ChemDataExtractor 2.0: Autopopulated Ontologies for Materials Science

The ever-growing abundance of data found in heterogeneous sources, such as scientific publications, has forced the development of automated techniques for data extraction. While in the past, in the physical sciences domain, the focus has been on the precise extraction of individual properties, attention has recently been devoted to the extraction of higher-level relationships. Here, we present a framework for an automated population of ontologies. That is, the direct extraction of a larger group of properties linked by a semantic network. We exploit data-rich sources, such as tables within documents, and present a new model concept that enables data extraction for chemical and physical properties with the ability to organize hierarchical data as nested information. Combining these capabilities with automatically generated parsers for data extraction and forward-looking interdependency resolution, we illustrate the power of our approach via the automatic extraction of a crystallographic hierarchy of information. This includes 18 interrelated submodels of nested data, extracted from an evaluation set of scientific articles, yielding an overall precision of 92.2%, across 26 different journals. Our method and associated toolkit, ChemDataExtractor 2.0, offers a key step toward the seamless integration of primary literature sources into a data-driven scientific framework.

Nanooptomechanical Transduction in a Single Crystal with 100% Photoconversion

Materials that exhibit nanooptomechanical transduction in their single-crystal form have prospective use in light-driven molecular machinery, nanotechnology, and quantum computing. Linkage photoisomerization is typically the source of such transduction in coordination complexes, although the isomers tend to undergo only partial photoconversion. We present a nanooptomechanical transducer, trans-[Ru(SO2)(NH3)4(3-bromopyridine)]tosylate2, whose S-bound η1-SO2 isomer fully converts into an O-bound η1-OSO photoisomer that is metastable while kept at 100 K. Its 100% photoconversion is confirmed structurally via photocrystallography, while single-crystal optical absorption and Raman spectroscopies reveal its metal-to-ligand charge-transfer and temperature-dependent characteristics. This perfect optical switching affords the material good prospects for nanooptomechanical transduction with single-photon control.

Bayesian Particle Instance Segmentation for Electron Microscopy Image Quantification

Automating the analysis portion of materials characterization by electron microscopy (EM) has the potential to accelerate the process of scientific discovery. To this end, we present a Bayesian deep-learning model for semantic segmentation and localization of particle instances in EM images. These segmentations can subsequently be used to compute quantitative measures such as particle-size distributions, radial- distribution functions, average sizes, and aspect ratios of the particles in an image. Moreover, by making use of the epistemic uncertainty of our model, we obtain uncertainty estimates of its outputs and use these to filter out false-positive predictions and hence produce more accurate quantitative measures. We incorporate our method into the ImageDataExtractor package, as ImageDataExtractor 2.0, which affords a full pipeline to automatically extract particle information for large-scale data-driven materials discovery. Finally, we present and make publicly available the Electron Microscopy Particle Segmentation (EMPS) data set. This is the first human-labeled particle instance segmentation data set, consisting of 465 EM images and their corresponding semantic instance segmentation maps.

Electrolyte/Dye/TiO2 Interfacial Structures of Dye-Sensitized Solar Cells Revealed by In Situ Neutron Reflectometry with Contrast Matching

The nature of an interfacial structure buried within a device assembly is often critical to its function. For example, the dye/TiO₂ interfacial structure that comprises the working electrode of a dye-sensitized solar cell (DSC) governs its photovoltaic output. These structures have been determined outside of the DSC device, using ex situ characterization methods; yet, they really should be probed while held within a DSC since they are modulated by the device environment. Dye/TiO₂ structures will be particularly influenced by a layer of electrolyte ions that lies above the dye self-assembly. We show that electrolyte/dye/TiO₂ interfacial structures can be resolved using in situ neutron reflectometry with contrast matching. We find that electrolyte constituents ingress into the self-assembled monolayer of dye molecules that anchor onto TiO₂. Some dye/TiO₂ anchoring configurations are modulated by the formation of electrolyte/dye intermolecular interactions. These electrolyte-influencing structural changes will affect dye-regeneration and electron-injection DSC operational processes. This underpins the importance of this in situ structural determination of electrolyte/dye/TiO₂ interfaces within representative DSC device environments.

Rapid build up of nanooptomechanical transduction in single crystals of a ruthenium-based SO2 linkage photoisomer

Single-crystal nanooptomechanical transduction occurs in [Ru(SO₂)(NH₃)₄(H₂O)]chlorobenzenesulfonate₂, reaching maximal levels within 40 s at 100 K when photostimulated by 505 nm light. Its in situ light-induced crystal structure reveals the molecular origins of this optical actuation: 26.0(3)% of the η¹-SO₂ ligand photoconverts into an η¹-OSO photoisomer which, in turn, induces a 49.6(9)° arene ring rotation in its neighbouring counter ion.

Low-energy optical switching of SO2 linkage isomerisation in single crystals of a ruthenium-based coordination complex

Single crystals that behave as optical switches are desirable for a wide range of applications, from optical sensors to read–write memory media. A series of ruthenium-based complexes that exhibit optical switching in their single-crystal form via SO₂ linkage photoisomerisation are of prospective interest for these technologies. This study explores the optical switching behaviour in one such complex, trans-[Ru(SO₂)(NH₃)₄(H₂O)]tosylate₂ (1), in terms of its dark and photoinduced crystal structure, as well as its light and thermal decay characteristics, which are deduced by photocrystallography, single-crystal optical absorption spectroscopy and microscopy. Photocrystallography results reveal that a photoisomerisation level of 21.5(5)% is achievable in 1. Biphasic photochromic crystals of 1 were generated by applying green and then red light to switch on and off the η²-(OS)O photoisomer in different regions of a crystal. Heat is a known alternative to its thermal decay, whereby a method is demonstrated that employs optical absorption spectra to determine its activation energy of 30 kJ mol⁻¹. This low-energy barrier to optical switching agrees well with computational studies on 1, as well as being comparable to activation energies in ruthenium-based nitrosyl linkage photoisomers that also display solid-state optical switching.

Single crystals that behave as optical switches are desirable for a wide range of applications, from optical sensors to read–write memory media.

Enumerating Intramolecular Charge Transfer in Conjugated Organic Compounds

Charge transfer across conjugated organic molecules is the functional basis of many optoelectronic and semiconductor devices. The ability to design such molecules to suit a given device application is highly desirable; yet, realizing this prospect is impeded by the lack of an algorithm that quantifies the extent of intramolecular charge transfer (ICT) in absolute terms. In turn, an algorithm to describe ICT is held back by a poor definition of one of its key dependent terms: conjugation. Current equations assume that π-bonding operates solely across two bonds, even though conjugation extends beyond these limits, and such equations only yield relative measures of π-conjugation. This work presents a four-step algorithm that enumerates ICT on an absolute scale. The method is applied successfully to four types of optoelectronic materials; results demonstrate the need to reconsider certain fundamental chemical-bonding and ICT concepts for conjugated molecules. These findings have implications for all optoelectronic and semiconducting materials.

3-D Inorganic Crystal Structure Generation and Property Prediction via Representation Learning

Generative models have been successfully used to synthesize completely novel images, text, music, and speech. As such, they present an exciting opportunity for the design of new materials for functional applications. So far, generative deep-learning methods applied to molecular and drug discovery have yet to produce stable and novel 3-D crystal structures across multiple material classes. To that end, we, herein, present an autoencoder-based generative deep-representation learning pipeline for geometrically optimized 3-D crystal structures that simultaneously predicts the values of eight target properties. The system is highly general, as demonstrated through creation of novel materials from three separate material classes: binary alloys, ternary perovskites, and Heusler compounds. Comparison of these generated structures to those optimized via electronic-structure calculations shows that our generated materials are valid and geometrically optimized.

A database of battery materials auto-generated using ChemDataExtractor

A database of battery materials is presented which comprises a total of 292,313 data records, with 214,617 unique chemical-property data relations between 17,354 unique chemicals and up to five material properties: capacity, voltage, conductivity, Coulombic efficiency and energy. 117,403 data are multivariate on a property where it is the dependent variable in part of a data series. The database was auto-generated by mining text from 229,061 academic papers using the chemistry-aware natural language processing toolkit, ChemDataExtractor version 1.5, which was modified for the specific domain of batteries. The collected data can be used as a representative overview of battery material information that is contained within text of scientific papers. Public availability of these data will also enable battery materials design and prediction via data-science methods. To the best of our knowledge, this is the first auto-generated database of battery materials extracted from a relatively large number of scientific papers. We also provide a Graphical User Interface (GUI) to aid the use of this database.

ChemSchematicResolver: A Toolkit to Decode 2D Chemical Diagrams with Labels and R-Groups into Annotated Chemical Named Entities

The number of journal articles in the scientific domain has grown to the point where it has become impossible for researchers to capitalize on all findings in their relevant discipline. Information is stored in these articles in a number of ways, including figures that describe important results. In organic chemistry, these figures often present chemical schematic diagrams that graphically define the structures of carbon-based compounds. These diagrams are intuitive for an expert to comprehend, but they are not designed for machines. This work presents ChemSchematicResolver, a software tool that can be used to identify chemical schematic diagrams within the figure of a document, resolve any R-group substituents within them, and convert the resulting diagrams to a machine-readable format in a high-throughput, autonomous fashion. The tool includes a new algorithm that is used to identify relevant diagrams and a mechanism that combines these data with contextual information from the rest of the document for the creation of highly relational databases. It includes support for a variety of general R-group structures, the first time this is available in any open-source chemical schematic diagram extraction tool. It is presented alongside a self-generated evaluation set, on which the most important assessment metric, precision, achieved 83-100% for all assessed areas. The ChemSchematicResolver tool is released under the MIT license and is available to download from www.chemschematicresolver.org.

A Design-to-Device Pipeline for Data-Driven Materials Discovery

The world needs new materials to stimulate the chemical industry in key sectors of our economy: environment and sustainability, information storage, optical telecommunications, and catalysis. Yet, nearly all functional materials are still discovered by "trial-and-error", of which the lack of predictability affords a major materials bottleneck to technological innovation. The average "molecule-to-market" lead time for materials discovery is currently 20 years. This is far too long for industrial needs, as highlighted by the Materials Genome Initiative, which has ambitious targets of up to 4-fold reductions in average molecule-to-market lead times. Such a large step change in progress can only be realistically achieved if one adopts an entirely new approach to materials discovery. Fortunately, a fundamentally new approach to materials discovery has been emerging, whereby data science with artificial intelligence offers a prospective solution to speed up these average molecule-to-market lead times.This approach is known as data-driven materials discovery. Its broad prospects have only recently become a reality, given the timely and major advances in "big data", artificial intelligence, and high-performance computing (HPC). Access to massive data sets has been stimulated by government-regulated open-access requirements for data and literature. Natural-language processing (NLP) and machine-learning (ML) tools that can mine data and find patterns therein are becoming mainstream. Exascale HPC capabilities that can aid data mining and pattern recognition and also generate their own data from calculations are now within our grasp. These timely advances present an ideal opportunity to develop data-driven materials-discovery strategies to systematically design and predict new chemicals for a given device application.This Account shows how data science can afford materials discovery via a four-step "design-to-device" pipeline that entails (1) data extraction, (2) data enrichment, (3) material prediction, and (4) experimental validation. Massive databases of cognate chemical and property information are first forged from "chemistry-aware" natural-language-processing tools, such as ChemDataExtractor, and enriched using machine-learning methods and high-throughput quantum-chemical calculations. New materials for a bespoke application can then be predicted by mining these databases with algorithmic encodings of relationships between chemical structures and physical properties that are known to deliver functional materials. These may take the form of classification, enumeration, or machine-learning algorithms. A data-mining workflow short-lists these predictions to a handful of lead candidate materials that go forward to experimental validation. This design-to-device approach is being developed to offer a roadmap for the accelerated discovery of new chemicals for functional applications. Case studies presented demonstrate its utility for photovoltaic, optical, and catalytic applications. While this Account is focused on applications in the physical sciences, the generic pipeline discussed is readily transferable to other scientific disciplines such as biology and medicine.

Single-Shot Multi-keV X-Ray Absorption Spectroscopy Using an Ultrashort Laser-Wakefield Accelerator Source

Single-shot absorption measurements have been performed using the multi-keV x rays generated by a laser-wakefield accelerator. A 200 TW laser was used to drive a laser-wakefield accelerator in a mode which produced broadband electron beams with a maximum energy above 1 GeV and a broad divergence of ≈15  mrad FWHM. Betatron oscillations of these electrons generated 1.2±0.2×10^{6}  photons/eV in the 5 keV region, with a signal-to-noise ratio of approximately 300∶1. This was sufficient to allow high-resolution x-ray absorption near-edge structure measurements at the K edge of a titanium sample in a single shot. We demonstrate that this source is capable of single-shot, simultaneous measurements of both the electron and ion distributions in matter heated to eV temperatures by comparison with density functional theory simulations. The unique combination of a high-flux, large bandwidth, few femtosecond duration x-ray pulse synchronized to a high-power laser will enable key advances in the study of ultrafast energetic processes such as electron-ion equilibration.

Comparative dataset of experimental and computational attributes of UV/vis absorption spectra

The ability to auto-generate databases of optical properties holds great prospects in data-driven materials discovery for optoelectronic applications. We present a cognate set of experimental and computational data that describes key features of optical absorption spectra. This includes an auto-generated database of 18,309 records of experimentally determined UV/vis absorption maxima, λ_max, and associated extinction coefficients, ϵ, where present. This database was produced using the text-mining toolkit, ChemDataExtractor, on 402,034 scientific documents. High-throughput electronic-structure calculations using fast (simplified Tamm-Dancoff approach) and traditional (time-dependent) density functional theory were executed to predict λ_max and oscillation strengths, f (related to ϵ) for a subset of validated compounds. Paired quantities of these computational and experimental data show strong correlations in λ_max, f and ϵ, laying the path for reliable in silico calculations of additional optical properties. The total dataset of 8,488 unique compounds and a subset of 5,380 compounds with experimental and computational data, are available in MongoDB, CSV and JSON formats. These can be queried using Python, R, Java, and MATLAB, for data-driven optoelectronic materials discovery.

Measurement(s)	ultraviolet–visible spectrum • absorption wavelength • extinction coefficient
Technology Type(s)	digital curation

Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.10304897

ImageDataExtractor: A Tool To Extract and Quantify Data from Microscopy Images

The rise of data science is leading to new paradigms in data-driven materials discovery. This carries an essential notion that large data sources containing chemical structure and property information can be mined in a fashion that detects and exploits structure-property relationships, such that chemicals can be predicted to suit a given material application. The success of material predictions is predicated on these large data sources of chemical structure and property information being suited to a target application. Microscopy is commonly used to characterize chemical structure, especially in fields such as nanotechnology where material properties are highly dependent on the size and shape of nanoparticles. Large data sources of nanoparticle information stemming from microscopy images would thus be highly beneficial. Millions of microscopy images exist, but they lie fragmented across the literature, typically presented individually within a paper article and usually in a qualitative fashion therein, even though they harbor a wealth of numeric information. We present the ImageDataExtractor toolkit that autoidentifies and autoextracts microscopy images from scientific documents, whereupon it autonomously analyzes each image to produce quantitative particle size and shape information about its subject material. Each image is quantified by decoding its scale bar information using optical character recognition, with help from super-resolution convolutional neural networks where required. Individual particles are detected and profiled using various thresholding, segmentation, polygon fitting, and edge correction routines. The high-throughput operational capability of ImageDataExtractor means that it can be used to generate large-data sources of particle information for data-driven materials discovery. Evaluation metrics, precision and recall, are greater than 80% for the majority of the image processing steps, and precision is above 80% for all critical steps. The ImageDataExtractor tool is released under the MIT license and is available to download from http://www.imagedataextractor.org.

Laser-wakefield accelerators for high-resolution X-ray imaging of complex microstructures

Laser-wakefield accelerators (LWFAs) are high acceleration-gradient plasma-based particle accelerators capable of producing ultra-relativistic electron beams. Within the strong focusing fields of the wakefield, accelerated electrons undergo betatron oscillations, emitting a bright pulse of X-rays with a micrometer-scale source size that may be used for imaging applications. Non-destructive X-ray phase contrast imaging and tomography of heterogeneous materials can provide insight into their processing, structure, and performance. To demonstrate the imaging capability of X-rays from an LWFA we have examined an irregular eutectic in the aluminum-silicon (Al-Si) system. The lamellar spacing of the Al-Si eutectic microstructure is on the order of a few micrometers, thus requiring high spatial resolution. We present comparisons between the sharpness and spatial resolution in phase contrast images of this eutectic alloy obtained via X-ray phase contrast imaging at the Swiss Light Source (SLS) synchrotron and X-ray projection microscopy via an LWFA source. An upper bound on the resolving power of 2.7 ± 0.3 μm of the LWFA source in this experiment was measured. These results indicate that betatron X-rays from laser wakefield acceleration can provide an alternative to conventional synchrotron sources for high resolution imaging of eutectics and, more broadly, complex microstructures.

A spectrometer for ultrashort gamma-ray pulses with photon energies greater than 10 MeV

We present a design for a pixelated scintillator based gamma-ray spectrometer for non-linear inverse Compton scattering experiments. By colliding a laser wakefield accelerated electron beam with a tightly focused, intense laser pulse, gamma-ray photons up to 100 MeV energies and with few femtosecond duration may be produced. To measure the energy spectrum and angular distribution, a 33 × 47 array of cesium-iodide crystals was oriented such that the 47 crystal length axis was parallel to the gamma-ray beam and the 33 crystal length axis was oriented in the vertical direction. Using an iterative deconvolution method similar to the YOGI code, modeling of the scintillator response using GEANT4 and fitting to a quantum Monte Carlo calculated photon spectrum, we are able to extract the gamma ray spectra generated by the inverse Compton interaction.

Experimental Observation of a Current-Driven Instability in a Neutral Electron-Positron Beam

We report on the first experimental observation of a current-driven instability developing in a quasineutral matter-antimatter beam. Strong magnetic fields (≥1  T) are measured, via means of a proton radiography technique, after the propagation of a neutral electron-positron beam through a background electron-ion plasma. The experimentally determined equipartition parameter of ε_{B}≈10^{-3} is typical of values inferred from models of astrophysical gamma-ray bursts, in which the relativistic flows are also expected to be pair dominated. The data, supported by particle-in-cell simulations and simple analytical estimates, indicate that these magnetic fields persist in the background plasma for thousands of inverse plasma frequencies. The existence of such long-lived magnetic fields can be related to analog astrophysical systems, such as those prevalent in lepton-dominated jets.

DyeTiO2 interfacial structure of dye-sensitised solar cell working electrodes buried under a solution of I-/I3- redox electrolyte

Dye-sensitised solar cells (DSCs) have niche prospects for electricity-generating windows that could equip buildings for energy-sustainable future cities. However, this 'smart window' technology is being held back by a lack of understanding in how the dye interacts with its device environment at the molecular level. A better appreciation of the dyeTiO₂ interfacial structure of the DSC working electrodes would be particularly valuable since associated structure-function relationships could be established; these rules would provide a 'toolkit' for the molecular engineering of more suitable DSC dyes via rational design. Previous materials characterisation efforts have been limited to determining this interfacial structure within an environment exposed to air or situated in a solvent medium. This study is the first to reveal the structure of this buried interface within the functional device environment, and represents the first application of in situ neutron reflectometry to DSC research. By incorporating the electrolyte into the structural model of this buried interface, we reveal how lithium cations from the electrolyte constituents influence the dyeTiO₂ binding configuration of an organic sensitiser, MK-44, via Li⁺ complexation to the cyanoacrylate group. This dye is the molecular congener of the high-performance MK-2 DSC dye, whose hexa-alkyl chains appear to stabilise it from Li⁺ complexation. Our in situ neutron reflectometry findings are built up from auxiliary structural models derived from ex situ X-ray reflectometry and corroborated via density functional theory and UV/vis absorption spectroscopy. Significant differences between the in situ and ex situ dyeTiO₂ interfacial structures are found, highlighting the need to characterise the molecular structure of DSC working electrodes while in a fully assembled device.

Discovery of S···C≡N Intramolecular Bonding in a Thiophenylcyanoacrylate-Based Dye: Realizing Charge Transfer Pathways and Dye···TiO2 Anchoring Characteristics for Dye-Sensitized Solar Cells

Donor-π-acceptor dyes containing thiophenyl π-conjugated units and cyanoacrylate acceptor groups are among the best-performing organic chromophores used in dye-sensitized solar cell (DSC) applications. Yet, the molecular origins of their high photovoltaic output have remained unclear until now. This synchrotron-based X-ray diffraction study elucidates these origins for the high-performance thiophenylcyanoacrylate-based dye MK-2 (7.7% DSC device efficiency) and its molecular building block, MK-44. The crystal structures of MK-2 and MK-44 are both determined, while a high-resolution charge-density mapping of the smaller molecule was also possible, enabling the nature of its bonding to be detailed. A strong S···C≡N intramolecular interaction is discovered, which bears a bond critical point, thus proving that this interaction should be formally classified as a chemical bond. A topological analysis of the π-conjugated portion of MK-44 shows that this S···C≡N bonding underpins the highly efficient intramolecular charge transfer (ICT) in thiophenylcyanoacrylate dyes. This manifests as two bipartite ICT pathways bearing carboxylate and nitrile end points. In turn, these pathways dictate a preferred COO/CN anchoring mode for the dye as it adsorbs onto TiO₂ surfaces, to form the dye···TiO₂ interface that constitutes the DSC working electrode. These results corroborate a recent proposal that all cyanoacrylate groups anchor onto TiO₂ in this COO/CN binding configuration. Conformational analysis of the MK-44 and MK-2 crystal structures reveals that this S···C≡N bonding will persist in MK-2. Accordingly, this newly discovered bond affords a rational explanation for the attractive photovoltaic properties of MK-2. More generally, this study provides the first unequivocal evidence for an S···C≡N interaction, confirming previous speculative assignments of such interactions in other compounds.