Search

Given the emergence of data science and machine learning throughout all aspects of society, but particularly in the scientific domain, there is increased importance placed on obtaining data. Data in materials science are particularly heterogeneous, based on the significant range in materials classes that are explored and the variety of materials properties that are of interest. This leads to data that range many orders of magnitude, and these data may manifest as numerical text or image-based information, which requires quantitative interpretation. The ability to automatically consume and codify the scientific literature across domains - enabled by techniques adapted from the field of natural language processing - therefore has immense potential to unlock and generate the rich datasets necessary for data science and machine learning. This review focuses on the progress and practices of natural language processing and text mining of materials science literature and highlights opportunities for extracting additional information beyond text contained in figures and tables in articles. We discuss and provide examples for several reasons for the pursuit of natural language processing for materials, including data compilation, hypothesis development, and understanding the trends within and across fields. Current and emerging natural language processing methods along with their applications to materials science are detailed. We, then, discuss natural language processing and data challenges within the materials science domain where future directions may prove valuable.

With the emergence of big data initiatives and the wealth of available chemical data, data-driven approaches are becoming a vital component of materials discovery pipelines or workflows. The screening of materials using machine-learning models, in particular, is increasingly gaining momentum to accelerate the discovery of new materials. However, the black-box treatment of machine-learning methods suffers from a lack of model interpretability, as feature relevance and interactions can be overlooked or disregarded. In addition, naive approaches to model training often lead to irrelevant features being used which necessitates the need for various regularization techniques to achieve model generalization; this incurs a high computational cost. We present a feature-selection workflow that overcomes this problem by leveraging a gradient boosting framework and statistical feature analyses to identify a subset of features, in a recursive manner, which maximizes their relevance to the target variable or classes. We subsequently obtain minimal feature redundancy through multicollinearity reduction by performing feature correlation and hierarchical cluster analyses. The features are further refined using a wrapper method, which follows a greedy search approach by evaluating all possible feature combinations against the evaluation criterion. A case study on elastic material-property prediction and a case study on the classification of materials by their metallicity are used to illustrate the use of our proposed workflow; although it is highly general, as demonstrated through our wider subsequent prediction of various material properties. Our Bayesian-optimized machine-learning models generated results, without the use of regularization techniques, which are comparable to the state-of-the-art that are reported in the scientific literature. [ABSTRACT FROM AUTHOR]

Electrical-circuit schematics are a foundational tool in electrical engineering. A method that can automatically digitalize them is desirable since a knowledge base of such schematics could preserve their functional information as well as provide a database that one can mine to predict more operationally efficient electrical circuits using data analytics and machine learning. We present a workflow that contains a novel pattern-recognition methodology and a custom-trained Optical Character Recognition (OCR) model that can digitalize images of electrical-circuit schematics with minimal configuration. The pattern-recognition and OCR stages of the workflow yield 86.4% and 99.6% success rates, respectively. We also present an extendable option toward predictive circuit-design efficiencies, subject to a large database of images being available. Thereby, data gathered from our pattern-recognition workflow are used to draw network graphs, which are in turn employed to form matrix equations that contain the voltages and currents for all nodes in the circuit in terms of component values. These equations could be applied to a database of electrical-circuit schematics to predict new circuit designs or circuit modifications that offer greater operational efficiency. Alternatively, these network graphs could be converted into simulation programs with integrated circuit emphasis netlists to afford more accurate and computationally automated simulations. [ABSTRACT FROM AUTHOR]

A measure of chemical similarity is only useful if it implies similarity in some relevant property space. Typically, similarity calculations operate by assigning each molecule a chemical fingerprint: a fixed-length vector of bits where the on-bits signify the presence of a certain feature. Common fingerprinting schemes, such as extended-connectivity fingerprints, are by definition general and fail to capture much of the domain-specific theory that underpins similarity in a specific domain. In this work, a hierarchical fingerprinting scheme is developed that is bespoke to a database of ∼4500 organic molecules and their cognate optical absorption spectral properties. Our fingerprinting scheme incorporates molecular fragmentation and domain-specific chemical intuition into an algorithm that categorizes each fragment as being one of a core chemical group, a substituent, or a bridge. The algorithm is applied to every molecule in the database to generate a pool of chemically relevant fragments that are labeled according to their structural category. The fingerprint of each molecule is then composed of a nested Python dictionary specifying the unique identifiers of its constituent fragment entities and the structural links between them to give a hierarchical molecular encoding scheme. Four case studies show the application of our fingerprinting scheme to the subject database. In each case, the clustered molecules display a host of interesting chemical trends. The application that was used to develop and implement this bespoke fingerprinting scheme, referred to as ChemCluster, also exposes a host of other cheminformatics tools pertaining to this database, a selection of which is demonstrated in this work. The enhanced similarity comparisons afforded by our fingerprinting scheme, as well as the large repository of categorized fragments generated during its development, constitute the first step toward using this database in a data-driven materials discovery workflow. [ABSTRACT FROM AUTHOR]

A family of coordination complexes of the type [Ru(SO2)(NH3)4X]m+Yn− (m, n = 1 or 2) exhibit optical switching capabilities in their single-crystal states. This striking effect is caused by the light-induced formation of SO2-linkage photoisomers, which are metastable if kept at suitably cool temperatures. We modeled the dark- and light-induced states of these large crystalline complexes via plane-wave (PW)- and molecular-orbital (MO)-based density functional theory (DFT) and time-dependent DFT in order to calculate their structural and optical properties; the calculated results are compared with experimental data. We show that the PW-DFT-based periodic models replicate the structural properties of these complexes more effectively than the MO-DFT-based molecular-fragment models, observing only small deviations in key bond lengths relative to the experimentally derived crystal structures. The periodic models were also found to more effectively simulate trends seen in experimental optical absorption spectra, with optical absorbance and coverage of the visible region increasing with the formation of the photoinduced geometries. The contribution of the metastable photoisomeric species more heavily focuses on the lower-energy end of the spectra. Spectra generated from the molecular-fragment models are limited by the geometry of the fragment used and the number of excited-state roots considered in those calculations. In general, periodic models outperform the molecular-fragment models owing to their ability to better appreciate the periodic phenomena that are present in these crystalline materials as opposed to MO approaches, which are finite methods. We thus demonstrate that PW-DFT-based periodic models should be considered as a more than viable method for simulating the optical and electronic properties of these single-crystal optical switches. [ABSTRACT FROM AUTHOR]

Endeavors in the field of dye‐sensitized solar cells (DSCs) have shown great promise when adopting a data‐driven approach to materials discovery, such as successful molecular‐scale predictions of light‐harvesting chromophores. However, predictions of DSC dyes would become much more sophisticated if a molecular‐to‐macroscopic DSC device prediction methodology existed. Thereby, a fully computational pipeline is presented that predicts device‐performance parameters of DSCs which contain varying dye combinations. Optimal pairing of complementary dyes is identified via a data‐driven workflow that affords cosensitized DSCs with maximum power‐conversion efficiencies. Six high‐performing DSC dyes are paired with partner dyes that are screened from a database of 8488 compounds using sequential heuristic filters. Existing models that predict short‐circuit‐current density (JSC) and open‐circuit voltage (VOC) parameters are adapted to predict singly sensitized and cosensitized DSC performance. The predictions for Jsc values of singly sensitized devices match experimental literature values with comparable accuracy to more computationally costly methods. Five out of six dye pairings are predicted to have greater JSC values when cosensitized compared to their corresponding singly sensitized devices, including two pairs that show strong Jsc boosts of +13% and +12% when cosensitized. Thus, the prospect of an entirely in‐silico prediction pipeline for DSC performance that can be used to realize the fully automated design of optimized cosensitized DSCs is demonstrated. [ABSTRACT FROM AUTHOR]