Machine Learning Classification of Local Environments in Molecular Crystals.

Identifying local structural motifs and packing patterns of molecular solids is a challenging task for both simulation and experiment. We demonstrate two novel approaches to characterize local environments in different polymorphs of molecular crystals using learning models that employ either flexibly learned or handcrafted molecular representations. In the first case, we follow our earlier work on graph learning in molecular crystals, deploying an atomistic graph convolutional network combined with molecule-wise aggregation to enable per-molecule environmental classification. For the second model, we develop a new set of descriptors based on symmetry functions combined with a point-vector representation of the molecules, encoding information about the positions and relative orientations of the molecule. We demonstrate very high classification accuracy for both approaches on urea and nicotinamide crystal polymorphs and practical applications to the analysis of dynamical trajectory data for nanocrystals and solid-solid interfaces. Both architectures are applicable to a wide range of molecules and diverse topologies, providing an essential step in the exploration of complex condensed matter phenomena.

Electrostatic Potential of Functional Cations as a Predictor of Hydroxide Diffusion Pathways in Nanoconfined Environments of Anion Exchange Membranes.

Nanoconfined anion exchange membranes (AEMs) play a vital role in emerging electrochemical technologies. The ability to control dominant hydroxide diffusion pathways is an important goal in the design of nanoconfined AEMs. Such control can shorten hydroxide transport pathways between electrodes, reduce transport resistance, and enhance device performance. In this work, we propose an electrostatic potential (ESP) approach to explore the effect of the polymer electrolyte cation spacing on hydroxide diffusion pathways from a molecular perspective. By exploring cation ESP energy surfaces and validating outcomes through prior ab initio molecular dynamics simulations of nanoconfined AEMs, we find that we can achieve control over preferred hydroxide diffusion pathways by adjusting the cation spacing. The results presented in this work provide a unique and straightforward approach to predict preferential hydroxide diffusion pathways, enabling efficient design of highly conductive nanoconfined AEM materials for electrochemical technologies.

Tailoring Electrochemical CO2 Reduction on Copper by Reactive Ionic Liquid and Native Hydrogen Bond Donors.

Electrochemical CO2 reduction (CO2 RR) on copper (Cu) shows promise for higher-value products beyond CO. However, challenges such as the limited CO2 solubility, high overpotentials, and the competing hydrogen evolution reaction (HER) in aqueous electrolytes hinder the practical realization. We propose a functionalized ionic liquid (IL) which generates ion-CO2 adducts and a hydrogen bond donor (HBD) upon CO2 absorption to modulate CO2 RR on Cu in a non-aqueous electrolyte. As revealed by transient voltammetry, electrochemical impedance spectroscopy (EIS), and in situ surface-enhanced Raman spectroscopy (SERS) complemented with image charge augmented quantum-mechanical/molecular mechanics (IC-QM/MM) computations, a unique microenvironment is constructed. In this microenvironment, the catalytic activity is primarily governed by the IL and HBD concentrations; former controlling the double layer thickness and the latter modulating the local proton availability. This translates to ample CO2 availability, reduced overpotential, and suppressed HER where C4 products are obtained. This study deepens the understanding of electrolyte effects in CO2 RR and the role of IL ions towards electrocatalytic microenvironment design.

Machine learning electronic structure methods based on the one-electron reduced density matrix.

The theorems of density functional theory (DFT) establish bijective maps between the local external potential of a many-body system and its electron density, wavefunction and, therefore, one-particle reduced density matrix. Building on this foundation, we show that machine learning models based on the one-electron reduced density matrix can be used to generate surrogate electronic structure methods. We generate surrogates of local and hybrid DFT, Hartree-Fock and full configuration interaction theories for systems ranging from small molecules such as water to more complex compounds like benzene and propanol. The surrogate models use the one-electron reduced density matrix as the central quantity to be learned. From the predicted density matrices, we show that either standard quantum chemistry or a second machine-learning model can be used to compute molecular observables, energies, and atomic forces. The surrogate models can generate essentially anything that a standard electronic structure method can, ranging from band gaps and Kohn-Sham orbitals to energy-conserving ab-initio molecular dynamics simulations and infrared spectra, which account for anharmonicity and thermal effects, without the need to employ computationally expensive algorithms such as self-consistent field theory. The algorithms are packaged in an efficient and easy to use Python code, QMLearn, accessible on popular platforms.

Microswimmers under the spotlight: interplay between agents with different levels of activity.

The ability of active matter to assemble into reconfigurable nonequilibrium structures has drawn considerable interest in recent years. We investigate how active fluids respond to spatial light patterns through simulations and experiments on light-activated self-propelled colloidal particles. We examine the processes of inverse templated assembly, which involves creating a region without active particles through a bright pattern, and templated assembly, which promotes the formation of dense particle regions through a dark pattern. We identify scaling relations for the characteristic times for both processes that quantify the interplay between the dimension of the applied pattern and the intrinsic properties of the active fluid. We also explore the assembly mechanism and dynamics of large clusters and show how assembly and inverse assembly can be combined to create any arbitrarily complex template. In addition to providing protocols for templated assembly via light patterning, our results demonstrate how the local packing fraction can be fine-tuned by modulation of the light intensity. The protocol so obtained exceeds the capabilities of conventional assembly strategies, in which packing fraction is dictated by thermodynamics, and opens the door to arbitrarily precise and programmable nonequilibrium assembly strategies in active matter.

An exploration of machine learning models for the determination of reaction coordinates associated with conformational transitions.

Determining collective variables (CVs) for conformational transitions is crucial to understanding their dynamics and targeting them in enhanced sampling simulations. Often, CVs are proposed based on intuition or prior knowledge of a system. However, the problem of systematically determining a proper reaction coordinate (RC) for a specific process in terms of a set of putative CVs can be achieved using committor analysis (CA). Identifying essential degrees of freedom that govern such transitions using CA remains elusive because of the high dimensionality of the conformational space. Various schemes exist to leverage the power of machine learning (ML) to extract an RC from CA. Here, we extend these studies and compare the ability of 17 different ML schemes to identify accurate RCs associated with conformational transitions. We tested these methods on an alanine dipeptide in vacuum and on a sarcosine dipeptoid in an implicit solvent. Our comparison revealed that the light gradient boosting machine method outperforms other methods. In order to extract key features from the models, we employed Shapley Additive exPlanations analysis and compared its interpretation with the "feature importance" approach. For the alanine dipeptide, our methodology identifies ϕ and θ dihedrals as essential degrees of freedom in the C7ax to C7eq transition. For the sarcosine dipeptoid system, the dihedrals ψ and ω are the most important for the cisαD to transαD transition. We further argue that analysis of the full dynamical pathway, and not just endpoint states, is essential for identifying key degrees of freedom governing transitions.

An interoperable implementation of collective-variable based enhanced sampling methods in extended phase space within the OpenMM package.

Collective variable (CV)-based enhanced sampling techniques are widely used today for accelerating barrier-crossing events in molecular simulations. A class of these methods, which includes temperature accelerated molecular dynamics (TAMD)/driven-adiabatic free energy dynamics (d-AFED), unified free energy dynamics (UFED), and temperature accelerated sliced sampling (TASS), uses an extended variable formalism to achieve quick exploration of conformational space. These techniques are powerful, as they enhance the sampling of a large number of CVs simultaneously compared to other techniques. Extended variables are kept at a much higher temperature than the physical temperature by ensuring adiabatic separation between the extended and physical subsystems and employing rigorous thermostatting. In this work, we present a computational platform to perform extended phase space enhanced sampling simulations using the open-source molecular dynamics engine OpenMM. The implementation allows users to have interoperability of sampling techniques, as well as employ state-of-the-art thermostats and multiple time-stepping. This work also presents protocols for determining the critical parameters and procedures for reconstructing high-dimensional free energy surfaces. As a demonstration, we present simulation results on the high dimensional conformational landscapes of the alanine tripeptide in vacuo, tetra-N-methylglycine (tetra-sarcosine) peptoid in implicit solvent, and the Trp-cage mini protein in explicit water.

Geometric Deep Learning for Molecular Crystal Structure Prediction.

We develop and test new machine learning strategies for accelerating molecular crystal structure ranking and crystal property prediction using tools from geometric deep learning on molecular graphs. Leveraging developments in graph-based learning and the availability of large molecular crystal data sets, we train models for density prediction and stability ranking which are accurate, fast to evaluate, and applicable to molecules of widely varying size and composition. Our density prediction model, MolXtalNet-D, achieves state-of-the-art performance, with lower than 2% mean absolute error on a large and diverse test data set. Our crystal ranking tool, MolXtalNet-S, correctly discriminates experimental samples from synthetically generated fakes and is further validated through analysis of the submissions to the Cambridge Structural Database Blind Tests 5 and 6. Our new tools are computationally cheap and flexible enough to be deployed within an existing crystal structure prediction pipeline both to reduce the search space and score/filter crystal structure candidates.

Hydroxide Diffusion in Functionalized Cylindrical Nanopores as Idealized Models of Anion Exchange Membrane Environments: An Ab Initio Molecular Dynamics Study.

Anion exchange membranes (AEMs) have attracted significant interest for their applications in fuel cells and other electrochemical devices in recent years. Understanding water distributions and hydroxide transport mechanisms within AEMs is critical to improving their performance as concerns hydroxide conductivity. Recently, nanoconfined environments have been used to mimic AEM environments. Following this approach, we construct nanoconfined cylindrical pore structures using graphane nanotubes (GNs) functionalized with trimethylammonium cations as models of local AEM morphology. These structures were then used to investigate hydroxide transport using ab initio molecular dynamics (AIMD). The simulations showed that hydroxide transport is suppressed in these confined environments relative to the bulk solution although the mechanism is dominated by structural diffusion. One factor causing the suppressed hydroxide transport is the reduced proton transfer (PT) rates due to changes in hydroxide and water solvation patterns under confinement compared to bulk solution as well as strong interactions between hydroxide ions and the tethered cation groups.

A Green's Function Approach for Determining Surface Induced Broadening and Shifting of Molecular Energy Levels.

Upon adsorption of a molecule onto a surface, the molecular energy levels (MELs) broaden and change their alignment. This phenomenon directly affects electron transfer across the interface and is, therefore, a fundamental observable that influences electrochemical device performance. Here, we propose a rigorous parameter-free framework, built upon the theoretical construct of Green's functions, for studying the interface between a molecule and a bulk surface and its effect on MELs. The method extends beyond the usual wide-band limit approximation, and its generality allows its use with any level of electronic structure theory. We demonstrate its ability to predict the broadening and shifting of MELs as a function of intramolecular coupling, molecule/surface coupling, and the surface density of states for a molecule with two MELs adsorbed on a one-dimensional model metal surface. The new approach could help provide guidelines for the design and experimental characterization of electrochemical devices with optimal electron transport.

Machine learning the Hohenberg-Kohn map for molecular excited states.

The Hohenberg-Kohn theorem of density-functional theory establishes the existence of a bijection between the ground-state electron density and the external potential of a many-body system. This guarantees a one-to-one map from the electron density to all observables of interest including electronic excited-state energies. Time-Dependent Density-Functional Theory (TDDFT) provides one framework to resolve this map; however, the approximations inherent in practical TDDFT calculations, together with their computational expense, motivate finding a cheaper, more direct map for electronic excitations. Here, we show that determining density and energy functionals via machine learning allows the equations of TDDFT to be bypassed. The framework we introduce is used to perform the first excited-state molecular dynamics simulations with a machine-learned functional on malonaldehyde and correctly capture the kinetics of its excited-state intramolecular proton transfer, allowing insight into how mechanical constraints can be used to control the proton transfer reaction in this molecule. This development opens the door to using machine-learned functionals for highly efficient excited-state dynamics simulations.

A data-driven and topological mapping approach for the a priori prediction of stable molecular crystalline hydrates.

Predictions of the structures of stoichiometric, fractional, or nonstoichiometric hydrates of organic molecular crystals are immensely challenging due to the extensive search space of different water contents, host molecular placements throughout the crystal, and internal molecular conformations. However, the dry frameworks of these hydrates, especially for nonstoichiometric or isostructural dehydrates, can often be predicted from a standard anhydrous crystal structure prediction (CSP) protocol. Inspired by developments in the field of drug binding, we introduce an efficient data-driven and topologically aware approach for predicting organic molecular crystal hydrate structures through a mapping of water positions within the crystal structure. The method does not require a priori specification of water content and can, therefore, predict stoichiometric, fractional, and nonstoichiometric hydrate structures. This approach, which we term a mapping approach for crystal hydrates (MACH), establishes a set of rules for systematic determination of favorable positions for water insertion within predicted or experimental crystal structures based on considerations of the chemical features of local environments and void regions. The proposed approach is tested on hydrates of three pharmaceutically relevant compounds that exhibit diverse crystal packing motifs and void environments characteristic of hydrate structures. Overall, we show that our mapping approach introduces an advance in the efficient performance of hydrate CSP through generation of stable hydrate stoichiometries at low cost and should be considered an integral component for CSP workflows.

Imaginary-time open-chain path-integral approach for two-state time correlation functions and applications in charge transfer.

Quantum time correlation functions (TCFs) involving two states are important for describing nonadiabatic dynamical processes such as charge transfer (CT). Based on a previous single-state method, we propose an imaginary-time open-chain path-integral (OCPI) approach for evaluating the two-state symmetrized TCFs. Expressing the forward and backward propagation on different electronic potential energy surfaces as a complex-time path integral, we then transform the path variables to average and difference variables such that the integration over the difference variables up to the second order can be performed analytically. The resulting expression for the symmetrized TCF is equivalent to sampling the open-chain configurations in an effective potential that corresponds to the average surface. Using importance sampling over the extended OCPI space via open path-integral molecular dynamics, we tested the resulting path-integral approximation by calculating the Fermi's golden rule CT rate constant within a widely used spin-boson model. Comparing with the real-time linearized semiclassical method and analytical result, we show that the imaginary-time OCPI provides an accurate two-state symmetrized TCF and rate constant in the typical turnover region. It is shown that the first bead of the open chain corresponds to physical zero-time and that the endpoint bead corresponds to final time t; oscillations of the end-to-end distance perfectly match the nuclear mode frequency. The two-state OCPI scheme is seen to capture the tested model's electronic quantum coherence and nuclear quantum effects accurately.

Controlling Hydronium Diffusivity in Model Proton Exchange Membranes.

Fuel-cell-based proton exchange membranes (PEMs) show great potential as cost-effective and clean energy conversion devices. In our recent work, we found that for the low-hydrated model PEMs with a inhomogeneous water distribution and a sulfonate anionic functional end group (SO3-), the H3O+ reacts with SO3- according to SO3- + H3O+ ↔ SO3H + H2O, indicating that the anions in PEMs become active participants in the hydronium diffusion. In this work, we use fully atomistic ab initio molecular dynamics simulations to elucidate the optimal conditions that would promote the participation of SO3- in the hydronium diffusion mechanism by increasing the H3O+/SO3- reactivity, thus increasing the hydronium diffusivity along the cell. The results presented in this work allow us to suggest a set of design rules for creating novel, highly conductive PEMs operating at high temperatures under a nonuniform water distribution using a linker/anion with a relatively high pKa such as (CH2)2SO3. We expect that the discovery of these key design principles will play an important role in the synthesis of high-performing materials for emerging PEM-based fuel cell technologies.

Evolution of microscopic heterogeneity and dynamics in choline chloride-based deep eutectic solvents.

Deep eutectic solvents (DESs) are an emerging class of non-aqueous solvents that are potentially scalable, easy to prepare and functionalize for many applications ranging from biomass processing to energy storage technologies. Predictive understanding of the fundamental correlations between local structure and macroscopic properties is needed to exploit the large design space and tunability of DESs for specific applications. Here, we employ a range of computational and experimental techniques that span length-scales from molecular to macroscopic and timescales from picoseconds to seconds to study the evolution of structure and dynamics in model DESs, namely Glyceline and Ethaline, starting from the parent compounds. We show that systematic addition of choline chloride leads to microscopic heterogeneities that alter the primary structural relaxation in glycerol and ethylene glycol and result in new dynamic modes that are strongly correlated to the macroscopic properties of the DES formed.

Imidacloprid Crystal Polymorphs for Disease Vector Control and Pollinator Protection.

Imidacloprid, the world's leading insecticide, has been approved recently for controlling infectious disease vectors; yet, in agricultural settings, it has been implicated in the frightening decline of pollinators. This argues for strategies that sharply reduce the environmental impact of imidacloprid. When used as a contact insecticide, the effectiveness of imidacloprid relies on physical contact between its crystal surfaces and insect tarsi. Herein, seven new imidacloprid crystal polymorphs are reported, adding to two known forms. Anticipating that insect uptake of imidacloprid molecules would depend on the respective free energies of crystal polymorph surfaces, measurements of insect knockdown times for the metastable crystal forms were as much as nine times faster acting than the commercial form against Aedes, Anopheles, and Culex mosquitoes as well as Drosophila (fruit flies). These results suggest that replacement of commercially available imidacloprid crystals (a.k.a. Form I) in space-spraying with any one of three new polymorphs, Forms IV, VI, IX, would suppress vector-borne disease transmission while reducing environmental exposure and harm to nontarget organisms.

Characterizing and Contrasting Structural Proton Transport Mechanisms in Azole Hydrogen Bond Networks Using Ab Initio Molecular Dynamics.

Imidazole and 1,2,3-triazole are promising hydrogen-bonded heterocycles that conduct protons via a structural mechanism and whose derivatives are present in systems ranging from biological proton channels to proton exchange membrane fuel cells. Here, we leverage multiple time-stepping to perform ab initio molecular dynamics of imidazole and 1,2,3-triazole at the nanosecond time scale. We show that despite the close structural similarities of these compounds, their proton diffusion constants vary by over an order of magnitude. Our simulations reveal the reasons for these differences in diffusion constants, which range from the degree of hydrogen-bonded chain linearity to the effect of the central nitrogen atom in 1,2,3-triazole on proton transport. In particular, we uncover evidence of two "blocking" mechanisms in 1,2,3-triazole, where covalent and hydrogen bonds formed by the central nitrogen atom limit the mobility of protons. Our simulations thus provide insights into the origins of the experimentally observed 10-fold difference in proton conductivity.