materials containing TADF candidates designed by calculation were synthesized and analyzed. We found that these materials showed the emission from charge transfer (CT) state, and the clear delayed emission was provided in solid state. Because the homo-junction TADF materials showed three exponential decayed emission in solid state, we employed novel four-state kinetic analysis.

Massive Theoretical Screen of Hole Conducting Organic Materials in the Heteroacene Family by Using a Cloud-Computing Environment.

Materials exhibiting higher mobilities than conventional organic semiconducting materials such as fullerenes and fused thiophenes are in high demand for applications in printed electronics. To discover new molecules in the heteroacene family that might show improved charge mobility, a massive theoretical screen of hole conducting properties of molecules was performed by using a cloud-computing environment. Over 7 000 000 structures of fused furans, thiophenes and selenophenes were generated and 250 000 structures were randomly selected to perform density functional theory (DFT) calculations of hole reorganization energies. The lowest hole reorganization energy calculated was 0.0548 eV for a fused thioacene having 8 aromatics rings. Hole mobilities of compounds with the lowest 130 reorganization energy were further processed by applying combined DFT and molecular dynamics (MD) methods. The highest mobility calculated was 1.02 and 9.65 cm2/(V s) based on percolation and disorder theory, respectively, for compounds containing selenium atoms with 8 aromatic rings. These values are about 20 times higher than those for dinaphthothienothiophene (DNTT).

Automated Transition State Search and Its Application to Diverse Types of Organic Reactions.

Transition state search is at the center of multiple types of computational chemical predictions related to mechanistic investigations, reactivity and regioselectivity predictions, and catalyst design. The process of finding transition states in practice is, however, a laborious multistep operation that requires significant user involvement. Here, we report a highly automated workflow designed to locate transition states for a given elementary reaction with minimal setup overhead. The only essential inputs required from the user are the structures of the separated reactants and products. The seamless workflow combining computational technologies from the fields of cheminformatics, molecular mechanics, and quantum chemistry automatically finds the most probable correspondence between the atoms in the reactants and the products, generates a transition state guess, launches a transition state search through a combined approach involving the relaxing string method and the quadratic synchronous transit, and finally validates the transition state via the analysis of the reactive chemical bonds and imaginary vibrational frequencies as well as by the intrinsic reaction coordinate method. Our approach does not target any specific reaction type, nor does it depend on training data; instead, it is meant to be of general applicability for a wide variety of reaction types. The workflow is highly flexible, permitting modifications such as a choice of accuracy, level of theory, basis set, or solvation treatment. Successfully located transition states can be used for setting up transition state guesses in related reactions, saving computational time and increasing the probability of success. The utility and performance of the method are demonstrated in applications to transition state searches in reactions typical for organic chemistry, medicinal chemistry, and homogeneous catalysis research. In particular, applications of our code to Michael additions, hydrogen abstractions, Diels-Alder cycloadditions, carbene insertions, and an enzyme reaction model involving a molybdenum complex are shown and discussed.