Predicting synthesizability

Accelerating the design of lattice structures using machine learning

Lattices remain an attractive class of structures due to their design versatility; however, rapidly designing lattice structures with tailored or optimal mechanical properties remains a significant challenge. With each added design variable, the design space quickly becomes intractable. To address this challenge, research efforts have sought to combine computational approaches with machine learning (ML)-based approaches to reduce the computational cost of the design process and accelerate mechanical design. While these efforts have made substantial progress, significant challenges remain in (1) building and interpreting the ML-based surrogate models and (2) iteratively and efficiently curating training datasets for optimization tasks. Here, we address the first challenge by combining ML-based surrogate modeling and Shapley additive explanation (SHAP) analysis to interpret the impact of each design variable. We find that our ML-based surrogate models achieve excellent prediction capabilities (R2 > 0.95) and SHAP values aid in uncovering design variables influencing performance. We address the second challenge by utilizing active learning-based methods, such as Bayesian optimization, to explore the design space and report a 5 × reduction in simulations relative to grid-based search. Collectively, these results underscore the value of building intelligent design systems that leverage ML-based methods for uncovering key design variables and accelerating design.

Crystal Structure Assignment for Unknown Compounds from X-ray Diffraction Patterns with Deep Learning

Determining the structures of previously unseen compounds from experimental characterizations is a crucial part of materials science. It requires a step of searching for the structure type that conforms to the lattice of the unknown compound, which enables the pattern matching process for characterization data, such as X-ray diffraction (XRD) patterns. However, this procedure typically places a high demand on domain expertise, thus creating an obstacle for computer-driven automation. Here, we address this challenge by leveraging a deep-learning model composed of a union of convolutional residual neural networks. The accuracy of the model is demonstrated on a dataset of over 60,000 different compounds for 100 structure types, and additional categories can be integrated without the need to retrain the existing networks. We also unravel the operation of the deep-learning black box and highlight the way in which the resemblance between the unknown compound and a structure type is quantified based on both local and global characteristics in XRD patterns. This computational tool opens new avenues for automating structure analysis on materials unearthed in high-throughput experimentation.

Towards the high-throughput prediction of finite-temperature properties using the quasi-harmonic approximation

Lattice dynamics calculations within the quasi-harmonic approximation (QHA) provide an infrastructure for modelling the finite-temperature properties of periodic solids at a modest computational cost. With the recent widespread interest in materials discovery by data mining, a database of computed finite-temperature properties would be highly desirable. In this work we provide a first step toward this goal with a comparative study of the accuracy of five exchange-correlation functionals, spanning the local density approximation (LDA), generalised-gradient approximation (GGA) and meta-GGA levels of theory, for predicting the properties of ten Group 1, 2 and 12 binary metal oxides. We find that the predictions are bounded by the LDA, which tends to underestimate lattice parameters and cell volumes relative to experiments, but yields the most accurate results for bulk moduli, expansion coefficients and Grüneisen parameters, and the PBE GGA, which shows the opposite behaviour. The PBEsol GGA gives the best overall predictions of the lattice parameters and volumes whilst also giving relatively reliable results for other properties. Our results demonstrate that, given a suitable choice of functional, a variety of finite-temperature properties can be predicted with useful accuracy, and hence that high-throughout QHA calculations are technically feasible.

Mechanistically Guided Materials Chemistry: Synthesis of Ternary Nitrides, CaZrN2 and CaHfN2

Recent computational studies have predicted many new ternary nitrides, revealing synthetic opportunities in this underexplored phase space. However, synthesizing new ternary nitrides is difficult, in part because intermediate and product phases often have high cohesive energies that inhibit diffusion. Here, we report the synthesis of two new phases, calcium zirconium nitride (CaZrN2) and calcium hafnium nitride (CaHfN2), by solid state metathesis reactions between Ca3N2 and MCl4 (M = Zr, Hf). Although the reaction nominally proceeds to the target phases in a 1:1 ratio of the precursors via Ca3N2 + MCl4 → CaMN2 + 2 CaCl2, reactions prepared this way result in Ca-poor materials (CaxM2-xN2, x < 1). A small excess of Ca3N2 (ca. 20 mol %) is needed to yield stoichiometric CaMN2, as confirmed by high-resolution synchrotron powder X-ray diffraction. In situ synchrotron X-ray diffraction studies reveal that nominally stoichiometric reactions produce Zr3+ intermediates early in the reaction pathway, and the excess Ca3N2 is needed to reoxidize Zr3+ intermediates back to the Zr4+ oxidation state of CaZrN2. Analysis of computationally derived chemical potential diagrams rationalizes this synthetic approach and its contrast from the synthesis of MgZrN2. These findings additionally highlight the utility of in situ diffraction studies and computational thermochemistry to provide mechanistic guidance for synthesis.

Cation-Disorder Engineering Promotes Efficient Charge-Carrier Transport in AgBiS2 Nanocrystal Films

Efficient charge-carrier transport is critical to the success of emergent semiconductors in photovoltaic applications. So far, disorder has been considered detrimental for charge-carrier transport, lowering mobilities, and causing fast recombination. This work demonstrates that, when properly engineered, cation disorder in a multinary chalcogenide semiconductor can considerably enhance the charge-carrier mobility and extend the charge-carrier lifetime. Here, the properties of AgBiS2 nanocrystals (NCs) are explored as a function of Ag and Bi cation-ordering, which can be modified via thermal-annealing. Local Ag-rich and Bi-rich domains formed during hot-injection synthesis are transformed to induce homogeneous disorder (random Ag-Bi distribution). Such cation-disorder engineering results in a sixfold increase in the charge-carrier mobility, reaching ≈2.7 cm2 V-1 s-1 in AgBiS2 NC thin films. It is further demonstrated that homogeneous cation disorder reduces charge-carrier localization, a hallmark of charge-carrier transport recently observed in silver-bismuth semiconductors. This work proposes that cation-disorder engineering flattens the disordered electronic landscape, removing tail states that would otherwise exacerbate Anderson localization of small polaronic states. Together, these findings unravel how cation-disorder engineering in multinary semiconductors can enhance the efficiency of renewable energy applications.

On the mechanical, electronic, and optical properties of the boron nitride analog for the recently synthesized biphenylene network: a DFT study

CONTEXT

Recently, a new 2D carbon allotrope named biphenylene network (BPN) was experimentally realized. Here, we use density functional theory (DFT) calculations to study its boron nitride analogue sheet's structural, electronic, and optical properties (BN-BPN). Results suggest that BN-BPN has good structural and dynamic stabilities. It also has a direct bandgap of 4.5 eV and significant optical activity in the ultraviolet range. BN-BPN Young's modulus varies between 234.4[Formula: see text]273.2 GPa depending on the strain direction.

METHODS

Density functional theory (DFT) simulations for the electronic and optical properties of BN-BPN were performed using the CASTEP package within the Biovia Materials Studio software. The exchange and correlation functions are treated within the generalized gradient approximation (GGA) as parameterized by Perdew-Burke-Ernzerhof (PBE) and the hybrid functional Heyd-Scuseria-Ernzerhof (HSE06). For convenience, the mechanical properties were carried out using the DFT approach implemented in the SIESTA code, also within the scope of the GGA/PBE method. We used the double-zeta plus polarization (DZP) for the basis set in these cases. Moreover, the norm-conserving Troullier-Martins pseudopotential was employed to describe the core electrons.

PARC: Physics-aware recurrent convolutional neural networks to assimilate meso scale reactive mechanics of energetic materials

The thermo-mechanical response of shock-initiated energetic materials (EMs) is highly influenced by their microstructures, presenting an opportunity to engineer EM microstructures in a "materials-by-design" framework. However, the current design practice is limited, as a large ensemble of simulations is required to construct the complex EM structure-property-performance linkages. We present the physics-aware recurrent convolutional (PARC) neural network, a deep learning algorithm capable of learning the mesoscale thermo-mechanics of EM from a modest number of high-resolution direct numerical simulations (DNS). Validation results demonstrated that PARC could predict the themo-mechanical response of shocked EMs with comparable accuracy to DNS but with notably less computation time. The physics-awareness of PARC enhances its modeling capabilities and generalizability, especially when challenged in unseen prediction scenarios. We also demonstrate that visualizing the artificial neurons at PARC can shed light on important aspects of EM thermos-mechanics and provide an additional lens for conceptualizing EM.

Generative Models as an Emerging Paradigm in the Chemical Sciences

Traditional computational approaches to design chemical species are limited by the need to compute properties for a vast number of candidates, e.g., by discriminative modeling. Therefore, inverse design methods aim to start from the desired property and optimize a corresponding chemical structure. From a machine learning viewpoint, the inverse design problem can be addressed through so-called generative modeling. Mathematically, discriminative models are defined by learning the probability distribution function of properties given the molecular or material structure. In contrast, a generative model seeks to exploit the joint probability of a chemical species with target characteristics. The overarching idea of generative modeling is to implement a system that produces novel compounds that are expected to have a desired set of chemical features, effectively sidestepping issues found in the forward design process. In this contribution, we overview and critically analyze popular generative algorithms like generative adversarial networks, variational autoencoders, flow, and diffusion models. We highlight key differences between each of the models, provide insights into recent success stories, and discuss outstanding challenges for realizing generative modeling discovered solutions in chemical applications.

Predicting Synthesizability using Machine Learning on Databases of Existing Inorganic Materials

Defining the metric for synthesizability and predicting new compounds that can be experimentally realized in the realm of data-driven research is a pressing problem in contemporary materials science. The increasing computational power and advancements in machine learning (ML) algorithms provide a new avenue to solve the synthesizability challenge. In this work, using the Inorganic Crystal Structure Database (ICSD) and the Materials Project (MP) database, we represent crystal structures in Fourier-transformed crystal properties (FTCP) representation and use a deep learning model to predict synthesizability in the form of a synthesizability score (SC). Such an SC model, as a synthesizability filter for new materials, enables an efficient and accurate classification to identify promising material candidates. The SC prediction model achieved 82.6/80.6% (precision/recall) overall accuracy in predicting ternary crystal materials. We also trained the SC model by only considering compounds uploaded on the MP before 2015 as the training set and testing on multiple sets of materials uploaded after 2015. In the post-2019 test set, we obtain a high 88.60% true positive rate accuracy, coupled with 9.81% precision, indicating that newly added materials remain unexplored and have high synthesis potential. Further, we provide a list of 100 materials predicted to be synthesizable from this post-2019 dataset (highest SC) for future studies, and our SC model, as a validation filter, is beneficial for future material screening and discovery.

Proton Transport in the Gadolinium-Doped Layered Perovskite BaLaInO4

Materials capable for use in energy generation have been actively investigated recently. Thermoelectrics, photovoltaics and electronic/ionic conductors are considered as a part of the modern energy system. Layered perovskites have many attractions, as materials with high conductivity. Gadolinium-doped layered perovskite BaLaInO4 was obtained and investigated for the first time. The high values of conductivity were proved. The composition BaLa0.9Gd0.1InO4 demonstrates predominantly protonic transport under wet air and low temperatures (<400 °C). The doping by rare earth metals of layered perovskite is a prospective method for significantly improving conductivity.

BatteryDataExtractor: battery-aware text-mining software embedded with BERT models

Due to the massive growth of scientific publications, literature mining is becoming increasingly popular for researchers to thoroughly explore scientific text and extract such data to create new databases or augment existing databases. Efforts in literature-mining software design and implementation have improved text-mining productivity, but most of the toolkits that mine text are based on traditional machine-learning-algorithms which hinder the performance of downstream text-mining tasks. Natural-language processing (NLP) and text-mining technologies have seen a rapid development since the release of transformer models, such as bidirectional encoder representations from transformers (BERT). Upgrading rule-based or machine-learning-based literature-mining toolkits by embedding transformer models into the software is therefore likely to improve their text-mining performance. To this end, we release a Python-based literature-mining toolkit for the field of battery materials, BatteryDataExtractor, which involves the embedding of BatteryBERT models in its automated data-extraction pipeline. This pipeline employs BERT models for token-classification tasks, such as abbreviation detection, part-of-speech tagging, and chemical-named-entity recognition, as well as new double-turn question-answering data-extraction models for auto-generating repositories of inter-related material and property data as well as general information. We demonstrate that BatteryDataExtractor exhibits state-of-the-art performance on the evaluation data sets for both token classification and automated data extraction. To aid the use of BatteryDataExtractor, its code is provided as open-source software, with associated documentation to serve as a user guide.

High-Throughput Preparation of Supramolecular Nanostructures on Metal Surfaces

One of the contemporary challenges in materials science lies in the rapid materials screening and discovery. Experimental sample libraries can be generated by high-throughput parallel synthesis to map the composition space for rapid material discoveries. Molecular self-assembly on surfaces has proved a useful way to construct nanostructures with interesting topologies or properties. Despite the strong dependence of molecular stoichiometry on the structures, high-throughput preparations of supramolecular surface nanostructures have been far less explored. Here, by integrating a physical mask into the standard ultra-high-vacuum (UHV) molecular preparation system we show a high-throughput approach for preparing supramolecular nanostructures of continuous composition spreads on metal surfaces. The spatially addressable sample libraries of supramolecular self-assemblies are characterized by high-resolution scanning probe microscopy. We could explore different binary nanostructures of varying molecular ratios on one single substrate. Moreover, we use the minimum spanning tree approach to qualitatively and quantitatively study the structural properties of the formed nanostructures. This high-throughput approach may accelerate the screening and exploration of surface-supported, low-dimensional nanostructures not limited to supramolecular interactions.

A first-principles-based high fidelity, high throughput approach for the design of high entropy alloys

Here, we present a preselected small set of ordered structures (PSSOS) method, a first principles-based high fidelity (HF), high throughput (HT) approach, for fast screening of the large composition space of high entropy alloys (HEAs) to select the most energetically stable, single-phase HEAs. Taking quinary AlCoCrFeNi HEA as an example system, we performed PSSOS calculations on the formation energies and mass densities of 8801 compositions in both FCC and BCC lattices and selected five most stable FCC and BCC HEAs for detailed analysis. The calculation results from the PSSOS approach were compared with existing experimental and first-principles data, and the good agreement was achieved. We also compared the PSSOS with the special quasi-random structures (SQS) method, and found that with a comparable accuracy, the PSSOS significantly outperforms the SQS in efficiency, making it ideal for HF, HT calculations of HEAs.

Thin-Film Ferroelectrics

Over the last 30 years, the study of ferroelectric oxides has been revolutionized by the implementation of epitaxial-thin-film-based studies, which have driven many advances in the understanding of ferroelectric physics and the realization of novel polar structures and functionalities. New questions have motivated the development of advanced synthesis, characterization, and simulations of epitaxial thin films and, in turn, have provided new insights and applications across the micro-, meso-, and macroscopic length scales. This review traces the evolution of ferroelectric thin-film research through the early days developing understanding of the roles of size and strain on ferroelectrics to the present day, where such understanding is used to create complex hierarchical domain structures, novel polar topologies, and controlled chemical and defect profiles. The extension of epitaxial techniques, coupled with advances in high-throughput simulations, now stands to accelerate the discovery and study of new ferroelectric materials. Coming hand-in-hand with these new materials is new understanding and control of ferroelectric functionalities. Today, researchers are actively working to apply these lessons in a number of applications, including novel memory and logic architectures, as well as a host of energy conversion devices.

Perovskite- and Dye-Sensitized Solar-Cell Device Databases Auto-generated Using ChemDataExtractor

The number of scientific publications reporting cutting-edge third-generation photovoltaic devices is increasing rapidly, owing to the pressing need to develop renewable-energy technologies that address the climate-change crisis. Consequently, the field could benefit from a central repository where photovoltaic-performance metrics, such as the power-conversion efficiency (η) are recorded. We present two automatically generated databases that contain photovoltaic properties and device material data for dye-sensitized solar cells (DSCs) and perovskite solar cells (PSCs), totalling 660,881 data entries representing 57,678 photovoltaic devices. The databases were generated by applying the text-mining toolkit ChemDataExtractor on a corpus of 25,720 articles. A multi-faceted evaluation, incorporating manual and automatic methods, was applied to ensure that the data contained therein were of the highest quality, with precision metrics ranging from 73.1% to 95.8%. The DSC database contains 475,045 entries representing 41,680 devices, and the PSC database contains 185,836 entries representing 15,818 devices. The databases are available in MongoDB and JSON formats, which can be queried in Python, R, Java and MATLAB for data-driven photovoltaic materials discovery.

Water electrolysis: from textbook knowledge to the latest scientific strategies and industrial developments

Replacing fossil fuels with energy sources and carriers that are sustainable, environmentally benign, and affordable is amongst the most pressing challenges for future socio-economic development. To that goal, hydrogen is presumed to be the most promising energy carrier. Electrocatalytic water splitting, if driven by green electricity, would provide hydrogen with minimal CO2 footprint. The viability of water electrolysis still hinges on the availability of durable earth-abundant electrocatalyst materials and the overall process efficiency. This review spans from the fundamentals of electrocatalytically initiated water splitting to the very latest scientific findings from university and institutional research, also covering specifications and special features of the current industrial processes and those processes currently being tested in large-scale applications. Recently developed strategies are described for the optimisation and discovery of active and durable materials for electrodes that ever-increasingly harness first-principles calculations and machine learning. In addition, a technoeconomic analysis of water electrolysis is included that allows an assessment of the extent to which a large-scale implementation of water splitting can help to combat climate change. This review article is intended to cross-pollinate and strengthen efforts from fundamental understanding to technical implementation and to improve the 'junctions' between the field's physical chemists, materials scientists and engineers, as well as stimulate much-needed exchange among these groups on challenges encountered in the different domains.

Machine Learning: An Advanced Platform for Materials Development and State Prediction in Lithium-Ion Batteries

Lithium-ion batteries (LIBs) are vital energy-storage devices in modern society. However, the performance and cost are still not satisfactory in terms of energy density, power density, cycle life, safety, etc. To further improve the performance of batteries, traditional "trial-and-error" processes require a vast number of tedious experiments. Computational chemistry and artificial intelligence (AI) can significantly accelerate the research and development of novel battery systems. Herein, a heterogeneous category of AI technology for predicting and discovering battery materials and estimating the state of the battery system is reviewed. Successful examples, the challenges of deploying AI in real-world scenarios, and an integrated framework are analyzed and outlined. The state-of-the-art research about the applications of ML in the property prediction and battery discovery, including electrolyte and electrode materials, are further summarized. Meanwhile, the prediction of battery states is also provided. Finally, various existing challenges and the framework to tackle the challenges on the further development of machine learning for rechargeable LIBs are proposed.

Combinatorial synthesis of heteroepitaxial, multi-cation, thin-films via pulsed laser deposition coupled with in-situ, chemical and structural characterization

Combinatorial synthesis via a continuous composition spread is an excellent route to develop thin-film libraries as it is both time- and cost-efficient. Creating libraries of functional, multicomponent, complex oxide films requires excellent control over the synthesis parameters combined with high-throughput analytical feedback. A reliable, high-throughput, in-situ characterization analysis method is required to meet the crucial need to rapidly screen materials libraries. Here, we report on the combination of two in-situ techniques—(a) Reflection high-energy electron diffraction (RHEED) for heteroepitaxial characterization and a newly developed compositional analysis technique, low-angle x-ray spectroscopy (LAXS), to map the chemical composition profile of combinatorial heteroepitaxial complex oxide films deposited using a continuous composition spread method via pulsed laser deposition. This is accomplished using a unique state-of-the-art combinatorial growth system with a fully synchronized four-axis mechanical substrate stage without shadow masks, alternating acquisition of chemical compositional data using LAXS at various different positions on the $$\sim$$ ∼ 41 mm $$\times$$ × 41 mm range and sequential deposition of multilayers of SrTiO$$_3$$ 3 and $$\hbox {SrTi}_{0.8}\hbox {Ru}_{0.2}\hbox {O}_3$$ SrTi 0.8 Ru 0.2 O 3 on a 2-inch (50.8 mm) $$\hbox {LaAlO}_3$$ LaAlO 3 wafer in a single growth run. Rutherford backscattering spectrometry (RBS) is used to calibrate and validate the compositions determined by LAXS. This study shows the feasibility of combinatorial synthesis of heteroepitaxial, functional complex oxide films at wafer-scale via two essential in-situ characterization tools—RHEED for structural analysis or heteroepitaxy and LAXS for compositional characterization. This is a powerful technique for development of new films with optimized heteroepitaxy and composition.

Materials for Sustainable Nuclear Energy: A European Strategic Research and Innovation Agenda for All Reactor Generations

Nuclear energy is presently the single major low-carbon electricity source in Europe and is overall expected to maintain (perhaps eventually even increase) its current installed power from now to 2045. Long-term operation (LTO) is a reality in essentially all nuclear European countries, even when planning to phase out. New builds are planned. Moreover, several European countries, including non-nuclear or phasing out ones, have interests in next generation nuclear systems. In this framework, materials and material science play a crucial role towards safer, more efficient, more economical and overall more sustainable nuclear energy. This paper proposes a research agenda that combines modern digital technologies with materials science practices to pursue a change of paradigm that promotes innovation, equally serving the different nuclear energy interests and positions throughout Europe. This paper chooses to overview structural and fuel materials used in current generation reactors, as well as their wider spectrum for next generation reactors, summarising the relevant issues. Next, it describes the materials science approaches that are common to any nuclear materials (including classes that are not addressed here, such as concrete, polymers and functional materials), identifying for each of them a research agenda goal. It is concluded that among these goals are the development of structured materials qualification test-beds and materials acceleration platforms (MAPs) for materials that operate under harsh conditions. Another goal is the development of multi-parameter-based approaches for materials health monitoring based on different non-destructive examination and testing (NDE&T) techniques. Hybrid models that suitably combine physics-based and data-driven approaches for materials behaviour prediction can valuably support these developments, together with the creation and population of a centralised, “smart” database for nuclear materials.

Inverse Design of Ferroelectric-Order in Perovskite Crystal for Self-Powered Ultraviolet Photodetection

It has always been a hot topic to design an orderly mesoscopic structure in functional materials to tailor the macroscopic properties or realize new functions. The existence of domains in ferroelectric materials has been proven to affect the macroscopic properties, being actively studied in nonlinear optical conversion and piezoelectric effects. However, the high-efficiency photoelectric conversion capability of ferroelectric crystals has not yet been explored. Here, the authors study the orderly arrangement of ferroelectric order in KTa_{1- x} Nb_x O₃ (KTN) perovskite crystals, and design the "head-to-head" domains by tuning the Curie temperature T_c , thereby generating abundant charged domain walls and robust conductive channels for electrons and holes. An ultrahigh ultraviolet photoresponsivity is achieved in the KTN crystal under zero bias voltage, being about four orders magnitude higher than that of the well-known ferroelectric materials. The substantial improvement can be attributed to the judiciously designed ferroelectric order, as demonstrated by the conductive atomic force microscopy. In addition, KTN detector exhibits high stability and reliability after high-temperature and fatigue treatment. KTN crystal features giant photoresponsivity, high electric-optical coefficient, and large χ⁽²⁾ nonlinearity concurrently, indicating its great potential for application of all-optical devices on photonic chips.

Computational perspective on recent advances in quantum electronics: from electron quantum optics to nanoelectronic devices and systems

Quantum electronics has significantly evolved over the last decades. Where initially the clear focus was on light-matter interactions, nowadays approaches based on the electron's wave nature have solidified themselves as additional focus areas. This development is largely driven by continuous advances in electron quantum optics, electron based quantum information processing, electronic materials, and nanoelectronic devices and systems. The pace of research in all of these areas is astonishing and is accompanied by substantial theoretical and experimental advancements. What is particularly exciting is the fact that the computational methods, together with broadly available large-scale computing resources, have matured to such a degree so as to be essential enabling technologies themselves. These methods allow to predict, analyze, and design not only individual physical processes but also entire devices and systems, which would otherwise be very challenging or sometimes even out of reach with conventional experimental capabilities. This review is thus a testament to the increasingly towering importance of computational methods for advancing the expanding field of quantum electronics. To that end, computational aspects of a representative selection of recent research in quantum electronics are highlighted where a major focus is on the electron's wave nature. By categorizing the research into concrete technological applications, researchers and engineers will be able to use this review as a source for inspiration regarding problem-specific computational methods.

Autonomous materials synthesis via hierarchical active learning of nonequilibrium phase diagrams

Autonomous experimentation enabled by artificial intelligence offers a new paradigm for accelerating scientific discovery. Nonequilibrium materials synthesis is emblematic of complex, resource-intensive experimentation whose acceleration would be a watershed for materials discovery. We demonstrate accelerated exploration of metastable materials through hierarchical autonomous experimentation governed by the Scientific Autonomous Reasoning Agent (SARA). SARA integrates robotic materials synthesis using lateral gradient laser spike annealing and optical characterization along with a hierarchy of AI methods to map out processing phase diagrams. Efficient exploration of the multidimensional parameter space is achieved with nested active learning cycles built upon advanced machine learning models that incorporate the underlying physics of the experiments and end-to-end uncertainty quantification. We demonstrate SARA’s performance by autonomously mapping synthesis phase boundaries for the Bi₂O₃ system, leading to orders-of-magnitude acceleration in the establishment of a synthesis phase diagram that includes conditions for stabilizing δ-Bi₂O₃ at room temperature, a critical development for electrochemical technologies.

Application of computational approach in plastic pyrolysis kinetic modelling: a review

During the past decade, pyrolysis routes have been identified as one of the most promising solutions for plastic waste management. However, the industrial adoption of such technologies has been limited and several unresolved blind spots hamper the commercial application of pyrolysis. Despite many years and efforts to explain pyrolysis models based on global kinetic approaches, recent advances in computational modelling such as machine learning and quantum mechanics offer new insights. For example, the kinetic and mechanistic information about plastic pyrolysis reactions necessary for scaling up processes is unravelling. This selective literature review reveals some of the foundational knowledge and accurate views on the reaction pathways, product yields, and other features of pyrolysis created by these new tools. Pyrolysis routes mapped by machine learning and quantum mechanics will gain more relevance in the coming years, especially studies that combine computational models with different time and scale resolutions governed by “first principles.” Existing research suggests that, as machine learning is further coupled to quantum mechanics, scientists and engineers will better predict products, yields, and compositions, as well as more complicated features such as ideal reactor design.

Discovery of complex oxides via automated experiments and data science

The quest to identify materials with tailored properties is increasingly expanding into high-order composition spaces, with a corresponding combinatorial explosion in the number of candidate materials. A key challenge is to discover regions in composition space where materials have novel properties. Traditional predictive models for material properties are not accurate enough to guide the search. Herein, we use high-throughput measurements of optical properties to identify novel regions in three-cation metal oxide composition spaces by identifying compositions whose optical trends cannot be explained by simple phase mixtures. We screen 376,752 distinct compositions from 108 three-cation oxide systems based on the cation elements Mg, Fe, Co, Ni, Cu, Y, In, Sn, Ce, and Ta. Data models for candidate phase diagrams and three-cation compositions with emergent optical properties guide the discovery of materials with complex phase-dependent properties, as demonstrated by the discovery of a Co-Ta-Sn substitutional alloy oxide with tunable transparency, catalytic activity, and stability in strong acid electrolytes. These results required close coupling of data validation to experiment design to generate a reliable end-to-end high-throughput workflow for accelerating scientific discovery.

How to Look for Compounds: Predictive Screening and in situ Studies in Na-Zn-Bi System

Here, the combination of theoretical computations followed by rapid experimental screening and in situ diffraction studies is demonstrated as a powerful strategy for novel compounds discovery. When applied for the previously “empty” Na−Zn−Bi system, such an approach led to four novel phases. The compositional space of this system was rapidly screened via the hydride route method and the theoretically predicted NaZnBi (PbClF type, P4/nmm) and Na₁₁Zn₂Bi₅ (Na₁₁Cd₂Sb₅ type, P1‾ ) phases were successfully synthesized, while other computationally generated compounds on the list were rejected. In addition, single crystal X‐ray diffraction studies of NaZnBi indicate minor deviations from the stoichiometric 1 : 1 : 1 molar ratio. As a result, two isostructural (PbClF type, P4/nmm) Zn‐deficient phases with similar compositions, but distinctly different unit cell parameters were discovered. The vacancies on Zn sites and unit cell expansion were rationalized from bonding analysis using electronic structure calculations on stoichiometric “NaZnBi”. In‐situ synchrotron powder X‐ray diffraction studies shed light on complex equilibria in the Na−Zn−Bi system at elevated temperatures. In particular, the high‐temperature polymorph HT‐Na₃Bi (BiF₃ type, Fm3‾m) was obtained as a product of Na₁₁Zn₂Bi₅ decomposition above 611 K. HT‐Na₃Bi cannot be stabilized at room temperature by quenching, and this type of structure was earlier observed in the high‐pressure polymorph HP‐Na₃Bi above 0.5 GPa. The aforementioned approach of predictive synthesis can be extended to other multinary systems.

Gaussian Process Regression for Materials and Molecules

We provide an introduction to Gaussian process regression (GPR) machine-learning methods in computational materials science and chemistry. The focus of the present review is on the regression of atomistic properties: in particular, on the construction of interatomic potentials, or force fields, in the Gaussian Approximation Potential (GAP) framework; beyond this, we also discuss the fitting of arbitrary scalar, vectorial, and tensorial quantities. Methodological aspects of reference data generation, representation, and regression, as well as the question of how a data-driven model may be validated, are reviewed and critically discussed. A survey of applications to a variety of research questions in chemistry and materials science illustrates the rapid growth in the field. A vision is outlined for the development of the methodology in the years to come.

Simplifying inverse materials design problems for fixed lattices with alchemical chirality

Brute-force compute campaigns relying on demanding ab initio calculations routinely search for previously unknown materials in chemical compound space (CCS), the vast set of all conceivable stable combinations of elements and structural configurations. Here, we demonstrate that four-dimensional chirality arising from antisymmetry of alchemical perturbations dissects CCS and defines approximate ranks, which reduce its formal dimensionality and break down its combinatorial scaling. The resulting "alchemical" enantiomers have the same electronic energy up to the third order, independent of respective covalent bond topology, imposing relevant constraints on chemical bonding. Alchemical chirality deepens our understanding of CCS and enables the establishment of trends without empiricism for any materials with fixed lattices. We demonstrate the efficacy for three cases: (i) new rules for electronic energy contributions to chemical bonding; (ii) analysis of the electron density of BN-doped benzene; and (iii) ranking over 2000 and 4 million BN-doped naphthalene and picene derivatives, respectively.

Theoretical Prediction of Two-Dimensional Materials, Behavior, and Properties

Predictive modeling of two-dimensional (2D) materials is at the crossroad of two current rapidly growing interests: 2D materials per se, massively sought after and explored in experimental laboratories, and materials theoretical-computational models in general, flourishing on a fertile mix of condensed-matter physics and chemistry with advancing computational technology. Here the general methods and specific techniques of modeling are briefly overviewed, along with a somewhat philosophical assessment of what "prediction" is, followed by selected practical examples for 2D materials, from structures and properties, to device functionalities and synthetic routes for their making. We conclude with a brief sketch-outlook of future developments.

Rapid Recombination by Cadmium Vacancies in CdTe

CdTe is currently the largest thin-film photovoltaic technology. Non-radiative electron-hole recombination reduces the solar conversion efficiency from an ideal value of 32% to a current champion performance of 22%. The cadmium vacancy (V_Cd) is a prominent acceptor species in p-type CdTe; however, debate continues regarding its structural and electronic behavior. Using ab initio defect techniques, we calculate a negative-U double-acceptor level for V_Cd, while reproducing the V_Cd ^1- hole-polaron, reconciling theoretical predictions with experimental observations. We find the cadmium vacancy facilitates rapid charge-carrier recombination, reducing maximum power-conversion efficiency by over 5% for untreated CdTe-a consequence of tellurium dimerization, metastable structural arrangements, and anharmonic potential energy surfaces for carrier capture.

Perovskite-inspired materials for photovoltaics and beyond-from design to devices

Lead-halide perovskites have demonstrated astonishing increases in power conversion efficiency in photovoltaics over the last decade. The most efficient perovskite devices now outperform industry-standard multi-crystalline silicon solar cells, despite the fact that perovskites are typically grown at low temperature using simple solution-based methods. However, the toxicity of lead and its ready solubility in water are concerns for widespread implementation. These challenges, alongside the many successes of the perovskites, have motivated significant efforts across multiple disciplines to find lead-free and stable alternatives which could mimic the ability of the perovskites to achieve high performance with low temperature, facile fabrication methods. This Review discusses the computational and experimental approaches that have been taken to discover lead-free perovskite-inspired materials, and the recent successes and challenges in synthesizing these compounds. The atomistic origins of the extraordinary performance exhibited by lead-halide perovskites in photovoltaic devices is discussed, alongside the key challenges in engineering such high-performance in alternative, next-generation materials. Beyond photovoltaics, this Review discusses the impact perovskite-inspired materials have had in spurring efforts to apply new materials in other optoelectronic applications, namely light-emitting diodes, photocatalysts, radiation detectors, thin film transistors and memristors. Finally, the prospects and key challenges faced by the field in advancing the development of perovskite-inspired materials towards realization in commercial devices is discussed.

Reducing Time to Discovery: Materials and Molecular Modeling, Imaging, Informatics, and Integration

Multiscale and multimodal imaging of material structures and properties provides solid ground on which materials theory and design can flourish. Recently, KAIST announced 10 flagship research fields, which include KAIST Materials Revolution: Materials and Molecular Modeling, Imaging, Informatics and Integration (M3I3). The M3I3 initiative aims to reduce the time for the discovery, design and development of materials based on elucidating multiscale processing-structure-property relationship and materials hierarchy, which are to be quantified and understood through a combination of machine learning and scientific insights. In this review, we begin by introducing recent progress on related initiatives around the globe, such as the Materials Genome Initiative (U.S.), Materials Informatics (U.S.), the Materials Project (U.S.), the Open Quantum Materials Database (U.S.), Materials Research by Information Integration Initiative (Japan), Novel Materials Discovery (E.U.), the NOMAD repository (E.U.), Materials Scientific Data Sharing Network (China), Vom Materials Zur Innovation (Germany), and Creative Materials Discovery (Korea), and discuss the role of multiscale materials and molecular imaging combined with machine learning in realizing the vision of M3I3. Specifically, microscopies using photons, electrons, and physical probes will be revisited with a focus on the multiscale structural hierarchy, as well as structure-property relationships. Additionally, data mining from the literature combined with machine learning will be shown to be more efficient in finding the future direction of materials structures with improved properties than the classical approach. Examples of materials for applications in energy and information will be reviewed and discussed. A case study on the development of a Ni-Co-Mn cathode materials illustrates M3I3's approach to creating libraries of multiscale structure-property-processing relationships. We end with a future outlook toward recent developments in the field of M3I3.

Impact of noble-gas filler atoms on the lattice thermal conductivity of CoSb3skutterudites: first-principles modelling

We present a systematic first-principles modelling study of the structural dynamics and thermal transport in CoSb₃skutterudites with a series of noble-gas filler atoms. Filling with chemically-inert atoms provides an idealised model for isolating the effects of the fillers from the impact of redox changes to the host electronic structure. A range of analysis techniques are proposed to estimate the filler rattling frequencies, to quantify the separate impacts of the fillers on the phonon group velocities and lifetimes, and to show how changes to the phonon spectra and interaction strengths lead to suppressed lifetimes. The noble-gas fillers are found to reduce the thermal conductivity of the CoSb₃framework by up to 15% primarily by suppressing the group velocities of low-lying optic modes. The filler rattling frequencies are determined by a detailed balance of increasing atomic mass and stronger interactions with the framework, and are found to be a good predictor of the impact on the heat transport. Lowering the rattling frequency below ∼1.5 THz by selecting heavy fillers that interact weakly with the framework is predicted to lead to a much larger suppression of the thermal transport, by inducing avoided crossings in the acoustic-mode dispersion and facilitating enhanced scattering and a consequent large reduction in phonon lifetimes. Approximate rattling frequencies determined from the harmonic force constants may therefore provide a useful metric for selecting filler atoms to optimise the thermal transport in skutterudites and other cage compounds such as clathrates.

Polymorphism in Post-Dichalcogenide Two-Dimensional Materials

Two-dimensional (2D) materials exhibit a wide range of atomic structures, compositions, and associated versatility of properties. Furthermore, for a given composition, a variety of different crystal structures (i.e., polymorphs) can be observed. Polymorphism in 2D materials presents a fertile landscape for designing novel architectures and imparting new functionalities. The objective of this Review is to identify the polymorphs of emerging 2D materials, describe their polymorph-dependent properties, and outline methods used for polymorph control. Since traditional 2D materials (e.g., graphene, hexagonal boron nitride, and transition metal dichalcogenides) have already been studied extensively, the focus here is on polymorphism in post-dichalcogenide 2D materials including group III, IV, and V elemental 2D materials, layered group III, IV, and V metal chalcogenides, and 2D transition metal halides. In addition to providing a comprehensive survey of recent experimental and theoretical literature, this Review identifies the most promising opportunities for future research including how 2D polymorph engineering can provide a pathway to materials by design.

Engineering structure and functionalities of chemical vapor deposited photocatalytic titanium dioxide films through different types of precursors

The functional properties of photocatalytic CVD processed TiO₂ films can be monitored by appropriately selecting the precursors chemistry.

Molecular Simulation and Statistical Learning Methods toward Predicting Drug-Polymer Amorphous Solid Dispersion Miscibility, Stability, and Formulation Design

Amorphous solid dispersions (ASDs) have emerged as widespread formulations for drug delivery of poorly soluble active pharmaceutical ingredients (APIs). Predicting the API solubility with various carriers in the API-carrier mixture and the principal API-carrier non-bonding interactions are critical factors for rational drug development and formulation decisions. Experimental determination of these interactions, solubility, and dissolution mechanisms is time-consuming, costly, and reliant on trial and error. To that end, molecular modeling has been applied to simulate ASD properties and mechanisms. Quantum mechanical methods elucidate the strength of API-carrier non-bonding interactions, while molecular dynamics simulations model and predict ASD physical stability, solubility, and dissolution mechanisms. Statistical learning models have been recently applied to the prediction of a variety of drug formulation properties and show immense potential for continued application in the understanding and prediction of ASD solubility. Continued theoretical progress and computational applications will accelerate lead compound development before clinical trials. This article reviews in silico research for the rational formulation design of low-solubility drugs. Pertinent theoretical groundwork is presented, modeling applications and limitations are discussed, and the prospective clinical benefits of accelerated ASD formulation are envisioned.

Artificial intelligence for photonics and photonic materials

Artificial intelligence (AI) is the most important new methodology in scientific research since the adoption of quantum mechanics and it is providing exciting results in numerous fields of science and technology. In this review we summarize research and discuss future opportunities for AI in the domains of photonics, nanophotonics, plasmonics and photonic materials discovery, including metamaterials.

Solution Combustion Synthesis of Transparent Conducting Thin Films for Sustainable Photovoltaic Applications

Sunlight is arguably the most promising continuous and cheap alternative sustainable energy source available at almost all living places of the human world. Photovoltaics (PV) is a process of direct conversion of sunlight into electricity and has become a technology of choice for sustainable production of cleaner and safer energy. The solar cell is the main component of any PV technology and transparent conducting oxides (TCO) comprising wide band gap semiconductors are an essential component of every PV technology. In this research, transparent conducting thin films were prepared by solution combustion synthesis of metal oxide nitrates wherein the use of indium is substituted or reduced. Individual 0.5 M indium, gallium and zinc oxide source solutions were mixed in ratios of 1:9 and 9:1 to obtain precursor solutions. Indium-rich IZO (A1), zinc-rich IZO (B1), gallium-rich GZO (C1) and zinc-rich GZO (D1) thin films were prepared through spin coating deposition. In the case of A1 and B1 thin films, electrical resistivity obtained was 3.4 × 10−3 Ω-cm and 7.9 × 10−3 Ω-cm, respectively. While C1 films remained insulating, D1 films showed an electrical resistivity of 1.3 × 10−2 Ω-cm. The optical transmittance remained more than 80% in visible for all films. Films with necessary transparent conducting properties were applied in an all solution-processed solar cell device and then characterized. The efficiency of 1.66%, 2.17%, and 0.77% was obtained for A1, B1, and D1 TCOs, respectively, while 6.88% was obtained using commercial fluorine doped SnO2: (FTO) TCO. The results are encouraging for the preparation of indium-free TCOs towards solution-processed thin-film photovoltaic devices. It is also observed that better filtration of precursor solutions and improving surface roughness would further reduce sheet resistance and improve solar cell efficiency.

Can we predict materials that can be synthesised?

The discovery of materials is an important element in the development of new technologies and abilities that can help humanity tackle many challenges. Materials discovery is frustratingly slow, with the large time and resource cost often providing only small gains in property performance. Furthermore, researchers are unwilling to take large risks that they will only know the outcome of months or years later. Computation is playing an increasing role in allowing rapid screening of large numbers of materials from vast search space to identify promising candidates for laboratory synthesis and testing. However, there is a problem, in that many materials computationally predicted to have encouraging properties cannot be readily realised in the lab. This minireview looks at how we can tackle the problem of confirming that hypothetical materials are synthetically realisable, through consideration of all the stages of the materials discovery process, from obtaining the components, reacting them to a material in the correct structure, through to processing into a desired form. In an ideal world, a material prediction would come with an associated 'recipe' for the successful laboratory preparation of the material. We discuss the opportunity to thus prevent wasted effort in experimental discovery programmes, including those using automation, to accelerate the discovery of novel materials.

Artificial neural networks for predicting charge transfer coupling

Quantum chemistry calculations have been very useful in providing many key detailed properties and enhancing our understanding of molecular systems. However, such calculation, especially with ab initio models, can be time-consuming. For example, in the prediction of charge-transfer properties, it is often necessary to work with an ensemble of different thermally populated structures. A possible alternative to such calculations is to use a machine-learning based approach. In this work, we show that the general prediction of electronic coupling, a property that is very sensitive to intermolecular degrees of freedom, can be obtained with artificial neural networks, with improved performance as compared to the popular kernel ridge regression method. We propose strategies for optimizing the learning rate and batch size, improving model performance, and further evaluating models to ensure that the physical signatures of charge-transfer coupling are well reproduced. We also address the effect of feature representation as well as statistical insights obtained from the loss function and the data structure. Our results pave the way for designing a general strategy for training such neural-network models for accurate prediction.

Recent advances in bioelectronics chemistry

Research in bioelectronics is highly interdisciplinary, with many new developments being based on techniques from across the physical and life sciences. Advances in our understanding of the fundamental chemistry underlying the materials used in bioelectronic applications have been a crucial component of many recent discoveries. In this review, we highlight ways in which a chemistry-oriented perspective may facilitate novel and deep insights into both the fundamental scientific understanding and the design of materials, which can in turn tune the functionality and biocompatibility of bioelectronic devices. We provide an in-depth examination of several developments in the field, organized by the chemical properties of the materials. We conclude by surveying how some of the latest major topics of chemical research may be further integrated with bioelectronics.