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Open material databases storing thousands of material structures and their properties have become the cornerstone of modern computational materials science. Yet, the raw simulation outputs are generally not shared due to their huge size. In this work, we describe a cloud-based platform to enable fast post-processing of the trajectories and to facilitate sharing of the raw data. As an initial demonstration, our database includes 6286 molecular dynamics trajectories for amorphous polymer electrolytes (5.7 terabytes of data). We create a public analysis library at https://github.com/TRI-AMDD/htp_md to extract ion transport properties from the raw data using expert-designed functions and machine learning models. The analysis is run automatically on the cloud, and the results are uploaded onto an open database. Our platform encourages users to contribute both new trajectory data and analysis functions via public interfaces. Finally, we create a front-end user interface at https://www.htpmd.matr.io/ for browsing and visualization of our data. We envision the platform to be a new way of sharing raw data and new insights for the materials science community.

Polymer electrolytes are promising candidates for the next generation lithium-ion battery technology. Large scale screening of polymer electrolytes is hindered by the significant cost of molecular dynamics (MD) simulation in amorphous systems: the amorphous structure of polymers requires multiple, repeated sampling to reduce noise and the slow relaxation requires long simulation time for convergence. Here, we accelerate the screening with a multi-task graph neural network that learns from a large amount of noisy, unconverged, short MD data and a small number of converged, long MD data. We achieve accurate predictions of 4 different converged properties and screen a space of 6247 polymers that is orders of magnitude larger than previous computational studies. Further, we extract several design principles for polymer electrolytes and provide an open dataset for the community. Our approach could be applicable to a broad class of material discovery problems that involve the simulation of complex, amorphous materials.

The application of machine learning to predict materials properties measured by experiments are valuable yet difficult due to the limited amount of experimental data. In this work, we use a multifidelity random forest model to learn the experimental formation enthalpy of materials with prediction accuracy higher than the Perdew-Burke-Ernzerhof (PBE) functional with linear correction, PBEsol, and meta-generalized gradient approximation (meta-GGA) functionals (SCAN and r2SCAN), and it outperforms the hotly studied deep neural network-based representation learning and transfer learning. We then use the model to calibrate the DFT formation enthalpy in the Materials Project database and discover materials with underestimated stability. The multifidelity model is also used as a data-mining approach to find how DFT deviates from experiments by explaining the model output.

Graph neural networks (GNNs) have recently been used to learn the representations of crystal structures through an end-to-end data-driven approach. However, a systematic top-down approach to evaluate and understand the limitations of GNNs in accurately capturing crystal structures has yet to be established. In this study, we introduce an approach using human-designed descriptors as a compendium of human knowledge to investigate the extent to which GNNs can comprehend crystal structures. Our findings reveal that current state-of-the-art GNNs fall short in accurately capturing the periodicity of crystal structures. We analyze this failure by exploring three aspects: local expressive power, long-range information processing, and readout function. To address these identified limitations, we propose a straightforward and general solution: the hybridization of descriptors with GNNs, which directly supplements the missing information to GNNs. The hybridization enhances the predictive accuracy of GNNs for specific material properties, most notably phonon internal energy and heat capacity, which heavily rely on the periodicity of materials.

The Origins, Spectral Interpretation, Resource Identification, and Security Regolith Explorer (OSIRIS-REx) spacecraft arrived at its target, near-Earth asteroid 101955 Bennu, in December 2018. After one year of operating in proximity, the team selected a primary site for sample collection. In October 2020, the spacecraft descended to the surface of Bennu and collected a sample. The spacecraft departed Bennu in May 2021 and will return the sample to Earth in September 2023. The analysis of the returned sample will produce key data to determine the history of this B-type asteroid and that of its components and precursor objects. The main goal of the OSIRIS-REx Sample Analysis Plan is to provide a framework for the Sample Analysis Team to meet the Level 1 mission requirement to analyze the returned sample to determine presolar history, formation age, nebular and parent-body alteration history, relation to known meteorites, organic history, space weathering, resurfacing history, and energy balance in the regolith of Bennu. To achieve this goal, this plan establishes a hypothesis-driven framework for coordinated sample analyses, defines the analytical instrumentation and techniques to be applied to the returned sample, provides guidance on the analysis strategy for baseline, overguide, and threshold amounts of returned sample, including a rare or unique lithology, describes the data storage, management, retrieval, and archiving system, establishes a protocol for the implementation of a micro-geographical information system to facilitate co-registration and coordinated analysis of sample science data, outlines the plans for Sample Analysis Readiness Testing, and provides guidance for the transfer of samples from curation to the Sample Analysis Team., Comment: 274 pages

The widespread application of multimodal machine learning models like GPT-4 has revolutionized various research fields including computer vision and natural language processing. However, its implementation in materials informatics remains underexplored, despite the presence of materials data across diverse modalities, such as composition and structure. The effectiveness of machine learning models trained on large calculated datasets depends on the accuracy of calculations, while experimental datasets often have limited data availability and incomplete information. This paper introduces a novel approach to multimodal machine learning in materials science via composition-structure bimodal learning. The proposed COmposition-Structure Bimodal Network (COSNet) is designed to enhance learning and predictions of experimentally measured materials properties that have incomplete structure information. Bimodal learning significantly reduces prediction errors across distinct materials properties including Li conductivity in solid electrolyte, band gap, refractive index, dielectric constant, energy, and magnetic moment, surpassing composition-only learning methods. Furthermore, we identified that data augmentation based on modal availability plays a pivotal role in the success of bimodal learning.

Open material databases storing hundreds of thousands of material structures and their corresponding properties have become the cornerstone of modern computational materials science. Yet, the raw outputs of the simulations, such as the trajectories from molecular dynamics simulations and charge densities from density functional theory calculations, are generally not shared due to their huge size. In this work, we describe a cloud-based platform to facilitate the sharing of raw data and enable the fast post-processing in the cloud to extract new properties defined by the user. As an initial demonstration, our database currently includes 6286 molecular dynamics trajectories for amorphous polymer electrolytes and 5.7 terabytes of data. We create a public analysis library at https://github.com/TRI-AMDD/htp_md to extract multiple properties from the raw data, using both expert designed functions and machine learning models. The analysis is run automatically with computation in the cloud, and results then populate a database that can be accessed publicly. Our platform encourages users to contribute both new trajectory data and analysis functions via public interfaces. Newly analyzed properties will be incorporated into the database. Finally, we create a front-end user interface at https://www.htpmd.matr.io for browsing and visualization of our data. We envision the platform to be a new way of sharing raw data and new insights for the computational materials science community., Comment: 21 pages, 7 figures

Data-driven approaches based on high-throughput capabilities and machine learning hold promise in revolutionizing human-centred materials discovery for sustainability and decarbonization. This Review examines the strengths and limitations of different traditional and emerging approaches to demonstrate their inherent connection and highlight the evolving paradigms of materials design., Breakthroughs in molecular and materials discovery require meaningful outliers to be identified in existing trends. As knowledge accumulates, the inherent bias of human intuition makes it harder to elucidate increasingly opaque chemical and physical principles. Moreover, given the limited manual and intellectual throughput of investigators, these principles cannot be efficiently applied to design new materials across a vast chemical space. Many data-driven approaches, following advances in high-throughput capabilities and machine learning, have tackled these limitations. In this Review, we compare traditional, human-centred methods with state-of-the-art, data-driven approaches to molecular and materials discovery. We first introduce the limitations of human-centred Edisonian, model-system and descriptor-based approaches. We then discuss how data-driven approaches can address these limitations by promoting throughput, reducing cognitive overload and biases, and establishing atomistic understanding that is transferable across a broad chemical space. We examine how high-throughput capabilities can be combined with active learning and inverse design to efficiently optimize materials out of millions or an intractable number of candidates. Lastly, we pinpoint challenges to accelerate future workflows and ultimately enable self-driving platforms, which automate and streamline the optimization of molecules and materials in iterative cycles.

© 2020 National Academy of Sciences. All rights reserved. Molecular ferroelectrics combine electromechanical coupling and electric polarizabilities, offering immense promise in stimuli-dependent metamaterials. Despite such promise, current physical realizations of mechanical metamaterials remain hindered by the lack of rapid-prototyping ferroelectric metamaterial structures. Here, we present a continuous rapid printing strategy for the volumetric deposition of water-soluble molecular ferroelectric metamaterials with precise spatial control in virtually any three-dimensional (3D) geometry by means of an electric-field–assisted additive manufacturing. We demonstrate a scaffold-supported ferroelectric crystalline lattice that enables self-healing and a reprogrammable stiffness for dynamic tuning of mechanical metamaterials with a long lifetime and sustainability. A molecular ferroelectric architecture with resonant inclusions then exhibits adaptive mitigation of incident vibroacoustic dynamic loads via an electrically tunable subwavelength-frequency band gap. The findings shown here pave the way for the versatile additive manufacturing of molecular ferroelectric metamaterials.

© 2021 American Chemical Society. Semitransparent organic solar cells (ST-OSCs) have garnered strong interest for building integrated photovoltaics (PV) and wearable electronics. Although seen as an appealing low-cost PV technology, OSCs suffer from high instability against moisture and gas, which limits their widespread commercialization. A method is proposed to increase significantly the lifetime of inverted ST-OSCs by encapsulating a silver nanowire (AgNW)-based anode with graphene oxide (GO). AgNWs are covered with a GO layer on both sides leading to a sandwich-like structure using only scalable and solution-compatible processes. On one side of the AgNW network, an ultrathin GO layer allows for protecting the AgNWs from the acidic poly(3,4-ethylenedioxythiophene):polystyrene sulfonate (PEDOT:PSS) hole transport layer (HTL) underneath without jeopardizing the device energy level alignment and the Ohmic contact between the HTL and the AgNWs. On the other side, a thicker GO layer at the top aims to prevent the AgNWs from degradation because of atmospheric sulfidation. Such double-sided GO encapsulation offers efficient protection to both the AgNW network and the entire device stack. A fivefold increase in the overall device lifetime without additional encapsulation is demonstrated. Cross-linking the PEDOT:PSS layer with (3-glycidyloxypropyl)trimethoxysilane is also found to be essential to preserve the PEDOT:PSS integrity during device fabrication and achieve high fill factors.

© 2020 American Chemical Society. Bubble nucleation is ubiquitous in gas evolving reactions that are instrumental for a variety of electrochemical systems. Fundamental understanding of the nucleation process, which is critical to system optimization, remains limited as prior works generally focused on the thermodynamics and have not considered the coupling between surface geometries and different forms of transport in the electrolytes. Here, we establish a comprehensive transport-based model framework to identify the underlying mechanism for bubble nucleation on gas evolving electrodes. We account for the complex effects on the electrical field, ion migration, ion diffusion, and gas diffusion arising from surface heterogeneities and gas pockets initiated from surface crevices. As a result, we show that neglecting these effects leads to significant underprediction of the energy needed for nucleation. Our model provides a non-monotonic relationship between the surface cavity size and the overpotential required for nucleation, which is physically more consistent than the monotonic relationship suggested by a traditional thermodynamics-based model. We also identify the significance of the gas diffuse layer thickness, a parameter controlled by external flow fields and overall electrode geometries, which has been largely overlooked in previous models. Our model framework offers guidelines for practical electrochemical systems whereby, without changing the surface chemistry, nucleation on electrodes can be tuned by engineering the cavity size and the gas diffuse layer thickness.

The production of molecular hydrogen by catalyzing water splitting is central to achieving the decarbonization of sustainable fuels and chemical transformations. In this work, a series of structure-making/breaking cations in the electrolyte were investigated as spectator cations in hydrogen evolution and oxidation reactions (HER/HOR) in the pH range of 1 to 14, whose kinetics was found to be altered by up to 2 orders of magnitude by these cations. The exchange current density of HER/HOR was shown to increase with greater structure-making tendency of cations in the order of Cs+ < Rb+ < K+ < Na+ < Li+, which was accompanied by decreasing reorganization energy from the Marcus-Hush-Chidsey formalism and increasing reaction entropy. Invoking the Born model of reorganization energy and reaction entropy, the static dielectric constant of the electrolyte at the electrified interface was found to be significantly lower than that of bulk, decreasing with the structure-making tendency of cations at the negatively charged Pt surface. The physical origin of cation-dependent HER/HOR kinetics can be rationalized by an increase in concentration of cations on the negatively charged Pt surface, altering the interfacial water structure and the H-bonding network, which is supported by classical molecular dynamics simulation and surface-enhanced infrared absorption spectroscopy. This work highlights immense opportunities to control the reaction rates by tuning interfacial structures of cation and solvents.

© 2020 Author(s). Evaporation plays a critical role in a range of technologies that power and sustain our society. Wicks are widely used as passive, capillary-fed evaporators, attracting much interest since these devices are highly efficient, compact, and thermally stable. While wick-based evaporators can be further improved with advanced materials and fabrication techniques, modeling of heat and mass transport at the device level is vital for guiding these innovations. In this perspective, we present the design and optimization of capillary-fed, thin film evaporation devices through a heat and mass transfer lens. This modeling framework can guide future research into materials innovations, fabrication of novel architectures, and systems design/optimization for next generation, high-performance wick-based evaporators. Furthermore, we describe specific challenges and opportunities for the fundamental understanding of evaporation physics. Finally, we apply our modeling framework to the analysis of two important applications-solar vapor generation and electronics cooling devices.

Copyright © 2020 American Chemical Society. We investigate the charge densities, lithium intercalation potentials, and Li-diffusion barrier energies of LixNiO2 (0.0 < x < 1.0) system using the diffusion quantum Monte Carlo (DMC) method. We find an average redox potential of 4.1(2) eV and a Li-diffusion barrier energy of 0.39(3) eV with DMC. Comparisoin of the charge densities from DMC and density functional theory (DFT) and show that local and semilocal DFT functionals yield spin polarization densities with an incorrect sign on the oxygen atoms. The SCAN functional and Hubbard-U correction improves the polarization density around Ni and O atoms, resulting in smaller deviations from the DMC densities. DMC accurately captures the many-body nature of Ni-O bonding, hence yielding accurate lithium intercalation voltages, polarization densities, and reaction barriers. ©

© 2018 American Chemical Society. We reveal a self-limiting mechanism during the formation of a specific type of circular nanopore in monolayer WS2 that limits its diameter to sub-nm. A single W atom vacancy (triangular nanopore) is transformed into the self-limiting nanopore (SLNP) through the atomic restructuring of S atoms around the area, reducing the number of dangling bonds at the nanopore edge by shifting them further in-plane with W-W bonding instead. Bond rotations in WS2 help accommodate the electron beam induced atomic loss and ensure the stability of the SLNP. The SLNP shows significant improvement in diameter stability during electron beam irradiation compared to other triangular nanopores in WS2 that typically continue to expand in diameter during atom loss. The atomic structure of these SLNPs is studied using aberration-corrected scanning transmission electron microscopy with an in situ heating holder, revealing that the SLNPs are mostly formed at a temperature of -500 °C, which is a balance between thermally activated S vacancy diffusion and sufficient S vacancy density to initiate local atomic reconstruction. At higher temperatures (i.e., 1000 °C), S vacancies quickly migrate away into long line vacancies, resulting in low S vacancy density and rapidly expanding holes generated at the edges of the line vacancies. At room temperature, S vacancy migration is low and vacancy density is very high, which limits atomic reconstruction, and instead many small holes open up. These results provide insights into the factors that lead to uniform sized nanopores in the sub-nm range in transition-metal dichalcogenides.

© The Royal Society of Chemistry. Half-Heusler (HH) compounds are among the most promising thermoelectric (TE) materials for large-scale applications due to their superior properties such as high power factor, excellent mechanical and thermal reliability, and non-toxicity. Their only drawback is the remaining-high lattice thermal conductivity. Various mechanisms were reported with claimed effectiveness to enhance the phonon scattering of HH compounds including grain-boundary scattering, phase separation, and electron-phonon interaction. In this work, however, we show that point-defect scattering has been the dominant mechanism for phonon scattering other than the intrinsic phonon-phonon interaction for ZrCoSb and possibly many other HH compounds. Induced by the charge-compensation effect, the formation of Co/4d Frenkel point defects is responsible for the drastic reduction of lattice thermal conductivity in ZrCoSb1-xSnx. Our work systematically depicts the phonon scattering profile of HH compounds and illuminates subsequent material optimizations. This journal is

© 2019, The Author(s). Electrochemical stability windows of electrolytes largely determine the limitations of operating regimes of lithium-ion batteries, but the degradation mechanisms are difficult to characterize and poorly understood. Using computational quantum chemistry to investigate the oxidative decomposition that govern voltage stability of multi-component organic electrolytes, we find that electrolyte decomposition is a process involving the solvent and the salt anion and requires explicit treatment of their coupling. We find that the ionization potential of the solvent-anion system is often lower than that of the isolated solvent or the anion. This mutual weakening effect is explained by the formation of the anion-solvent charge-transfer complex, which we study for 16 anion-solvent combinations. This understanding of the oxidation mechanism allows the formulation of a simple predictive model that explains experimentally observed trends in the onset voltages of degradation of electrolytes near the cathode. This model opens opportunities for rapid rational design of stable electrolytes for high-energy batteries.

Understanding and broad screening Li interaction energetics with surfaces are key to the development of materials for a wide range of applications including Li-based electrochemical capacitors, Li sensors, Li separation membranes, and Li-ion batteries. In this work, we build a high-throughput screening scheme to screen Li adsorption energetics on 2D metallic materials. First, density functional theory and graph convolution networks are utilized to calculate the minimum Li adsorption energies for some 2D metallic materials. The data is then used to find a dependence of the minimum Li adsorption energies on the sum of ionization potential, work function of the 2D metal, and coupling energy between Li+ and substrate, and the dependence is used to screen all 2D metallic materials. Physics-simplified learning by splitting the property into different contributions and learning or calculating each component is shown to have higher accuracy and transferability for machine learning of complex materials properties.

© 2019, The Author(s). Understanding the dynamical processes that govern the performance of functional materials is essential for the design of next generation materials to tackle global energy and environmental challenges. Many of these processes involve the dynamics of individual atoms or small molecules in condensed phases, e.g. lithium ions in electrolytes, water molecules in membranes, molten atoms at interfaces, etc., which are difficult to understand due to the complexity of local environments. In this work, we develop graph dynamical networks, an unsupervised learning approach for understanding atomic scale dynamics in arbitrary phases and environments from molecular dynamics simulations. We show that important dynamical information, which would be difficult to obtain otherwise, can be learned for various multi-component amorphous material systems. With the large amounts of molecular dynamics data generated every day in nearly every aspect of materials design, this approach provides a broadly applicable, automated tool to understand atomic scale dynamics in material systems.

© 2018 American Chemical Society. Nanoscale pore formation on chemical vapor deposition grown monolayer MoS2 is achieved using oxygen plasma etching through a nanoporous silicon mask, creating round pores of â70 nm in diameter. The microscale areas with high porosity were successfully patterned via the usage of silicon masks. Thermal annealing in air after the pore formation in the monolayers results in the gradual enlargement of the pores, providing an effective method of controlling edge-to-area ratio of MoS2 crystals. The photoluminescence of the nanoporous MoS2 exhibits rapid increase and blue-shift due to facile p-doping during the thermal annealing process compared to pristine MoS2. This method of fabricating porous transition metal dichalcogenide layers with controlled edge densities presents opportunities in various applications that require atomically thin nanomaterials with controlled pore density and edge sites, such as filtration, electrocatalysis, and sensing.

Understanding and optimizing the key mechanisms used in the synthesis of pitch-based carbon fibers (CFs) are challenging, because unlike polyacrylonitrile-based CFs, the feedstock for pitch-based CFs is chemically heterogeneous, resulting in complex fabrication leading to inconsistency in the final properties. In this work, we use molecular dynamics simulations to explore the processing and chemical phase space through a framework of CF models to identify their effects on elastic performance. The results are in excellent agreement with experiments. We find that density, followed by alignment, and functionality of the molecular constituents dictate the CF mechanical properties more strongly than their size and shape. Last, we propose a previously unexplored fabrication route for high-modulus CFs. Unlike graphitization, this results in increased sp3 fraction, achieved via generating high-density CFs. In addition, the high sp3 fraction leads to the fabrication of CFs with isometric compressive and tensile moduli, enabling their potential applications for compressive loading.

© 2018 American Chemical Society. High-energy irradiation of materials can lead to void formation due to the aggregation of vacancies, reducing the local stress in the system. Studying void formation and its interplay with vacancy clusters in bulk materials at the atomic level has been challenging due to the thick volume of 3D materials, which generally limits high-resolution transmission electron microscopy. The thin nature of 2D materials is ideal for studying fundamental material defects such as dislocations and crack tips and has potential to reveal void formation by vacancy aggregation in detail. Here, using atomic-resolution in situ transmission electron microscopy of 2D monolayer MoS2, we capture rapid thermal diffusion of S vacancies into ultralong (∼60 nm) 1D S vacancy channels that initiate void formation at high vacancy densities. Strong interactions are observed between the 1D channels and void growth, whereby Mo and S atoms are funneled back and forth between the void edge and the crystal surface to enable void enlargement. Preferential void growth up to 100 nm is shown to occur by rapid digestion of 1D S vacancy channels as they make contact. These results reveal the atomistic mechanisms behind void enlargement in 2D materials under intense high-energy irradiation at high temperatures and the existence of ultralong 1D vacancy channels. This knowledge may also help improve the understanding of void formation in other systems such as nuclear materials, where direct visualization is challenging due to 3D bulk volume.

Hygroscopic hydrogels hold significant promise for high-performance atmospheric water harvesting, passive cooling, and thermal management. However, a mechanistic understanding of the sorption kinetics of hygroscopic hydrogels remains elusive, impeding an optimized design and broad adoption. Here, we develop a generalized two-concentration model (TCM) to describe the sorption kinetics of hygroscopic hydrogels, where vapor transport in hydrogel micropores and liquid transport in polymer nanopores are coupled through the sorption at the interface. We show that the liquid transport due to the chemical potential gradient in the hydrogel plays an important role in the fast kinetics. The high water uptake is attributed to the expansion of hydrogel during liquid transport. Moreover, we identify key design parameters governing the kinetics, including the initial porosity, hydrogel thickness, and shear modulus. This work provides a generic framework of sorption kinetics, which bridges the knowledge gap between the fundamental transport and practical design of hygroscopic hydrogels.

The production of molecular hydrogen by catalyzing water splitting is central to achieving the decarbonization of sustainable fuels and chemical transformations. In this work, a series of structure-making/breaking cations in the electrolyte were investigated as spectator cations in hydrogen evolution and oxidation reactions (HER/HOR) in the pH range of 1 to 14, whose kinetics was found to be altered by up to 2 orders of magnitude by these cations. The exchange current density of HER/HOR was shown to increase with greater structure-making tendency of cations in the order of Cs+ < Rb+ < K+ < Na+ < Li+, which was accompanied by decreasing reorganization energy from the Marcus-Hush-Chidsey formalism and increasing reaction entropy. Invoking the Born model of reorganization energy and reaction entropy, the static dielectric constant of the electrolyte at the electrified interface was found to be significantly lower than that of bulk, decreasing with the structure-making tendency of cations at the negatively charged Pt surface. The physical origin of cation-dependent HER/HOR kinetics can be rationalized by an increase in concentration of cations on the negatively charged Pt surface, altering the interfacial water structure and the H-bonding network, which is supported by classical molecular dynamics simulation and surface-enhanced infrared absorption spectroscopy. This work highlights immense opportunities to control the reaction rates by tuning interfacial structures of cation and solvents.

Adsorption energy differences can lead to selectivity.

Understanding and optimizing the key mechanisms used in the synthesis of pitch-based carbon fibers (CFs) are challenging, because unlike polyacrylonitrile-based CFs, the feedstock for pitch-based CFs is chemically heterogeneous, resulting in complex fabrication leading to inconsistency in the final properties. In this work, we use molecular dynamics simulations to explore the processing and chemical phase space through a framework of CF models to identify their effects on elastic performance. The results are in excellent agreement with experiments. We find that density, followed by alignment, and functionality of the molecular constituents dictate the CF mechanical properties more strongly than their size and shape. Last, we propose a previously unexplored fabrication route for high-modulus CFs. Unlike graphitization, this results in increased sp3 fraction, achieved via generating high-density CFs. In addition, the high sp3 fraction leads to the fabrication of CFs with isometric compressive and tensile moduli, enabling their potential applications for compressive loading.

Supramolecular engineering bridges molecular assembly with macromolecular charge-transfer salts, promising the design to construct supramolecular architectures that integrate cooperative properties difficult or impossible to find in conventional lattices. Here, we report the crystal engineering design and kinetic growth of one-dimensional supramolecular wires composed of bis(ethylenedithio)tetrathiafulvalene (ET+) cation and polymeric Cu[N(CN)2]2- anion. A bulk ferromagnetic order is discovered for filling up the gap where strong ferromagnetism is missing in such ET molecule-based charge-transfer salts. Metallicity is induced by electric current from the semiconducting wire, which is attributed to strain effect by tuning its close molecular contact. This structural feature is evidenced through the combination of various mechanistic spectroscopic studies. Electric dipole is established from the close molecular contacts and is suggestive to stabilize ferromagnetic spin interaction through anions bridging spin sites. The breakthrough shown here provides a pathway to explore low-dimensional supramolecular materials exhibiting strong electron correlation, metallicity, and ferromagnetism.

The production of molecular hydrogen by catalyzing water splitting is central to achieving the decarbonization of sustainable fuels and chemical transformations. In this work, a series of structure-making/breaking cations in the electrolyte were investigated as spectator cations in hydrogen evolution and oxidation reactions (HER/HOR) in the pH range of 1 to 14, whose kinetics was found to be altered by up to 2 orders of magnitude by these cations. The exchange current density of HER/HOR was shown to increase with greater structure-making tendency of cations in the order of Cs+ < Rb+ < K+ < Na+ < Li+, which was accompanied by decreasing reorganization energy from the Marcus-Hush-Chidsey formalism and increasing reaction entropy. Invoking the Born model of reorganization energy and reaction entropy, the static dielectric constant of the electrolyte at the electrified interface was found to be significantly lower than that of bulk, decreasing with the structure-making tendency of cations at the negatively charged Pt surface. The physical origin of cation-dependent HER/HOR kinetics can be rationalized by an increase in concentration of cations on the negatively charged Pt surface, altering the interfacial water structure and the H-bonding network, which is supported by classical molecular dynamics simulation and surface-enhanced infrared absorption spectroscopy. This work highlights immense opportunities to control the reaction rates by tuning interfacial structures of cation and solvents.

The electron charge density distribution of materials is one of the key quantities in computational materials science as theoretically it determines the ground state energy and practically it is used in many materials analyses. However, the scaling of density functional theory calculations with number of atoms limits the usage of charge-density-based calculations and analyses. Here we introduce a machine-learning scheme with local-environment-based graphs and graph convolutional neural networks to predict charge density on grid points from the crystal structure. We show the accuracy of this scheme through a comparison of predicted charge densities as well as properties derived from the charge density, and that the scaling is O(N). More importantly, the transferability is shown to be high with respect to different compositions and structures, which results from the explicit encoding of geometry., United States. Department of Energy. Office of Science (Contract DE-AC02-05CH11231), National Science Foundation (U.S.) (Grant ACI-1053575)

© 2018 American Chemical Society. High-energy irradiation of materials can lead to void formation due to the aggregation of vacancies, reducing the local stress in the system. Studying void formation and its interplay with vacancy clusters in bulk materials at the atomic level has been challenging due to the thick volume of 3D materials, which generally limits high-resolution transmission electron microscopy. The thin nature of 2D materials is ideal for studying fundamental material defects such as dislocations and crack tips and has potential to reveal void formation by vacancy aggregation in detail. Here, using atomic-resolution in situ transmission electron microscopy of 2D monolayer MoS2, we capture rapid thermal diffusion of S vacancies into ultralong (∼60 nm) 1D S vacancy channels that initiate void formation at high vacancy densities. Strong interactions are observed between the 1D channels and void growth, whereby Mo and S atoms are funneled back and forth between the void edge and the crystal surface to enable void enlargement. Preferential void growth up to 100 nm is shown to occur by rapid digestion of 1D S vacancy channels as they make contact. These results reveal the atomistic mechanisms behind void enlargement in 2D materials under intense high-energy irradiation at high temperatures and the existence of ultralong 1D vacancy channels. This knowledge may also help improve the understanding of void formation in other systems such as nuclear materials, where direct visualization is challenging due to 3D bulk volume.

© 2018 American Chemical Society. Wearable conformal electronics are essential components for next-generation humanlike sensing devices that can accurately respond to external stimuli in nonplanar and dynamic surfaces. However, to explore this potential, it is indispensable to achieve the desired level of deformability and charge-transport mobility in strain-accommodating soft semiconductors. Here, we show pseudo-two-dimensional freestanding conjugated polymer heterojunction nanosheets integrated into substrate-free conformal electronics owing to their exceptional crystalline controlled charge transport and high level of mechanical strength. These freestanding and mechanical robust polymer nanosheets can be adapted into a variety of artificial structured surfaces such as fibers, squares, circles, etc., which produce large-area stretchable conformal charge-transfer sensors for real-time static and dynamic monitoring.

© The Royal Society of Chemistry 2020. Electrically conductive membranes have shown tremendous promise in the treatment of water, due to their ability to overcome certain limitations that traditional membranes face. Membranes that can simultaneously serve as electrodes in an electrochemical cell can reduce membrane fouling, degrade contaminants, and even enhance permeability and selectivity in certain separations. Carbonaceous conductive materials are particularly promising for the creation of conductive membranes, due to their high surface area, high conductivity, excellent chemical and electrochemical stability, relative inertness, and wide chemical tunability, all of which are crucial in realizing the full benefits of electrically conductive membranes. In this perspective, we review recent work using carbonaceous conductive membranes for fouling mitigation, contaminant degradation, and permeability and selectivity enhancement, noting the advantages of carbonaceous conductive membranes in each and highlighting areas that we believe are promising future research directions. Based on current advancements, future work should be focused on disentangling mechanisms of fouling reduction, overcoming mass transfer limitations and testing more relevant model systems for contaminant degradation, and identifying large-scale application areas that can benefit from permselective functionality. We also perform a technoeconomic analysis for the addition of an electrified setup to an example desalination plant. Our analysis suggests that the future work towards commercialization should focus on improving the fouling reduction from electrification and reducing electrical energy consumption to justify the application of conductive carbonaceous membranes in water filtration and separation processes.

© 2019, The Author(s). Understanding the dynamical processes that govern the performance of functional materials is essential for the design of next generation materials to tackle global energy and environmental challenges. Many of these processes involve the dynamics of individual atoms or small molecules in condensed phases, e.g. lithium ions in electrolytes, water molecules in membranes, molten atoms at interfaces, etc., which are difficult to understand due to the complexity of local environments. In this work, we develop graph dynamical networks, an unsupervised learning approach for understanding atomic scale dynamics in arbitrary phases and environments from molecular dynamics simulations. We show that important dynamical information, which would be difficult to obtain otherwise, can be learned for various multi-component amorphous material systems. With the large amounts of molecular dynamics data generated every day in nearly every aspect of materials design, this approach provides a broadly applicable, automated tool to understand atomic scale dynamics in material systems.

© 2018 American Chemical Society. Next generation batteries based on lithium (Li) metal anodes have been plagued by the dendritic electrodeposition of Li metal on the anode during cycling, resulting in short circuit and capacity loss. Suppression of dendritic growth through the use of solid electrolytes has emerged as one of the most promising strategies for enabling the use of Li metal anodes. We perform a computational screening of over 12000 inorganic solids based on their ability to suppress dendrite initiation in contact with Li metal anode. Properties for mechanically isotropic and anisotropic interfaces that can be used in stability criteria for determining the propensity of dendrite initiation are usually obtained from computationally expensive first-principles methods. In order to obtain a large data set for screening, we use machine-learning models to predict the mechanical properties of several new solid electrolytes. The machine-learning models are trained on purely structural features of the material, which do not require any first-principles calculations. We train a graph convolutional neural network on the shear and bulk moduli because of the availability of a large training data set with low noise due to low uncertainty in their first-principles-calculated values. We use gradient boosting regressor and kernel ridge regression to train the elastic constants, where the choice of the model depends on the size of the training data and the noise that it can handle. The material stiffness is found to increase with an increase in mass density and ratio of Li and sublattice bond ionicity, and decrease with increase in volume per atom and sublattice electronegativity. Cross-validation/test performance suggests our models generalize well. We predict over 20 mechanically anisotropic interfaces between Li metal and four solid electrolytes which can be used to suppress dendrite growth. Our screened candidates are generally soft and highly anisotropic, and present

© 2018 American Chemical Society. We reveal a self-limiting mechanism during the formation of a specific type of circular nanopore in monolayer WS2 that limits its diameter to sub-nm. A single W atom vacancy (triangular nanopore) is transformed into the self-limiting nanopore (SLNP) through the atomic restructuring of S atoms around the area, reducing the number of dangling bonds at the nanopore edge by shifting them further in-plane with W-W bonding instead. Bond rotations in WS2 help accommodate the electron beam induced atomic loss and ensure the stability of the SLNP. The SLNP shows significant improvement in diameter stability during electron beam irradiation compared to other triangular nanopores in WS2 that typically continue to expand in diameter during atom loss. The atomic structure of these SLNPs is studied using aberration-corrected scanning transmission electron microscopy with an in situ heating holder, revealing that the SLNPs are mostly formed at a temperature of -500 °C, which is a balance between thermally activated S vacancy diffusion and sufficient S vacancy density to initiate local atomic reconstruction. At higher temperatures (i.e., 1000 °C), S vacancies quickly migrate away into long line vacancies, resulting in low S vacancy density and rapidly expanding holes generated at the edges of the line vacancies. At room temperature, S vacancy migration is low and vacancy density is very high, which limits atomic reconstruction, and instead many small holes open up. These results provide insights into the factors that lead to uniform sized nanopores in the sub-nm range in transition-metal dichalcogenides.

© 2018 American Chemical Society. Nanoscale pore formation on chemical vapor deposition grown monolayer MoS2 is achieved using oxygen plasma etching through a nanoporous silicon mask, creating round pores of â70 nm in diameter. The microscale areas with high porosity were successfully patterned via the usage of silicon masks. Thermal annealing in air after the pore formation in the monolayers results in the gradual enlargement of the pores, providing an effective method of controlling edge-to-area ratio of MoS2 crystals. The photoluminescence of the nanoporous MoS2 exhibits rapid increase and blue-shift due to facile p-doping during the thermal annealing process compared to pristine MoS2. This method of fabricating porous transition metal dichalcogenide layers with controlled edge densities presents opportunities in various applications that require atomically thin nanomaterials with controlled pore density and edge sites, such as filtration, electrocatalysis, and sensing.

Refractory metals and their carbides possess extraordinary chemical and temperature resilience and exceptional mechanical strength. Yet, they are notoriously difficult to employ in additive manufacturing, due to the high temperatures needed for processing. State of the art approaches to manufacture these materials generally require either a high-energy laser or electron beam as well as ventilation to protect the metal powder from combustion. Here, we present a versatile manufacturing process that utilizes tar as both a light absorber and antioxidant binder to sinter thin films of aluminum, copper, nickel, molybdenum, and tungsten powder using a low power (<2W) CO2 laser in air. Films of sintered Al/Cu/Ni metals have sheet resistances of ∼10-1 ohm/sq, while laser-sintered Mo/W-tar thin films form carbide phases. Several devices are demonstrated, including laser-sintered porous copper with a stable response to large strain (3.0) after 150 cycles, and a laserprocessed Mo/MoC(1-x) filament that reaches T ∼1000 °C in open air at 12 V. These results show that tar-mediated laser sintering represents a possible low energy, cost-effective route for engineering refractory materials and one that can easily be extended to additive manufacturing processes.

© 2019, The Author(s). Ultrathin transition metal carbides with high capacity, high surface area, and high conductivity are a promising family of materials for applications from energy storage to catalysis. However, large-scale, cost-effective, and precursor-free methods to prepare ultrathin carbides are lacking. Here, we demonstrate a direct pattern method to manufacture ultrathin carbides (MoCx, WCx, and CoCx) on versatile substrates using a CO2 laser. The laser-sculptured polycrystalline carbides (macroporous, ~10–20 nm wall thickness, ~10 nm crystallinity) show high energy storage capability, hierarchical porous structure, and higher thermal resilience than MXenes and other laser-ablated carbon materials. A flexible supercapacitor made of MoCx demonstrates a wide temperature range (−50 to 300 °C). Furthermore, the sculptured microstructures endow the carbide network with enhanced visible light absorption, providing high solar energy harvesting efficiency (~72 %) for steam generation. The laser-based, scalable, resilient, and low-cost manufacturing process presents an approach for construction of carbides and their subsequent applications.

© 2018 American Chemical Society. Understanding the origins of the excessive Stokes shift in the lead chalcogenides family of colloidal quantum dots (CQDs) is of great importance at both the fundamental and applied levels; however, our current understanding is far from satisfactory. Here, utilizing a combination of ab initio computations and UV-vis and photoluminescence measurements, we investigated the contributions to the Stokes shift from polydispersity, ligands, and defects in PbS CQDs. The key results are as follows: (1) The size and energetic disorder of a polydisperse CQD film increase the Stokes shift by 20 to 50 meV compared to that of an isolated CQD; (2) Franck-Condon (FC) shifts increase as the electronegativities of the ligands increase, but the variations are small (<15 meV). (3) Unlike the aforementioned two minor factors, the presence of certain intrinsic defects such as VCl+ (in Cl-passivated CQDs) can cause substantial electron density localization of the band edge states and consequent large FC shifts (100s of meV). This effect arising from defects can explain the excessive Stokes shifts in PbS CQDs and improve our understanding of the optical properties of PbS CQDs.

Copyright © 2020 American Chemical Society. We investigate the charge densities, lithium intercalation potentials, and Li-diffusion barrier energies of LixNiO2 (0.0 < x < 1.0) system using the diffusion quantum Monte Carlo (DMC) method. We find an average redox potential of 4.1(2) eV and a Li-diffusion barrier energy of 0.39(3) eV with DMC. Comparisoin of the charge densities from DMC and density functional theory (DFT) and show that local and semilocal DFT functionals yield spin polarization densities with an incorrect sign on the oxygen atoms. The SCAN functional and Hubbard-U correction improves the polarization density around Ni and O atoms, resulting in smaller deviations from the DMC densities. DMC accurately captures the many-body nature of Ni-O bonding, hence yielding accurate lithium intercalation voltages, polarization densities, and reaction barriers. ©

© 2019, The Author(s). Electrochemical stability windows of electrolytes largely determine the limitations of operating regimes of lithium-ion batteries, but the degradation mechanisms are difficult to characterize and poorly understood. Using computational quantum chemistry to investigate the oxidative decomposition that govern voltage stability of multi-component organic electrolytes, we find that electrolyte decomposition is a process involving the solvent and the salt anion and requires explicit treatment of their coupling. We find that the ionization potential of the solvent-anion system is often lower than that of the isolated solvent or the anion. This mutual weakening effect is explained by the formation of the anion-solvent charge-transfer complex, which we study for 16 anion-solvent combinations. This understanding of the oxidation mechanism allows the formulation of a simple predictive model that explains experimentally observed trends in the onset voltages of degradation of electrolytes near the cathode. This model opens opportunities for rapid rational design of stable electrolytes for high-energy batteries.

© 2018 IOP Publishing Ltd. Solar thermal fuels (STFs) are an unconventional paradigm for solar energy conversion and storage which is attracting renewed attention. In this concept, a material absorbs sunlight and stores the energy chemically via an induced structural change, which can later be reversed to release the energy as heat. An example is the azobenzene molecule which has a cis-trans photoisomerization with these properties, and can be tuned by chemical substitution and attachment to templates such as carbon nanotubes, small molecules, or polymers. By analogy to the Shockley-Queisser limit for photovoltaics, we analyze the maximum attainable efficiency for STFs from fundamental thermodynamic considerations. Microscopic reversibility provides a bound on the quantum yield of photoisomerization due to fluorescence, regardless of details of photochemistry. We emphasize the importance of analyzing the free energy, not just enthalpy, of the metastable molecules, and find an efficiency limit for conversion to stored chemical energy equal to the Shockley-Queisser limit. STF candidates from a recent high-throughput search are analyzed in light of the efficiency limit.

Polymer electrolytes are promising candidates for the next generation lithium-ion battery technology. Large scale screening of polymer electrolytes is hindered by the significant cost of molecular dynamics (MD) simulation in amorphous systems: the amorphous structure of polymers requires multiple, repeated sampling to reduce noise and the slow relaxation requires long simulation time for convergence. Here, we accelerate the screening with a multi-task graph neural network that learns from a large amount of noisy, unconverged, short MD data and a small number of converged, long MD data. We achieve accurate predictions of 4 different converged properties and screen a space of 6247 polymers that is orders of magnitude larger than previous computational studies. Further, we extract several design principles for polymer electrolytes and provide an open dataset for the community. Our approach could be applicable to a broad class of material discovery problems that involve the simulation of complex, amorphous materials., Comment: 29 pages, 6 figures + supplementary information

The nanoporous and tunable nature of metal-organic frameworks (MOFs) has made them promising adsorbents for water adsorption applications such as water harvesting and adsorptive heat pumps. In these applications, water adsorption properties in MOFs play a crucial role. However, understanding their structural defects and how defects influence adsorption thermodynamics remains limited to date. In this work, by employing Monte Carlo techniques and first-principle density functional theory calculations, we investigate the effect of defects on the water adsorption properties in MOF-801 structures at an atomic level. Our calculations show that the adsorption isotherm in perfect MOF-801 (without defects) greatly deviates from that measured experimentally. With the introduction of defects with a high density, a reasonably good agreement can be achieved, suggesting that a high defect density in MOF-801 may be responsible for its hydrophilic adsorptive behaviors. Further, water adsorption properties in MOF-801 structures are found to depend on the spatial configuration of defects, and water condensation in nanoporous MOF-801 is identified to occur preferentially along the «110» direction. Detailed structural characteristics (accessible volume, etc.) of MOF-801 structures and the adsorption energetics of water in the frameworks are also studied and correlated with the computed adsorption isotherms. Our findings reveal important insights into the role of defects, offering a microscopic picture to help facilitate the rational design of better MOFs for water adsorption applications., Office of Science of the U.S. Department of Energy (DE-AC02-05CH11231), National Science Foundation (ACI-1548562)