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.
The rise of comprehensive materials property and reaction databases has accelerated the development of chemical synthesis planning through the use of first-principles and machine-learning computational techniques @cite_10 @cite_12 @cite_28 @cite_21 @cite_17 @cite_15 . Using chemical reaction records retrieved from a database, showed that it is possible to construct a universal'' graph of chemistry such that molecules and reactions correspond to vertices and edges, respectively @cite_5 : Synthesis action graphs are computed by resolving unitary chemical reactions into action vertices with input and output molecules denoted by directed graph edges. Traversals on this universal chemical reaction graph allow for the optimization of pathways (e.g., for minimizing economic cost) @cite_5 , but methods for predicting novel, highly-structured synthesis pathways have remained elusive until very recently. The work by and investigates two complementary problems by learning on historical chemical reaction databases @cite_4 @cite_22 . Using a neural-network-driven candidate ranking approach, produce a model for predicting organic reaction outcomes @cite_4 . Conversely, approach the opposite problem, using Monte Carlo tree search to predict a synthesis pathway for a given output molecule. Impressively, the results attained by are shown to perform at a level comparable to human-driven organic molecule synthesis planning @cite_22 .
@cite_4 @cite_22 @cite_28 @cite_21 @cite_5 @cite_15 @cite_10 @cite_12 @cite_17
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Computer assistance in synthesis design has existed for over 40 years, yet retrosynthesis planning software has struggled to achieve widespread adoption. One critical challenge in developing high-quality pathway suggestions is that proposed reaction steps often fail when attempted in the laboratory, despite initially seeming viable. The true measure of success for any synthesis program is whether the predicted outcome matches what is observed experimentally. We report a model framework for anticipating reaction outcomes that combines the traditional use of reaction templates with the flexibility in pattern recognition afforded by neural networks. Using 15 000 experimental reaction records from granted United States patents, a model is trained to select the major (recorded) product by ranking a self-generated list of candidates where one candidate is known to be the major product. Candidate reactions are represented using a unique edit-based representation that emphasizes the fundamental transformation fro... Scripts represent knowledge of stereotypical event sequences that can aid text understanding. Initial statistical methods have been developed to learn probabilistic scripts from raw text corpora; however, they utilize a very impoverished representation of events, consisting of a verb and one dependent argument. We present a script learning approach that employs events with multiple arguments. Unlike previous work, we model the interactions between multiple entities in a script. Experiments on a large corpus using the task of inferring held-out events (the “narrative cloze evaluation”) demonstrate that modeling multi-argument events improves predictive accuracy. Accelerating the discovery of advanced materials is essential for human welfare and sustainable, clean energy. In this paper, we introduce the Materials Project (www.materialsproject.org), a core program of the Materials Genome Initiative that uses high-throughput computing to uncover the properties of all known inorganic materials. This open dataset can be accessed through multiple channels for both interactive exploration and data mining. The Materials Project also seeks to create open-source platforms for developing robust, sophisticated materials analyses. Future efforts will enable users to perform ‘‘rapid-prototyping’’ of new materials in silico, and provide researchers with new avenues for cost-effective, data-driven materials design. © 2013 Author(s). All article content, except where otherwise noted, is licensed under a Creative Commons Attribution 3.0 Unported License. Researchers in the USA and Germany introduce a database of over 300,000 calculations detailing the electronic structure and stability of inorganic materials. Chris Wolverton and co-workers from Northwestern University and the Leibniz Institute for Information Infrastructure describe the structure of the Open Quantum Materials Database—a catalog storing information about the electronic properties of a significant fraction of the known crystalline solids determined using density functional theory calculations. Density functional theory is a powerful computational technique that uses quantum mechanics to determine the lowest energy state of the electrons travelling through a lattice of atoms. The researchers verified the accuracy of the calculations by comparing them to experimental results on 1,670 crystals. The database is freely available to scientists, enabling them to design and predict the properties of as yet unrealised materials. The vast number of known organic compounds and the reactions that connect them together can be thought of as a complex network. Analysing the organic chemistry universe in this manner may prove useful for both fundamental and practical purposes, such as predicting chemical reactivity or improving how regulated substances are monitored. 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. This Perspective introduces the Harvard Clean Energy Project (CEP), a theory-driven search for the next generation of organic solar cell materials. We give a broad overview of its setup and infrastructure, present first results, and outline upcoming developments. CEP has established an automated, high-throughput, in silico framework to study potential candidate structures for organic photovoltaics. The current project phase is concerned with the characterization of millions of molecular motifs using first-principles quantum chemistry. The scale of this study requires a correspondingly large computational resource, which is provided by distributed volunteer computing on IBM’s World Community Grid. The results are compiled and analyzed in a reference database and will be made available for public use. In addition to finding specific candidates with certain properties, it is the goal of CEP to illuminate and understand the structure–property relations in the domain of organic electronics. Such insights can o...