Computational synthesis planning approaches have achieved recent success in organic chemistry, where tabulated synthesis procedures are readily available for supervised learning. The syntheses of inorganic materials, however, exist primarily as natural language narratives contained within scientific journal articles. This synthesis information must first be extracted from the text in order to enable analogous synthesis planning methods for inorganic materials. In this work, we present a system for automatically extracting structured representations of synthesis procedures from the texts of materials science journal articles that describe explicit, experimental syntheses of inorganic compounds. We define the structured representation as a set of linked events made up of extracted scientific entities and evaluate two unsupervised approaches for extracting these structures on expert-annotated articles: a strong heuristic baseline and a generative model of procedural text. We also evaluate a variety of supervised models for extracting scientific entities. Our results provide insight into the nature of the data and directions for further work in this exciting new area of research.
While significant strides have been made in computational synthesis planning via the prediction of synthetic action graphs in organic chemistry, these approaches have relied significantly on datasets comprised of historical chemical reactions. Despite efforts to standardize the reporting of chemical and materials science data @cite_3 , materials synthesis routes continue to reside in non-standard form. Several past studies have applied a variety of text extraction techniques to materials science literature using regular expressions, lexicon matches, or human-driven data labelling to extract synthesis information @cite_18 @cite_0 @cite_11 @cite_15 @cite_9 , but such approaches are inherently difficult to scale across sub-domains of materials science since new rules and lexicons must be created by domain experts for different types of materials or synthesis techniques. Accordingly, have previously developed automatic methods for extracting aspects of inorganic synthesis routes from natural language text to step in the direction of building comprehensive databases of the syntheses of inorganic materials @cite_26 @cite_27 .
@cite_18 @cite_26 @cite_9 @cite_3 @cite_0 @cite_27 @cite_15 @cite_11
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Background The primary method for scientific communication is in the form of published scientific articles and theses which use natural language combined with domain-specific terminology. As such, they contain free owing unstructured text. Given the usefulness of data extraction from unstructured literature, we aim to show how this can be achieved for the discipline of chemistry. The highly formulaic style of writing most chemists adopt make their contributions well suited to high-throughput Natural Language Processing (NLP) approaches. In this work we present a data-driven approach to the rational design of battery materials based on both resource and performance considerations. A large database of Li-ion battery material has been created by abstracting information from over 200 publications. The database consists of over 16 000 data points from various classes of materials. In addition to reference information, key parameters and variables determining the performance of batteries were collected. This work also includes resource considerations such as crustal abundance and the Herfindahl–Hirschman index, a commonly used measure of market concentration. The data is organized into a free web-based resource where battery researchers can employ a unique visualization method to plot database parameters against one another. This contribution is concerned with cathode and anode electrode materials. Cathode materials are mostly based on an intercalation mechanism, while anode materials are primarily based on conversion and alloying. Results indicate that cathode materials follow a common trend consistent with their crystal structure. On the other hand anode materials display similar behavior, based on elemental composition. Of particular interest is that high energy cathodes are scarcer than high power materials and high performance anode materials are less available. More sustainable materials for both electrodes based on alternative compositions are identified. Chemical markup language (CML) is an application of XML, the extensible markup language, developed for containing chemical information components within documents. Its design supports interoperability with the XML family of tools and protocols. It provides a base functionality for atomic, molecular, and crystallographic information and allows extensibility for other chemical applications. Legacy files can be imported into CML without information loss and can carry any desired chemical ontology. Some applications of CML (Markush structures, chemical searching) will be discussed in later articles. An XML document type declaration (DTD) for CML is included as a Chart. In this study, we demonstrate the use of natural language processing methods to extract, from nanomedicine literature, numeric values of biomedical property terms of poly(amidoamine) dendrimers. We have developed a method for extracting these values for properties taken from the NanoParticle Ontology, using the General Architecture for Text Engineering and a Nearly-New Information Extraction System. We also created a method for associating the identified numeric values with their corresponding dendrimer properties, called NanoSifter. We demonstrate that our system can correctly extract numeric values of dendrimer properties reported in the cancer treatment literature with high recall, precision, and f-measure. The micro-averaged recall was 0.99, precision was 0.84, and f-measure was 0.91. Similarly, the macro-averaged recall was 0.99, precision was 0.87, and f-measure was 0.92. To our knowledge, these results are the first application of text mining to extract and associate dendrimer property terms and their corresponding numeric values. Universal schema builds a knowledge base (KB) of entities and relations by jointly embedding all relation types from input KBs as well as textual patterns expressing relations from raw text. In most previous applications of universal schema, each textual pattern is represented as a single embedding, preventing generalization to unseen patterns. Recent work employs a neural network to capture patterns' compositional semantics, providing generalization to all possible input text. In response, this paper introduces significant further improvements to the coverage and flexibility of universal schema relation extraction: predictions for entities unseen in training and multilingual transfer learning to domains with no annotation. We evaluate our model through extensive experiments on the English and Spanish TAC KBP benchmark, outperforming the top system from TAC 2013 slot-filling using no handwritten patterns or additional annotation. We also consider a multilingual setting in which English training data entities overlap with the seed KB, but Spanish text does not. Despite having no annotation for Spanish data, we train an accurate predictor, with additional improvements obtained by tying word embeddings across languages. Furthermore, we find that multilingual training improves English relation extraction accuracy. Our approach is thus suited to broad-coverage automated knowledge base construction in a variety of languages and domains. The pursuit of more advanced electronics, and finding solutions to energy needs often hinges upon the discovery and optimization of new functional materials. However, the discovery rate of these materials is alarmingly low. Much of the information that could drive this rate higher is scattered across tens of thousands of papers in the extant literature published over several decades but is not in an indexed form, and cannot be used in entirety without substantial effort. Many of these limitations can be circumvented if the experimentalist has access to systematized collections of prior experimental procedures and results. Here, we investigate the property-processing relationship during growth of oxide films by pulsed laser deposition. To do so, we develop an enabling software tool to (1) mine the literature of relevant papers for synthesis parameters and functional properties of previously studied materials, (2) enhance the accuracy of this mining through crowd sourcing approaches, (3) create a searchable...