ML-Aided Computational Screening of 2D Materials for Photocatalytic Water Splitting.

The exploration of two-dimensional (2D) materials with exceptional physical and chemical properties is essential for the advancement of solar water splitting technologies. However, the discovery of 2D materials is currently heavily reliant on fragmented studies with limited opportunities for fine-tuning the chemical composition and electronic features of compounds. Starting from the V2DB digital library as a resource of 2D materials, we set up and execute a funnel approach that incorporates multiple screening steps to uncover potential candidates for photocatalytic water splitting. The initial screening step is based upon machine learning (ML) predicted properties, and subsequent steps involve first-principles modeling of increasing complexity, going from density functional theory (DFT) to hybrid-DFT to GW calculations. Ensuring that at each stage more complex calculations are only applied to the most promising candidates, our study introduces an effective screening methodology that may serve as a model for accelerating 2D materials discovery within a large chemical space. Our screening process yields a selection of 11 promising 2D photocatalysts.

Towards the 4 V-class n-type organic lithium-ion positive electrode materials: the case of conjugated triflimides and cyanamides.

Organic electrode materials have garnered a great deal of interest owing to their sustainability, cost-efficiency, and design flexibility metrics. Despite numerous endeavors to fine-tune their redox potential, the pool of organic positive electrode materials with a redox potential above 3 V versus Li+/Li0, and maintaining air stability in the Li-reservoir configuration remains limited. This study expands the chemical landscape of organic Li-ion positive electrode chemistries towards the 4 V-class through molecular design based on electron density depletion within the redox center via the mesomeric effect of electron-withdrawing groups (EWGs). This results in the development of novel families of conjugated triflimides and cyanamides as high-voltage electrode materials for organic lithium-ion batteries. These are found to exhibit ambient air stability and demonstrate reversible electrochemistry with redox potentials spanning the range of 3.1 V to 3.8 V (versus Li+/Li0), marking the highest reported values so far within the realm of n-type organic chemistries. Through comprehensive structural analysis and extensive electrochemical studies, we elucidate the relationship between the molecular structure and the ability to fine-tune the redox potential. These findings offer promising opportunities to customize the redox properties of organic electrodes, bridging the gap with their inorganic counterparts for application in sustainable and eco-friendly electrochemical energy storage devices.

Discovery of lead quinone cathode materials for Li-ion batteries.

Organic cathode materials are attractive candidates for the development of high-performance Li-ion batteries (LIBs). The chemical space of candidate molecules is too vast to be explored solely by experiments; however, it can be systematically explored by a high-throughput computational search that incorporates a spectrum of screening techniques. Here, we present a time- and resource-efficient computational scheme that incorporates machine learning and semi-empirical quantum mechanical methods to study the chemical space of approximately 200 000 quinone-based molecules for use as cathode materials in LIBs. By performing an automated search on a commercial vendor database, computing battery-relevant properties such as redox potential, gravimetric charge capacity, gravimetric energy density, and synthetic complexity score, and evaluating the structural integrity upon the lithiation process, a total of 349 molecules were identified as potentially high-performing cathode materials for LIBs. The chemical space of the screened candidates was visualized using dimensionality reduction methods with the aim of further downselecting the best candidates for experimental validation. One such directly purchasable candidate, 1,4,9,10-anthracenetetraone, was analyzed through cyclic voltammetry experiments. The measured redox potentials of the two lithiation steps, , of 3.3 and 2.4 V, were in good agreement with the predicted redox potentials, , of 3.2 and 2.3 V vs. Li/Li+, respectively. Lastly, to lay out the principles for rational design of quinone-based cathode materials beyond the current work, we constructed and discussed the quantitative structure property relationships of quinones based on the data generated from the calculations.

Polymer Vesicles with Integrated Photothermal Responsiveness.

Functionalized polymer vesicles have been proven to be highly promising in biomedical applications due to their good biocompatibility, easy processability, and multifunctional responsive capacities. However, photothermal-responsive polymer vesicles triggered by near-infrared (NIR) light have not been widely reported until now. Herein, we propose a new strategy for designing NIR light-mediated photothermal polymer vesicles. A small molecule (PTA) with NIR-triggered photothermal features was synthesized by combining a D-D'-A-D'-D configuration framework with a molecular rotor function (TPE). The feasibility of the design strategy was demonstrated through density functional theory calculations. PTA moieties were introduced in the hydrophobic segment of a poly(ethylene glycol)-poly(trimethylene carbonate) block copolymer, of which the carbonate monomers were modified in the side chain with an active ester group. The amphiphilic block copolymers (PEG44-PTA2) were then used as building blocks for the self-assembly of photothermal-responsive polymer vesicles. The new class of functionalized polymer vesicles inherited the NIR-mediated high photothermal performance of the photothermal agent (PTA). After NIR laser irradiation for 10 min, the temperature of the PTA-Ps aqueous solution was raised to 56 °C. The photothermal properties and bilayer structure of PTA-Ps after laser irradiation were still intact, which demonstrated that they could be applied as a robust platform in photothermal therapy. Besides their photothermal performance, the loading capacity of PTA-Ps was investigated as well. Hydrophobic cargo (Cy7) and hydrophilic cargo (Sulfo-Cy5) were successfully encapsulated in the PTA-Ps. These properties make this new class of functionalized polymer vesicles an interesting platform for synergistic therapy in anticancer treatment.

Virtual screening of organic quinones as cathode materials for sodium-ion batteries.

High-throughput virtual screening (HTVS) has been increasingly applied as an effective approach to find candidate materials for energy applications. We performed a HTVS study, which is powered by: (i) automated virtual screening library generation, (ii) automated search on a readily purchasable chemical space of quinone-based compounds, and (iii) computed physicochemical descriptors for the prediction of key battery-related features of compounds, including the reduction potential, gravimetric energy density, gravimetric charge capacity, and molecular stability. From the initial virtual library of approximately 450k molecules, a total of 326 compounds have been identified as commercially available. Among them, 289 of the molecules are predicted to be stable for the sodiation reactions that take place at the sodium-ion battery cathodes. To study the behaviour of molecules over time at room temperature, we performed molecular dynamics simulations on a group of sodiated product molecules, which was narrowed down to 21 quinones after scrutinizing the key battery performance indicators. As a result, 17 compounds are suggested for validation as candidate cathode materials in sodium-ion batteries.

RedDB, a computational database of electroactive molecules for aqueous redox flow batteries.

An increasing number of electroactive compounds have recently been explored for their use in high-performance redox flow batteries for grid-scale energy storage. Given the vast and highly diverse chemical space of the candidate compounds, it is alluring to access their physicochemical properties in a speedy way. High-throughput virtual screening approaches, which use powerful combinatorial techniques for systematic enumerations of large virtual chemical libraries and respective property evaluations, are indispensable tools for an agile exploration of the designated chemical space. Herein, RedDB: a computational database that contains 31,618 molecules from two prominent classes of organic electroactive compounds, quinones and aza-aromatics, has been presented. RedDB incorporates miscellaneous physicochemical property information of the compounds that can potentially be employed as battery performance descriptors. RedDB's development steps, including: (i) chemical library generation, (ii) molecular property prediction based on quantum chemical calculations, (iii) aqueous solubility prediction using machine learning, and (iv) data processing and database creation, have been described.

OPTIMADE, an API for exchanging materials data.

The Open Databases Integration for Materials Design (OPTIMADE) consortium has designed a universal application programming interface (API) to make materials databases accessible and interoperable. We outline the first stable release of the specification, v1.0, which is already supported by many leading databases and several software packages. We illustrate the advantages of the OPTIMADE API through worked examples on each of the public materials databases that support the full API specification.

High-Throughput Computational Screening of Cubic Perovskites for Solid Oxide Fuel Cell Cathodes.

It is a present-day challenge to design and develop oxygen-permeable solid oxide fuel cell (SOFC) electrode and electrolyte materials that operate at low temperatures. Herein, by performing high-throughput density functional theory calculations, oxygen vacancy formation energy, Evac, data for a pool of all-inorganic ABO3 and AI0.5AII0.5BO3 cubic perovskites is generated. Using Evac data of perovskites, the area-specific resistance (ASR) data, which is related to both oxygen reduction reaction activity and selective oxygen ion conductivity of materials, is calculated. Screening a total of 270 chemical compositions, 31 perovskites are identified as candidates with properties that are between those of state-of-the-art SOFC cathode and oxygen permeation components. In addition, an intuitive approach to estimate Evac and ASR data of complex perovskites by using solely the easy-to-access data of simple perovskites is shown, which is expected to boost future explorations in the perovskite material search space for genuinely diverse energy applications.

A quantitative evaluation of computational methods to accelerate the study of alloxazine-derived electroactive compounds for energy storage.

Alloxazines are a promising class of organic electroactive compounds for application in aqueous redox flow batteries (ARFBs), whose redox properties need to be tuned further for higher performance. High-throughput computational screening (HTCS) enables rational and time-efficient study of energy storage compounds. We compared the performance of computational chemistry methods, including the force field based molecular mechanics, semi-empirical quantum mechanics, density functional tight binding, and density functional theory, on the basis of their accuracy and computational cost in predicting the redox potentials of alloxazines. Various energy-based descriptors, including the redox reaction energies and the frontier orbital energies of the reactant and product molecules, were considered. We found that the lowest unoccupied molecular orbital (LUMO) energy of the reactant molecules is the best performing chemical descriptor for alloxazines, which is in contrast to other classes of energy storage compounds, such as quinones that we reported earlier. Notably, we present a flexible in silico approach to accelerate both the singly and the HTCS studies, therewithal considering the level of accuracy versus measured electrochemical data, which is readily applicable for the discovery of alloxazine-derived organic compounds for energy storage in ARFBs.

Pushing the limits of solubility prediction via quality-oriented data selection.

Accurate prediction of the solubility of chemical substances in solvents remains a challenge. The sparsity of high-quality solubility data is recognized as the biggest hurdle in the development of robust data-driven methods for practical use. Nonetheless, the effects of the quality and quantity of data on aqueous solubility predictions have not yet been scrutinized. In this study, the roles of the size and the quality of data sets on the performances of the solubility prediction models are unraveled, and the concepts of actual and observed performances are introduced. In an effort to curtail the gap between actual and observed performances, a quality-oriented data selection method, which evaluates the quality of data and extracts the most accurate part of it through statistical validation, is designed. Applying this method on the largest publicly available solubility database and using a consensus machine learning approach, a top-performing solubility prediction model is achieved.

Comparison of computational chemistry methods for the discovery of quinone-based electroactive compounds for energy storage.

High-throughput computational screening (HTCS) is a powerful approach for the rational and time-efficient design of electroactive compounds. The effectiveness of HTCS is dependent on accuracy and speed at which the performance descriptors can be estimated for possibly millions of candidate compounds. Here, a systematic evaluation of computational methods, including force field (FF), semi-empirical quantum mechanics (SEQM), density functional based tight binding (DFTB), and density functional theory (DFT), is performed on the basis of their accuracy in predicting the redox potentials of redox-active organic compounds. Geometry optimizations at low-level theories followed by single point energy (SPE) DFT calculations that include an implicit solvation model are found to offer equipollent accuracy as the high-level DFT methods, albeit at significantly lower computational costs. Effects of implicit solvation on molecular geometries and SPEs, and their overall effects on the prediction accuracy of redox potentials are analyzed in view of computational cost versus prediction accuracy, which outlines the best choice of methods corresponding to a desired level of accuracy. The modular computational approach is applicable for accelerating the virtual studies on functional quinones and the respective discovery of candidate compounds for energy storage.

AqSolDB, a curated reference set of aqueous solubility and 2D descriptors for a diverse set of compounds.

Water is a ubiquitous solvent in chemistry and life. It is therefore no surprise that the aqueous solubility of compounds has a key role in various domains, including but not limited to drug discovery, paint, coating, and battery materials design. Measurement and prediction of aqueous solubility is a complex and prevailing challenge in chemistry. For the latter, different data-driven prediction models have recently been developed to augment the physics-based modeling approaches. To construct accurate data-driven estimation models, it is essential that the underlying experimental calibration data used by these models is of high fidelity and quality. Existing solubility datasets show variance in the chemical space of compounds covered, measurement methods, experimental conditions, but also in the non-standard representations, size, and accessibility of data. To address this problem, we generated a new database of compounds, AqSolDB, by merging a total of nine different aqueous solubility datasets, curating the merged data, standardizing and validating the compound representation formats, marking with reliability labels, and providing 2D descriptors of compounds as a Supplementary Resource.

Anomalous Dependence of the Reactivity on the Presence of Steps: Dissociation of D2 on Cu(211).

Stepped metal surfaces are usually assumed to exhibit an increased catalytic activity for bond cleavage of small molecules over their flat single-crystal counterparts. We present experimental and theoretical data on the dissociative adsorption of molecular hydrogen on copper that contradicts this notion. We observe hydrogen molecules to be more reactive on the flat Cu(111) than on the stepped Cu(211) surface. We suggest that this exceptional behavior is due to a geometric effect, that is, that bond cleavage on the flat surface does not occur preferentially over a top site.

Layer-by-Layer Assembled Films of Perylene Diimide- and Squaraine-Containing Metal-Organic Framework-like Materials: Solar Energy Capture and Directional Energy Transfer.

We demonstrate that thin films of metal-organic framework (MOF)-like materials, containing two perylenediimides (PDICl4, PDIOPh2) and a squaraine dye (S1), can be fabricated by layer-by-layer assembly (LbL). Interestingly, these LbL films absorb across the visible light region (400-750 nm) and facilitate directional energy transfer. Due to the high spectral overlap and oriented transition dipole moments of the donor (PDICl4 and PDIOPh2) and acceptor (S1) components, directional long-range energy transfer from the bluest to reddest absorber was successfully demonstrated in the multicomponent MOF-like films. These findings have significant implications for the development of solar energy conversion devices based on MOFs.

Computational design of molecules for an all-quinone redox flow battery.

Inspired by the electron transfer properties of quinones in biological systems, we recently showed that quinones are also very promising electroactive materials for stationary energy storage applications. Due to the practically infinite chemical space of organic molecules, the discovery of additional quinones or other redox-active organic molecules for energy storage applications is an open field of inquiry. Here, we introduce a high-throughput computational screening approach that we applied to an accelerated study of a total of 1710 quinone (Q) and hydroquinone (QH2) (i.e., two-electron two-proton) redox couples. We identified the promising candidates for both the negative and positive sides of organic-based aqueous flow batteries, thus enabling an all-quinone battery. To further aid the development of additional interesting electroactive small molecules we also provide emerging quantitative structure-property relationships.

High electrical conductivity in Ni3(2,3,6,7,10,11-hexaiminotriphenylene)2, a semiconducting metal-organic graphene analogue.

Reaction of 2,3,6,7,10,11-hexaaminotriphenylene with Ni(2+) in aqueous NH3 solution under aerobic conditions produces Ni3(HITP)2 (HITP = 2,3,6,7,10,11-hexaiminotriphenylene), a new two-dimensional metal-organic framework (MOF). The new material can be isolated as a highly conductive black powder or dark blue-violet films. Two-probe and van der Pauw electrical measurements reveal bulk (pellet) and surface (film) conductivity values of 2 and 40 S·cm(-1), respectively, both records for MOFs and among the best for any coordination polymer.

A metal-free organic-inorganic aqueous flow battery.

As the fraction of electricity generation from intermittent renewable sources--such as solar or wind--grows, the ability to store large amounts of electrical energy is of increasing importance. Solid-electrode batteries maintain discharge at peak power for far too short a time to fully regulate wind or solar power output. In contrast, flow batteries can independently scale the power (electrode area) and energy (arbitrarily large storage volume) components of the system by maintaining all of the electro-active species in fluid form. Wide-scale utilization of flow batteries is, however, limited by the abundance and cost of these materials, particularly those using redox-active metals and precious-metal electrocatalysts. Here we describe a class of energy storage materials that exploits the favourable chemical and electrochemical properties of a family of molecules known as quinones. The example we demonstrate is a metal-free flow battery based on the redox chemistry of 9,10-anthraquinone-2,7-disulphonic acid (AQDS). AQDS undergoes extremely rapid and reversible two-electron two-proton reduction on a glassy carbon electrode in sulphuric acid. An aqueous flow battery with inexpensive carbon electrodes, combining the quinone/hydroquinone couple with the Br2/Br(-) redox couple, yields a peak galvanic power density exceeding 0.6 W cm(-2) at 1.3 A cm(-2). Cycling of this quinone-bromide flow battery showed >99 per cent storage capacity retention per cycle. The organic anthraquinone species can be synthesized from inexpensive commodity chemicals. This organic approach permits tuning of important properties such as the reduction potential and solubility by adding functional groups: for example, we demonstrate that the addition of two hydroxy groups to AQDS increases the open circuit potential of the cell by 11% and we describe a pathway for further increases in cell voltage. The use of π-aromatic redox-active organic molecules instead of redox-active metals represents a new and promising direction for realizing massive electrical energy storage at greatly reduced cost.

First-principles modelling of magnesium titanium hydrides.

Mixing Mg with Ti leads to a hydride Mg(x)Ti((1 - x))H(2) with markedly improved (de)hydrogenation properties for x ≤ 0.8, as compared to MgH(2). Optically thin films of Mg(x)Ti((1 - x))H(2) have a black appearance, which is remarkable for a hydride material. In this paper we study the structure and stability of Mg(x)Ti((1 - x))H(2), x = 0-1 by first-principles calculations at the level of density functional theory. We give evidence for a fluorite to rutile phase transition at a critical composition x(c) = 0.8-0.9, which correlates with the experimentally observed sharp decrease in (de)hydrogenation rates at this composition. The densities of states of Mg(x)Ti((1 - x))H(2) have a peak at the Fermi level, composed of Ti d states. Disorder in the positions of the Ti atoms easily destroys the metallic plasma, however, which suppresses the optical reflection. Interband transitions result in a featureless optical absorption over a large energy range, causing the black appearance of Mg(x)Ti((1 - x))H(2).