Similarity of Precursors in Solid-State Synthesis as Text-Mined from Scientific Literature

Abstract

Collecting and analyzing the vast amount of information available in the solid-state chemistry literature may accelerate our understanding of materials synthesis. However, one major problem is the difficulty of identifying which materials from a synthesis paragraph are precursors or are target materials. In this study, we developed a two-step Chemical Named Entity Recognition (CNER) model to identify precursors and targets, based on information from the context around material entities. Using the extracted data, we conducted a meta-analysis to study the similarities and differences between precursors in the context of solid-state synthesis. To quantify precursor similarity, we built a substitution model to calculate the viability of substituting one precursor with another while retaining the target. From a hierarchical clustering of the precursors, we demonstrate that "chemical similarity" of precursors can be extracted from text data. Quantifying the similarity of precursors helps provide a foundation for suggesting candidate reactants in a predictive synthesis model.

Opportunities and challenges of text mining in materials research

“…This significantly modifies the meaning of the tokens and usually results in lowered accuracy of the named entity recognition (see below). Currently, this problem is solved case-by-case by creating task-specific wrappers for existing tokenizers and named entity recognition models ( Huang and Ling, 2019 ; Alperin et al., 2016 ; He et al., 2020 ). Building a robust approach for chemistry-specific sentence tokenization and data extraction requires a thorough development of standard nomenclature for complex chemical terms and materials names.…”

“…The early applications of chemical NER were mainly focused on extraction of drugs and biochemical information to perform more effective document searches ( Corbett and Copestake, 2008 ; Jessop et al., 2011 ; Rocktäschel et al., 2012 ; García-Remesal et al, 2013 ). Recently, chemical NER has shifted toward (in)organic materials and their characteristics ( Swain and Cole, 2016 ; He et al., 2020 ; Weston et al., 2019 ; Shah et al., 2018 ), polymers ( Tchoua et al., 2019 ), nanoparticles ( Hiszpanski et al., 2020 ), synthesis actions and conditions ( Vaucher et al., 2020 ; Hawizy et al., 2011 ; Kim et al., 2017c ; Kononova et al., 2019 ). The methods used for NER vary from traditional rule-based and dictionary look-up approaches to modern methodology built around advanced ML and NLP techniques, including conditional random field (CRF) ( Lafferty et al., 2001 ), long short-term memory (LSTM) neural networks ( Hochreiter and Schmidhuber, 1997 ), and others.…”

“…ChemDataExtractor ( Swain and Cole, 2016 ), ChemSpot ( Rocktäschel et al., 2012 ), tmChem ( Leaman et al., 2015 )) to assign labels to chemical terms. Some recent works implemented advanced ML models such as bidirectional LSTM models ( He et al., 2020 ; Weston et al., 2019 ; Kuniyoshi et al, 2020 ) as well as a combination of deep convolutional and recurrent neural networks ( Korvigo et al., 2018 ) to identify chemical and material terms in the text and use context information to assign their roles. Table 3 shows a few examples of the NER output obtained using some of these tools and compares it to non-scientific NER models implemented in NLTK ( Bird et al., 2009 ) and SpaCy ( Honnibal and Johnson, 2015 ) libraries.…”

“… Examples of the chemical named entities extracted by the general-purpose NER tools NLTK ( Bird et al., 2009 ) and SpaCy ( Honnibal and Johnson, 2015 ), and the tools trained on chemical corpus OSCAR4 ( Jessop et al., 2011 ), tmChem ( Leaman et al., 2015 ), ChemSpot ( Rocktäschel et al., 2012 ), ChemDataExtractor ( Swain and Cole, 2016 ), BiLSTM chemical NER ( He et al., 2020 ). For the general-purpose tools, the assigned labels are given in parenthesis.…”

“…(2019) used the random forest decision model to resolve synonyms between chemical entities and materials-related terms. He et al. (2020) applied a two-step LSTM model to resolve the role of materials in a synthesis procedure.…”