Text-mined dataset of inorganic materials synthesis recipes

Artificial Intelligence Applied to Battery Research: Hype or Reality?

This is a critical review of artificial intelligence/machine learning (AI/ML) methods applied to battery research. It aims at providing a comprehensive, authoritative, and critical, yet easily understandable, review of general interest to the battery community. It addresses the concepts, approaches, tools, outcomes, and challenges of using AI/ML as an accelerator for the design and optimization of the next generation of batteries—a current hot topic. It intends to create both accessibility of these tools to the chemistry and electrochemical energy sciences communities and completeness in terms of the different battery R&D aspects covered.

In Situ Mechanistic Studies of Two Divergent Synthesis Routes Forming the Heteroanionic BiOCuSe

Heteroanionic materials are a burgeoning class of compounds that offer new properties via the targeted selection of anions. However, understanding the design principles and achieving successful syntheses of new materials in this class are in their infancy. To obtain mechanistic insight and a panoramic view of the reaction progression from beginning to end of the formation of a heteroanionic material, we selected BiOCuSe, a well-known thermoelectric compound, and utilized in situ synchrotron powder diffraction as a function of temperature and time. BiOCuSe is a layered material, which crystallizes in a common mixed anion structure type: ZrSiAsFe. Two reactions of starting materials (Bi₂O₂Se + Cu₂Se and Bi₂O₃ + Bi + 3Cu + 3Se) were studied to determine the effect of precursors on the reaction pathway. Our in situ investigation shows that the ternary-binary Bi₂O₂Se + Cu₂Se reaction proceeds without intermediates to directly form BiOCuSe, while the binary-elemental Bi₂O₃ + Bi + 3Cu + 3Se reaction generates many intermediates before the final product forms. These intermediates include CuSe, Bi₃Se₄, Bi₂Se₃, and Cu₂Se. While the stoichiometric loading of the precursors necessarily dictates the identity of the first intermediates, kinetics also plays a contributing role in stabilizing unexpected intermediates such as CuSe and Bi₃Se₄. Understanding and establishing a link between the selection of precursors and the reaction pathways improves the potential for rational synthesis of heteroanionic materials and solid-state reactions in general.

Toward autonomous design and synthesis of novel inorganic materials

Autonomous experimentation driven by artificial intelligence (AI) provides an exciting opportunity to revolutionize inorganic materials discovery and development. Herein, we review recent progress in the design of self-driving laboratories, including robotics to automate materials synthesis and characterization, in conjunction with AI to interpret experimental outcomes and propose new experimental procedures. We focus on efforts to automate inorganic synthesis through solution-based routes, solid-state reactions, and thin film deposition. In each case, connections are made to relevant work in organic chemistry, where automation is more common. Characterization techniques are primarily discussed in the context of phase identification, as this task is critical to understand what products have formed during synthesis. The application of deep learning to analyze multivariate characterization data and perform phase identification is examined. To achieve "closed-loop" materials synthesis and design, we further provide a detailed overview of optimization algorithms that use active learning to rationally guide experimental iterations. Finally, we highlight several key opportunities and challenges for the future development of self-driving inorganic materials synthesis platforms.

IP Analytics and Machine Learning Applied to Create Process Visualization Graphs for Chemical Utility Patents

Researchers must read and understand a large volume of technical papers, including patent documents, to fully grasp the state-of-the-art technological progress in a given domain. Chemical research is particularly challenging with the fast growth of newly registered utility patents (also known as intellectual property or IP) that provide detailed descriptions of the processes used to create a new chemical or a new process to manufacture a known chemical. The researcher must be able to understand the latest patents and literature in order to develop new chemicals and processes that do not infringe on existing claims and processes. This research uses text mining, integrated machine learning, and knowledge visualization techniques to effectively and accurately support the extraction and graphical presentation of chemical processes disclosed in patent documents. The computer framework trains a machine learning model called ALBERT for automatic paragraph text classification. ALBERT separates chemical and non-chemical descriptive paragraphs from a patent for effective chemical term extraction. The ChemDataExtractor is used to classify chemical terms, such as inputs, units, and reactions from the chemical paragraphs. A computer-supported graph-based knowledge representation interface is developed to plot the extracted chemical terms and their chemical process links as a network of nodes with connecting arcs. The computer-supported chemical knowledge visualization approach helps researchers to quickly understand the innovative and unique chemical or processes of any chemical patent of interest.

Rational Solid-State Synthesis Routes for Inorganic Materials

The rational solid-state synthesis of inorganic compounds is formulated as catalytic nucleation on crystalline reactants, where contributions of reaction and interfacial energies to the nucleation barriers are approximated from high-throughput thermochemical data and structural and interfacial features of crystals, respectively. Favorable synthesis reactions are then identified by a Pareto analysis of relative nucleation barriers and phase selectivities of reactions leading to the target. We demonstrate the application of this approach in reaction planning for the solid-state synthesis of a range of compounds, including the widely studied oxides LiCoO₂, BaTiO₃, and YBa₂Cu₃O₇, as well as other metal oxide, oxyfluoride, phosphate, and nitride targets. Pathways for enabling the retrosynthesis of inorganics are also discussed.

Observing and Modeling the Sequential Pairwise Reactions that Drive Solid-State Ceramic Synthesis

Solid-state synthesis from powder precursors is the primary processing route to advanced multicomponent ceramic materials. Designing reaction conditions and precursors for ceramic synthesis can be a laborious, trial-and-error process, as heterogeneous mixtures of precursors often evolve through a complicated series of reaction intermediates. Here, ab initio thermodynamics is used to model which pair of precursors has the most reactive interface, enabling the understanding and anticipation of which non-equilibrium intermediates form in the early stages of a solid-state reaction. In situ X-ray diffraction and in situ electron microscopy are then used to observe how these initial intermediates influence phase evolution in the synthesis of the classic high-temperature superconductor YBa₂ Cu₃ O_{6+ x}   (YBCO). The model developed herein rationalizes how the replacement of the traditional BaCO₃ precursor with BaO₂ redirects phase evolution through a low-temperature eutectic melt, facilitating the formation of YBCO in 30 min instead of 12+ h. Precursor selection plays an important role in tuning the thermodynamics of interfacial reactions and emerges as an important design parameter in planning kinetically favorable synthesis pathways to complex ceramic materials.

Sentence, Phrase, and Triple Annotations to Build a Knowledge Graph of Natural Language Processing Contributions—A Trial Dataset

Purpose

This work aims to normalize the NlpContributions scheme (henceforward, NlpContributionGraph) to structure, directly from article sentences, the contributions information in Natural Language Processing (NLP) scholarly articles via a two-stage annotation methodology: 1) pilot stage—to define the scheme (described in prior work); and 2) adjudication stage—to normalize the graphing model (the focus of this paper).

Design/methodology/approach

We re-annotate, a second time, the contributions-pertinent information across 50 prior-annotated NLP scholarly articles in terms of a data pipeline comprising: contribution-centered sentences, phrases, and triple statements. To this end, specifically, care was taken in the adjudication annotation stage to reduce annotation noise while formulating the guidelines for our proposed novel NLP contributions structuring and graphing scheme.

Findings

The application of NlpContributionGraph on the 50 articles resulted finally in a dataset of 900 contribution-focused sentences, 4,702 contribution-information-centered phrases, and 2,980 surface-structured triples. The intra-annotation agreement between the first and second stages, in terms of F1-score, was 67.92% for sentences, 41.82% for phrases, and 22.31% for triple statements indicating that with increased granularity of the information, the annotation decision variance is greater.

Research limitations

NlpContributionGraph has limited scope for structuring scholarly contributions compared with STEM (Science, Technology, Engineering, and Medicine) scholarly knowledge at large. Further, the annotation scheme in this work is designed by only an intra-annotator consensus—a single annotator first annotated the data to propose the initial scheme, following which, the same annotator reannotated the data to normalize the annotations in an adjudication stage. However, the expected goal of this work is to achieve a standardized retrospective model of capturing NLP contributions from scholarly articles. This would entail a larger initiative of enlisting multiple annotators to accommodate different worldviews into a “single” set of structures and relationships as the final scheme. Given that the initial scheme is first proposed and the complexity of the annotation task in the realistic timeframe, our intra-annotation procedure is well-suited. Nevertheless, the model proposed in this work is presently limited since it does not incorporate multiple annotator worldviews. This is planned as future work to produce a robust model.

Practical implications

We demonstrate NlpContributionGraph data integrated into the Open Research Knowledge Graph (ORKG), a next-generation KG-based digital library with intelligent computations enabled over structured scholarly knowledge, as a viable aid to assist researchers in their day-to-day tasks.

Originality/value

NlpContributionGraph is a novel scheme to annotate research contributions from NLP articles and integrate them in a knowledge graph, which to the best of our knowledge does not exist in the community. Furthermore, our quantitative evaluations over the two-stage annotation tasks offer insights into task difficulty.

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.

Opportunities and challenges of text mining in aterials research

Research publications are the major repository of scientific knowledge. However, their unstructured and highly heterogenous format creates a significant obstacle to large-scale analysis of the information contained within. Recent progress in natural language processing (NLP) has provided a variety of tools for high-quality information extraction from unstructured text. These tools are primarily trained on non-technical text and struggle to produce accurate results when applied to scientific text, involving specific technical terminology. During the last years, significant efforts in information retrieval have been made for biomedical and biochemical publications. For materials science, text mining (TM) methodology is still at the dawn of its development. In this review, we survey the recent progress in creating and applying TM and NLP approaches to materials science field. This review is directed at the broad class of researchers aiming to learn the fundamentals of TM as applied to the materials science publications.

Prospects for lithium-ion batteries and beyond-a 2030 vision

It would be unwise to assume ‘conventional’ lithium-ion batteries are approaching the end of their era and so we discuss current strategies to improve the current and next generation systems, where a holistic approach will be needed to unlock higher energy density while also maintaining lifetime and safety. We end by briefly reviewing areas where fundamental science advances will be needed to enable revolutionary new battery systems.

Polymer Nanocomposite Data: Curation, Frameworks, Access, and Potential for Discovery and Design

With the advent of the materials genome initiative (MGI) in the United States and a similar focus on materials data around the world, a number of materials data resources and associated vocabularies, tools, and repositories have been developed. While the majority of systems focus on slices of computational data with an emphasis on metallic alloys, NanoMine is an open source platform with the goal of curating and storing widely varying experimental data on polymer nanocomposites (polymers doped with nanoparticles) and providing access to characterization and analysis tools with the long-term objective of promoting facile nanocomposite design. Data on over 2500 samples from the literature and individual laboratories has been curated to date into NanoMine, including 230 samples from the papers bound in this virtual issue. This virtual issue represents an experiment of the flexibility of the data repository to capture the unique experimental metadata requirements of many data sets at one time and to challenge the authors to participate in the curation of their research data associated with a given publication. In principle, NanoMine offers a FAIR platform in which data published in papers becomes directly Findable and Accessible via simple search tools, with open metadata standards that are Interoperable with larger materials data registries, and allows easy Reuse of data, e.g. benchmarking against new results. Our hope is that with time, platforms such as this one could capture much of the newly published data on materials and form nodes in an interconnected materials data ecosystem which would allow researchers to robustly archive their data, add to the growing body of readily accessible data, and enable new forms of discovery by application of data analysis and design tools.

Promising photovoltaic efficiency of a layered silicon oxide crystal Si3O

Computational searching and screening of new functional materials exploiting Earth abundant elements can accelerate the development of their energy applications. Based on the state-of-the-art material search algorithm and ab initio calculations, we demonstrate a recently suggested stable silicon oxide with a layered structure (Si₃O) as an ideal photovoltaic material. With many-body first-principles approaches, the monolayer and layered bulk of Si₃O show direct quasiparticle gaps of 1.85 eV and 1.25 eV, respectively, while an optical gap of about 1.2 eV is nearly independent of the number of layers. Spectroscopic limited maximum efficiency (SLME) is estimated to be 27% for a thickness of 0.5 μm, making it a promising candidate for solar energy applications.

Property-Oriented Material Design Based on a Data-Driven Machine Learning Technique

Property-oriented material design is a persistent pursuit for material scientists. Recently, machine learning (ML) as a powerful new tool has attracted worldwide attention in the material design field. Based on statistics instead of solving physical equations, ML can predict material properties faster with lower cost. Because of its data-driven characteristics, the quantity and quality of material data become the keys to the practical applications of this technique. In this Perspective, problems caused by lack of data and diversity of data are discussed. Various approaches, including high-throughput calculations, database construction, feedback loop algorithms, and better descriptors, have been exploited to address these problems. It is expected that this Perspective will bring data itself to the forefront of ML-based material design.

Perovskite Oxides Prepared by Hydrothermal and Solvothermal Synthesis: A Review of Crystallisation, Chemistry, and Compositions

Perovskite oxides with general composition ABO₃ are a large group of inorganic materials that can contain a variety of cations from all parts of the Periodic Table and that have diverse properties of application in fields ranging from electronics, energy storage to photocatalysis. Solvothermal synthesis routes to these materials have become increasingly investigated in the past decade as a means of direct crystallisation of the solids from solution. These methods have significant advantages leading to adjustment of crystal form from the nanoscale to the micron-scale, the isolation of compositions not possible using conventional solid-state synthesis and in addition may lead to scalable processes for producing materials at moderate temperatures. These aspects are reviewed, with examples taken from the past decade's literature on the solvothermal synthesis of perovskites with a systematic survey of B-site cations, from transition metals in Groups 4-8 and main group elements in Groups 13, 14 and 15, to solid solutions and heterostructures. As well as hydrothermal reactions, the use of various solvents and solution additives are discussed and some trends identified, along with prospects for developing control and predictability in the crystallisation of complex oxide materials.

Nanomaterial Synthesis Insights from Machine Learning of Scientific Articles by Extracting, Structuring, and Visualizing Knowledge

Nanomaterials of varying compositions and morphologies are of interest for many applications from catalysis to optics, but the synthesis of nanomaterials and their scale-up are most often time-consuming and Edisonian processes. Information gleaned from the scientific literature can help inform and accelerate nanomaterials development, but again, searching the literature and digesting the information are time-consuming manual processes for researchers. To help address these challenges, we developed scientific article-processing tools that extract and structure information from the text and figures of nanomaterials articles, thereby enabling the creation of a personalized knowledgebase for nanomaterials synthesis that can be mined to help inform further nanomaterials development. Starting with a corpus of ∼35k nanomaterials-related articles, we developed models to classify articles according to the nanomaterial composition and morphology, extract synthesis protocols from within the articles' text, and extract, normalize, and categorize chemical terms within synthesis protocols. We demonstrate the efficiency of the proposed pipeline on an expert-labeled set of nanomaterials synthesis articles, achieving 100% accuracy on composition prediction, 95% accuracy on morphology prediction, 0.99 AUC on protocol identification, and up to a 0.87 F1-score on chemical entity recognition. In addition to processing articles' text, microscopy images of nanomaterials within the articles are also automatically identified and analyzed to determine the nanomaterials' morphologies and size distributions. To enable users to easily explore the database, we developed a complementary browser-based visualization tool that provides flexibility in comparing across subsets of articles of interest. We use these tools and information to identify trends in nanomaterials synthesis, such as the correlation of certain reagents with various nanomaterial morphologies, which is useful in guiding hypotheses and reducing the potential parameter space during experimental design.

Text-mined dataset of inorganic materials synthesis recipes

Materials discovery has become significantly facilitated and accelerated by high-throughput ab-initio computations. This ability to rapidly design interesting novel compounds has displaced the materials innovation bottleneck to the development of synthesis routes for the desired material. As there is no a fundamental theory for materials synthesis, one might attempt a data-driven approach for predicting inorganic materials synthesis, but this is impeded by the lack of a comprehensive database containing synthesis processes. To overcome this limitation, we have generated a dataset of “codified recipes” for solid-state synthesis automatically extracted from scientific publications. The dataset consists of 19,488 synthesis entries retrieved from 53,538 solid-state synthesis paragraphs by using text mining and natural language processing approaches. Every entry contains information about target material, starting compounds, operations used and their conditions, as well as the balanced chemical equation of the synthesis reaction. The dataset is publicly available and can be used for data mining of various aspects of inorganic materials synthesis.

Measurement(s)	solid-state synthesis data
Technology Type(s)	natural language processing

Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.9906608