Interpretable Machine Learning Models for Practical Antimonate Electrocatalyst Performance.

Computationally predicting the performance of catalysts under reaction conditions is a challenging task due to the complexity of catalytic surfaces and their evolution in situ, different reaction paths, and the presence of solid-liquid interfaces in the case of electrochemistry. We demonstrate here how relatively simple machine learning models can be found that enable prediction of experimentally observed onset potentials. Inputs to our model are comprised of data from the oxygen reduction reaction on non-precious transition-metal antimony oxide nanoparticulate catalysts with a combination of experimental conditions and computationally affordable bulk atomic and electronic structural descriptors from density functional theory simulations. From human-interpretable genetic programming models, we identify key experimental descriptors and key supplemental bulk electronic and atomic structural descriptors that govern trends in onset potentials for these oxides and deduce how these descriptors should be tuned to increase onset potentials. We finally validate these machine learning predictions by experimentally confirming that scandium as a dopant in nickel antimony oxide leads to a desired onset potential increase. Macroscopic experimental factors are found to be crucially important descriptors to be considered for models of catalytic performance, highlighting the important role machine learning can play here even in the presence of small datasets.

From materials to structures: A holistic examination of achieving linearity in flexible pressure sensors.

As a pivotal category in the realm of wearable electronics, flexible pressure sensors have become a focal point due to their diverse applications such as human-machine interfaces and health monitoring. To cater for these applications, novel material design and fabrication strategies have been devised as to enhance the device performance by manipulating mechanical and electrical properties. This work primarily reviews the development of flexible pressure sensors during recent years with a focus on sensitive materials and related applications. First, an overview of the fundamental working mechanisms for various sensors was elucidated. Then, the influence of distinct surface microstructures or internal microstructures on the linearity of flexible sensors was explored. Following this, we delve into diverse applications of linear flexible pressure sensors, spanning from robotics, safety, electronic skin, to health monitoring. Finally, the existing constraints and future research prospects are outlined to pave the way for the further development of high-performance flexible pressure sensors. .

Database Construction of Two-Dimensional Charged Building Blocks for Functional-Oriented Material Design.

Databases for charge-neutral two-dimensional (2D) building blocks (BBs), i.e., 2D materials, have been built for years due to their applications in nanoelectronics. Though lots of solids are constructed from charged 2DBBs, a database for them is still missing. Here, we identify 1028 charged 2DBBs from Materials Project database using a topological-scaling algorithm. These BBs host versatile functionalities including superconductivity, magnetism, and topological properties. We construct layered materials by assembling these BBs considering valence state and lattice mismatch and predict 353 stable layered materials by high-throughput density functional theory calculations. These materials can not only inherit their functionalities but also show enhanced/emergent properties compared with their parent materials: CaAlSiF displays superconducting transition temperature higher than NaAlSi; Na2CuIO6 shows bipolar ferromagnetic semiconductivity and anomalous valley Hall effect that are absent in KCuIO6; LaRhGeO possesses nontrivial band topology. This database expands the design space of functional materials for fundamental research and potential applications.

Recent Advances in Polymer Nanocomposites: Unveiling the Frontier of Shape Memory and Self-Healing Properties-A Comprehensive Review.

Shape memory and self-healing polymer nanocomposites have attracted considerable attention due to their modifiable properties and promising applications. The incorporation of nanomaterials (polypyrrole, carboxyl methyl cellulose, carbon nanotubes, titania nanotubes, graphene, graphene oxide, mesoporous silica) into these polymers has significantly enhanced their performance, opening up new avenues for diverse applications. The self-healing capability in polymer nanocomposites depends on several factors, including heat, quadruple hydrogen bonding, π-π stacking, Diels-Alder reactions, and metal-ligand coordination, which collectively govern the interactions within the composite materials. Among possible interactions, only quadruple hydrogen bonding between composite constituents has been shown to be effective in facilitating self-healing at approximately room temperature. Conversely, thermo-responsive self-healing and shape memory polymer nanocomposites require elevated temperatures to initiate the healing and recovery processes. Thermo-responsive (TRSMPs), light-actuated, magnetically actuated, and Electrically actuated Shape Memory Polymer Nanocomposite are discussed. This paper provides a comprehensive overview of the different types of interactions involved in SMP and SHP nanocomposites and examines their behavior at both room temperature and elevated temperature conditions, along with their biomedical applications. Among many applications of SMPs, special attention has been given to biomedical (drug delivery, orthodontics, tissue engineering, orthopedics, endovascular surgery), aerospace (hinges, space deployable structures, morphing aircrafts), textile (breathable fabrics, reinforced fabrics, self-healing electromagnetic interference shielding fabrics), sensor, electrical (triboelectric nanogenerators, information energy storage devices), electronic, paint and self-healing coating, and construction material (polymer cement composites) applications.

Nanoscale Design for High Entropy Alloy Electrocatalysts.

Due to their distinctive physical and chemical characteristics, high entropy alloys (HEAs), a class of alloys comprising multiple elements, have garnered a lot of attention. It is demonstrated recently that HEA electrocatalysts increase the activity and stability of several processes. In this paper, the most recent developments in HEA electrocatalysts research are reviewed, and the performance of HEAs in catalyzing key reactions in water electrolysis and fuel cells is summarized. In addition, the design strategies for HEA electrocatalysts optimization is introduced, which include component selection, size optimization, morphology control, structural engineering, crystal phase regulation, and theoretical prediction, which can guide component selection and structural design of HEA electrocatalysts.

Accelerating the Design of High-Energy-Density Hydrocarbon Fuels by Learning from the Data.

In the ZINC20 database, with the aid of maximum substructure searches, common substructures were obtained from molecules with high-strain-energy and combustion heat values, and further provided domain knowledge on how to design high-energy-density hydrocarbon (HEDH) fuels. Notably, quadricyclane and syntin could be topologically assembled through these substructures, and the corresponding assembled schemes guided the design of 20 fuel molecules (ZD-1 to ZD-20). The fuel properties of the molecules were evaluated by using group-contribution methods and density functional theory (DFT) calculations, where ZD-6 stood out due to the high volumetric net heat of combustion, high specific impulse, low melting point, and acceptable flash point. Based on the neural network model for evaluating the synthetic complexity (SCScore), the estimated value of ZD-6 was close to that of syntin, indicating that the synthetic complexity of ZD-6 was comparable to that of syntin. This work not only provides ZD-6 as a potential HEDH fuel, but also illustrates the superiority of learning design strategies from the data in increasing the understanding of structure and performance relationships and accelerating the development of novel HEDH fuels.

Strength and Nature of Host-Guest Interactions in Metal-Organic Frameworks from a Quantum-Chemical Perspective.

Metal-organic frameworks (MOFs) offer a convenient means for capturing, transporting, and releasing small molecules. Their rational design requires an in-depth understanding of the underlying non-covalent host-guest interactions, and the ability to easily and rapidly pre-screen candidate architectures in silico. In this work, we devised a recipe for computing the strength and analysing the nature of the host-guest interactions in MOFs. By assessing a range of density functional theory methods across periodic and finite supramolecular cluster scale we find that appropriately constructed clusters readily reproduce the key interactions occurring in periodic models at a fraction of the computational cost. Host-guest interaction energies can be reliably computed with dispersion-corrected density functional theory methods; however, decoding their precise nature demands insights from energy decomposition schemes and quantum-chemical tools for bonding analysis such as the quantum theory of atoms in molecules, the non-covalent interactions index or the density overlap regions indicator.

One Step before Synthesis: Structure-Property-Condition Relationship Models to Sustainable Design of Efficient TiO2-Based Multicomponent Nanomaterials.

To control the photocatalytic activity, it is essential to consider several parameters affecting the structure of ordered multicomponent TiO2-based photocatalytic nanotubes. The lack of systematic knowledge about the relationship between structure, property, and preparation parameters may be provided by applying a machine learning (ML) methodology and predictive models based on the quantitative structure-property-condition relationship (QSPCR). In the present study, for the first time, the quantitative mapping of preparation parameters, morphology, and photocatalytic activity of 136 TiO2 NTs doped with metal and non-metal nanoparticles synthesized with the one-step anodization method has been investigated via linear and nonlinear ML methods. Moreover, the developed QSPCR model, for the first time, provides systematic knowledge supporting the design of effective TiO2-based nanotubes by proper structure manipulation. The proposed computer-aided methodology reduces cost and speeds up the process (optimize) of efficient photocatalysts' design at the earliest possible stage (before synthesis) in line with the sustainability-by-design strategy.

Porous Tungsten Oxide: Recent Advances in Design, Synthesis, and Applications.

Tungsten oxide (WO3 ) has received ever more attention and has been highly researched over the last decade due to its being a low-cost transition metal semiconductor with tunable, yet widely stable, band gaps. This minireview briefly highlights the challenges in the design and synthesis of porous WO3 including methods, precursors, solvent effects, crystal phases, and surface activities of the porous WO3 base material. These topics are explored while also drawing a connection of how the morphology and crystal phase affect the band gap. The shifts in band gap not only impact the optical properties of tungsten but also allow tuning to operate on different energy levels, which makes WO3 highly desirable in many applications such as supercapacitors, batteries, solar cells, catalysts, sensors, smart windows, and bioapplications.

New two-dimensional arsenene polymorph predicted by first-principles calculation: robust direct bandgap and enhanced optical adsorption.

In this work, we predict a new polymorph of 2D monolayer arsenic. This structure, namedδ-As, consists of a centrosymmetric monolayer, which is thermodynamically and kinetically stable. Distinctly different from the previously predicted monolayer arsenic with an indirect bandgap, the new allotrope exhibits a direct bandgap characteristic. Moreover, while keeping the direct bandgap unchanged, the bandgap of monolayerδ-As can be adjusted from 1.83 eV to 0 eV by applying zigzag-direction tensile strain, which is pronounced an advantage for solar cell and photodetector applications.

Topics in the mathematical design of materials.

We present a perspective on several current research directions relevant to the mathematical design of new materials. We discuss: (i) design problems for phase-transforming and shape-morphing materials, (ii) epitaxy as an approach of central importance in the design of advanced semiconductor materials, (iii) selected design problems in soft matter, (iv) mathematical problems in magnetic materials, (v) some open problems in liquid crystals and soft materials and (vi) mathematical problems on liquid crystal colloids. The presentation combines topics from soft and hard condensed matter, with specific focus on those design themes where mathematical approaches could possibly lead to exciting progress. This article is part of the theme issue 'Topics in mathematical design of complex materials'.

The geometric blueprint of perovskites.

Perovskite minerals form an essential component of the Earth's mantle, and synthetic crystals are ubiquitous in electronics, photonics, and energy technology. The extraordinary chemical diversity of these crystals raises the question of how many and which perovskites are yet to be discovered. Here we show that the "no-rattling" principle postulated by Goldschmidt in 1926, describing the geometric conditions under which a perovskite can form, is much more effective than previously thought and allows us to predict perovskites with a fidelity of 80%. By supplementing this principle with inferential statistics and internet data mining we establish that currently known perovskites are only the tip of the iceberg, and we enumerate 90,000 hitherto-unknown compounds awaiting to be studied. Our results suggest that geometric blueprints may enable the systematic screening of millions of compounds and offer untapped opportunities in structure prediction and materials design.

Molecular Vanadium Oxides for Energy Conversion and Energy Storage: Current Trends and Emerging Opportunities.

Molecular vanadium oxides, or polyoxovanadates (POVs), have recently emerged as a new class of molecular energy conversion/storage materials, which combine diverse, chemically tunable redox behavior and reversible multielectron storage capabilities. This Review explores current challenges, major breakthroughs, and future opportunities in the use of POVs for energy conversion and storage. The reactivity, advantages, and limitations of POVs are explored, with a focus on their use in lithium and post-lithium-ion batteries, redox-flow batteries, and light-driven energy conversion. Finally, emerging themes and new research directions are critically assessed to provide inspiration for how this promising materials class can advance research in sustainable energy technologies.

A New Family of Two-Dimensional Crystals: Open-Framework T3 X ( T = C, Si, Ge, Sn; X = O, S, Se, Te) Compounds with Tetrahedral Bonding.

To accelerate development of innovative materials, their modelings and predictions with useful functionalities are of vital importance. Here, based on a recently developed crystal structure prediction method, we find a new family of stable two-dimensional crystals with an open-channel tetrahedral bonding network, rendering a potential prototype for electronic and energy applications. The proposed structural prototype with a space group of Cmme hosts at least 13 different freestanding T3 X compounds with group IV ( T = C, Si, Ge, Sn) and VI ( X = O, S, Se, Te) elements. Moreover, the proposed materials display diverse electronic properties ranging from direct band gap semiconductor to topological insulator at their pristine forms, which are further tunable by mechanical strain.

Covalent Organic Frameworks: A Promising Materials Platform for Photocatalytic CO2 Reductions.

Covalent organic frameworks (COFs) are a kind of porous crystalline polymeric material. They are constructed by organic module units connected with strong covalent bonds extending in two or three dimensions. COFs possess the advantages of low-density, large specific surface area, high thermal stability, developed pore-structure, long-range order, good crystallinity, and the excellent tunability of the monomer units and the linking reticular chemistry. These features endowed COFs with the ability to be applied in a plethora of applications, ranging from adsorption and separation, sensing, catalysis, optoelectronics, energy storage, mass transport, etc. In this paper, we will review the recent progress of COFs materials applied in photocatalytic CO2 reduction. The state-of-the-art paragon examples and the current challenges will be discussed in detail. The future direction in this research field will be finally outlooked.

Planting Repulsion Centers for Faster Ionic Diffusion in Superionic Conductors.

The successful launch of solid-state batteries relies on the discovery of solid electrolytes with remarkably high ionic conductivity. Extensive efforts have identified several important superionic conductors (SICs) and broadened our understanding of their superionic conductivity. Herein, we propose a new design strategy to facilitate ionic conduction in SICs by planting immobile repulsion centers. Our ab initio molecular dynamics simulations on the model system Na11 Sn2 PS12 demonstrate that the sodium ionic conductivity can be increased by approximately one order of magnitude by simply doping large Cs ions as repulsion centers in the characteristic vacant site of Na11 Sn2 PS12 . Planting immobile repulsion centers locally induces the formation of high-energy sites, leading to a fast track for ionic conduction owing to the unique interactions among mobile ions in SICs. Seemingly non-intuitive approaches tailor the ionic diffusion by exploiting these immobile repulsion centers.

Deep Learning in Chemistry.

Machine learning enables computers to address problems by learning from data. Deep learning is a type of machine learning that uses a hierarchical recombination of features to extract pertinent information and then learn the patterns represented in the data. Over the last eight years, its abilities have increasingly been applied to a wide variety of chemical challenges, from improving computational chemistry to drug and materials design and even synthesis planning. This review aims to explain the concepts of deep learning to chemists from any background and follows this with an overview of the diverse applications demonstrated in the literature. We hope that this will empower the broader chemical community to engage with this burgeoning field and foster the growing movement of deep learning accelerated chemistry.

Turning statistical physics models into materials design engines.

Despite the success statistical physics has enjoyed at predicting the properties of materials for given parameters, the inverse problem, identifying which material parameters produce given, desired properties, is only beginning to be addressed. Recently, several methods have emerged across disciplines that draw upon optimization and simulation to create computer programs that tailor material responses to specified behaviors. However, so far the methods developed either involve black-box techniques, in which the optimizer operates without explicit knowledge of the material's configuration space, or require carefully tuned algorithms with applicability limited to a narrow subclass of materials. Here we introduce a formalism that can generate optimizers automatically by extending statistical mechanics into the realm of design. The strength of this approach lies in its capability to transform statistical models that describe materials into optimizers to tailor them. By comparing against standard black-box optimization methods, we demonstrate how optimizers generated by this formalism can be faster and more effective, while remaining straightforward to implement. The scope of our approach includes possibilities for solving a variety of complex optimization and design problems concerning materials both in and out of equilibrium.

Polar Metallocenes.

Crystalline polar metallocenes are potentially useful active materials as piezoelectrics, ferroelectrics, and multiferroics. Within density functional theory (DFT), we computed structural properties, energy differences for various phases, molecular configurations, and magnetic states, computed polarizations for different polar crystal structures, and computed dipole moments for the constituent molecules with a Wannier function analysis. Of the systems studied, Mn2(C9H9N)2 is the most promising as a multiferroic material, since the ground state is both polar and ferromagnetic. We found that the predicted crystalline polarizations are 30⁻40% higher than the values that would be obtained from the dipole moments of the isolated constituent molecules, due to the local effects of the self-consistent internal electric field, indicating high polarizabilities.

stk: A python toolkit for supramolecular assembly.

A tool for the automated assembly, molecular optimization and property calculation of supramolecular materials is presented. stk is a modular, extensible and open-source Python library that provides a simple Python API and integration with third party computational codes. stk currently supports the construction of linear polymers, small linear oligomers, organic cages in multiple topologies and covalent organic frameworks (COFs) in multiple framework topologies, but is designed to be easy to extend to new, unrelated, supramolecules or new topologies. Extension to metal-organic frameworks (MOFs), metallocycles or supramolecules, such as catenanes, would be straightforward. Through integration with third party codes, stk offers the user the opportunity to explore the potential energy landscape of the assembled supramolecule and then calculate the supramolecule's structural features and properties. stk provides support for high-throughput screening of large batches of supramolecules at a time. The source code of the program can be found at https://github.com/supramolecular-toolkit/stk. © 2018 Wiley Periodicals, Inc.