Yea-Jin Shon,Kyoungmin Min
Extracting Chemical Information from Scientific Literature Using Text Mining: Building an Ionic Conductivity Database for Solid-State Electrolytes
Recently, as the demand for electric vehicles has rapidly grown, concerns regarding the safety of liquid electrolytes used as battery materials have increased. Rechargeable batteries made of liquid electrolytes pose a risk of fire and may explode due to the decomposition reaction of the electrolyte. Accordingly, interest in solid-state electrolytes (SSEs), which have greater stability than liquid electrolytes, is increasing, and research into finding stable SSEs with high ionic conductivity is actively being conducted. Consequently, it is essential to obtain a large amount of material data to explore new SSEs. However, the data collection process is highly repetitive and time-consuming. Therefore, the goal of this study is to automatically extract the ionic conductivities of SSEs from published literature using text-mining algorithms and use this information to construct a materials database. The extraction procedure includes document processing, natural language preprocessing, phase parsing, relation extraction, and data post-processing. For performance verification, the ionic conductivities are extracted from 38 studies, and the accuracy of the proposed model is confirmed by comparing extracted conductivities with the actual ones. In previous research, 93% of battery-related records were unable to distinguish between ionic and electrical conductivities. However, by applying the proposed model, the proportion of undistinguished records was successfully reduced from 93 to 24.3%. Finally, the ionic conductivity database was constructed by extracting the ionic conductivity from 3258 papers, and the battery database was reconstructed by adding eight pieces of representative structural information.
Extracting Chemical Information from Scientific Literature Using Text Mining: Building an Ionic Conductivity Database for Solid-State Electrolytes

Recently, as the demand for electric vehicles has rapidly grown, concerns regarding the safety of liquid electrolytes used as battery materials have increased. Rechargeable batteries made of liquid electrolytes pose a risk of fire and may explode due to the decomposition reaction of the electrolyte. Accordingly, interest in solid-state electrolytes (SSEs), which have greater stability than liquid electrolytes, is increasing, and research into finding stable SSEs with high ionic conductivity is actively being conducted. Consequently, it is essential to obtain a large amount of material data to explore new SSEs. However, the data collection process is highly repetitive and time-consuming. Therefore, the goal of this study is to automatically extract the ionic conductivities of SSEs from published literature using text-mining algorithms and use this information to construct a materials database. The extraction procedure includes document processing, natural language preprocessing, phase parsing, relation extraction, and data post-processing. For performance verification, the ionic conductivities are extracted from 38 studies, and the accuracy of the proposed model is confirmed by comparing extracted conductivities with the actual ones. In previous research, 93% of battery-related records were unable to distinguish between ionic and electrical conductivities. However, by applying the proposed model, the proportion of undistinguished records was successfully reduced from 93 to 24.3%. Finally, the ionic conductivity database was constructed by extracting the ionic conductivity from 3258 papers, and the battery database was reconstructed by adding eight pieces of representative structural information.

Electric vehicles are becoming more common in our daily lives as a result of their recent popularity. Simultaneously, there are increasing concerns about the safety of liquid electrolytes, a key material in electric vehicle batteries. In lithium-ion batteries that use conventional organic-based liquid electrolytes, a solid electrolyte interphase (SEI) is created between the electrolyte and the lithium metal during repeated charging and discharging processes. If the SEI is destroyed owing to repeated adsorption and dissolution on the surface of the lithium ions, dissolution occurs unevenly. This reduces the capacity of the battery and causes dendrites to form during charging and discharging, leading to a short circuit that may cause serious problems such as explosion, fire, and leakage. Accordingly, a solid electrolyte has been proposed as a new battery material that does not have such a risk. When a solid electrolyte is used, the battery is composed of only solid materials, so the manufacturing process is simple, the battery is light, and the mechanical properties of the battery are higher than the liquid-type; thus, there are advantages in structural stability compared to conventional lithium-ion batteries. In addition, because lithium metal can be used as an anode, the energy density increases and the formation of lithium dendrites is suppressed. However, solid electrolytes generally have lower ionic conductivities than liquid electrolytes, which makes them difficult to commercialize. In particular, if the ionic conductivity is high, a large number of ions can be moved in a short time, which has the advantage of enabling fast charging. Therefore, it is important to develop a solid electrolyte with an ionic conductivity equivalent to that of a liquid electrolyte. Recently, many studies have been conducted to design solid electrolytes with high ionic conductivity. In addition, several review papers provide more information to researchers by comparing ionic conductivity information. However, despite numerous published papers, databases containing information on candidate materials for solid electrolytes with various structures are still lacking, and collecting information about the many newly announced materials is difficult. A well-organized paper or materials database would make it possible to know the direction in which further research should be conducted, based on the comprehensive results of existing studies and the strengths and weaknesses of each material. Based on these databases, artificial intelligence (AI)-based regression or classification models could be built and used in research to explore new solid electrolytes. Therefore, this study proposes a method of collecting previous papers, which are unstructured data, on the Internet to quickly and accurately extract the ionic conductivity of candidate solid electrolyte materials. Unstructured data is the data that does not follow predefined rules and cannot be stored in the form of rows and columns as in a database. By establishing a database using this method, we want to help researchers find next-generation battery materials with high ionic conductivity by making it easier for researchers to comprehensively identify the results of newly published papers. We introduce a method of arranging hundreds of thousands of databases in a standardized form using a natural language processing technique that has been widely used recently. Research is actively being conducted on the extraction of required information using natural language processing techniques and its refinement into structured data; examples include a study that predicts the status and probabilities of interaction and builds databases by extracting information about human intergenic interactions from genetic papers and a study that classifies only records of prostate cancer patients from unstructured electronic medical records. There is also ongoing research into extracting chemical formulas, units, roles, and material properties from papers, journals, and articles using ChemDataExtractor, which specializes in natural language processing for material information processing. ChemDataExtractor is a text-mining tool that automatically extracts chemical information from scientific literature. It has the advantage of being able to design a model directly according to the user’s intention so that even complex interrelated properties can be extracted. Studies have been conducted to extract the band gap from semiconductor-related papers and to build a database by extracting thermoelectric figures of merit, Seebeck coefficients, thermal conductivities, electrical conductivities, and power factors from papers studying thermoelectric materials. In addition, a study was conducted to extract the battery capacity, conductivity, voltage, chemical characteristics, energy, and Faraday efficiency values from battery-related papers. However, because the method proposed in the existing battery database construction research has the limitation of being unable to distinguish between electrical and ionic conductivities, we developed a methodology that can extract only the ionic conductivity based on previous studies. Additionally, to verify that the proposed method extracts valid conductivities, chemical formulas and ionic conductivities were extracted from 38 papers cited in Bachman et al., which summarized the ionic conductivities of various materials and were used to confirm whether the ionic conductivities were extracted properly. Finally, we applied the code implementing natural language processing to the 3258 papers used in the battery database creation study. This establishes an ionic conductivity database and reconstructs the battery database by adding information on ionic conductivity and chemical structure.

Figure 1 shows a flowchart of the program proposed in this study for extracting the ionic conductivity. A detailed explanation is provided below.

First, we use the data crawling package and hypertext transfer protocol (HTTP) client library included in ChemDataExtractor to automatically collect battery-related papers. Clients can send the query to the server to search for the word ‘battery’ and receive articles in the extensible markup language (XML) and hypertext markup language (HTML) formats that mention batteries. Markup languages such as HTML and XML are used to express the data hierarchically through tags like <head>, <body>, <title>, and <meta>. Next, the reader module included in ChemDataExtractor is used to convert the collected papers into document objects used inside the program. In the document conversion process, tags like <head>, <body>, <title>, and <meta> are removed, and the document is re-expressed by assigning the tags to the citation, heading, title, and paragraph properties of the document object.

If unstructured data is converted into structured data through the preprocessing of natural language, a previously developed data-mining methodology can be applied. Natural language preprocessing goes through steps that include: (1) tokenization, (2) normalization, (3) part-of-speech tagging, (4) entity recognition, (5) phase parsing, and (6) relation extraction. (1) The tokenization step separates documents into tokens, which are the smallest units of expression that can accurately represent meaning. Using the Punkt model trained on abbreviations such as period, ‘fig’, and ‘ref’, the input text from 3592 documents published by the Royal Society of Chemistry (RSC), the American Chemical Society (ACS), and the Springer Society is split into sentences. The Punkt model divides sentences based on periods by treating them as single words, ignoring the periods included in abbreviations. Next, the text divided into sentence units is re-divided into token units. In this process, the Penn treebank tokenization rule is applied to divide sentences by considering meaning rather than simply cutting them based on punctuation marks or spaces. According to the Penn treebank tokenization rules, words connected with ‘-’ are treated as one token, and when an apostrophe and a clitic are used together to express an abbreviation, the word, the apostrophe, and the clitic are separated into separate tokens. (2) In the normalization step, tokens with the same meaning but with differing forms, depending on their use in sentences, are integrated into one token. For example, ‘separates’ and ‘separated’ are integrated into the token ‘separate.’ (3) In the part-of-speech tagging step, the parts of speech for each token, such as adjectives, nouns, and verbs, are specified. Because the same word may have different meanings depending on the part of speech it is used as, every word goes through the part-of-speech tagging process; the higher the accuracy of this step, the better the performance of later steps. For part-of-speech tagging, the conditional random field (CRF) algorithm is used. The feature function of the CRF performs classification using the current location, the classification results of surrounding words, and information from the entire sentence as input. Using this feature function, the part of speech of the token can be effectively specified by considering the surrounding context. (4) In the entity recognition step, the name of the chemical substance mentioned in the document is recognized. Three classifiers are used in combination: a CRF-based classifier to effectively recognize the name of a chemical substance, a dictionary-based classifier to improve the recognition performance of trade and trivial names, and a regular expression-based classifier to recognize database identifiers and chemical formulas. In a dictionary-based classifier, a dictionary is defined and used in the form of a directed acyclic graph, which can quickly remove duplicates by comparing similar names. (5) In the phase parsing step, syntax analysis is performed according to the parsing rules. The parts of speech, contents, and order of the tokens are defined in the parsing rules. Table 1 lists both the parse expression of the conductivity property defined in the existing battery database creation research and that of the ionic conductivity property defined in our proposed program. In the battery database generation program, the parsing rule is defined such that the ‘Conductivity’ or ‘Conductivities’ token must appear to extract ionic conductivity, and ‘Electronic’ or ‘Electrical’ modifiers appear selectively in front of it. In this study, to extract only ionic conductivity, the parsing rule is defined such that the ‘Conductivity’ or ‘Conductivities’ token must appear with the ‘Ionic’ or ‘Ion’ modifier before it. (6) In the relation extraction step, using ChemDataExtractor, the user can define a model suitable for the material properties and save the data in accordance with the defined model format. The model applies the parsing rule specified in Table 1 as an attribute to the phase parsing step to extract the relation.

The document is processed by both the natural language processing module and the module using the snowball algorithm, and the results are then integrated. Snowball is an algorithm that generates 5-tuples for each string and then clusters these 5-tuples using the match function computing the similarity between the vectors and some minimum similarity threshold to generate patterns and extract tuples from plain text documents. Unlike other methods that require predefined expert knowledge, the snowball algorithm automatically generates and extracts patterns from the plain text, so it can extract incomplete relationships that could not be handled by the natural language processing module.

Finally, it goes through the process of deleting inaccurate chemical names, values, units, and other properties included in the extraction results according to specific rules. Data is deleted when special symbols or abnormal words are included in chemical formulas, when attribute values do not belong to a valid range or when terms that are irrelevant to the domain appear. We set the valid range of ionic conductivity as 10–20 S/cm or more and 100 S/cm or less, which is wider than that shown in papers cited in Bachman et al. to search materials with various ionic conductivities.

We have verified that the natural language processing model developed in this study can properly extract the ionic conductivities from papers in two ways. First, ionic conductivities were extracted from 38 papers cited in the paper, which summarized the ionic conductivities of various solid electrolytes and compared them with the suggested values. The ionic conductivities of 10 materials were extracted by conducting experiments on 38 papers. Table 2 shows the chemical formulas and ionic conductivities extracted using our program and the ionic conductivities specified in previous papers. The mean absolute error (MAE) between the extracted ionic conductivity and the ionic conductivity specified in the paper was 5.63 × 10–3 S/cm, and several ionic conductivities of Li2NH and Li2CdCl4 were extracted; this is because the ionic conductivity according to temperature change was extracted from the paper. Except for the 10 extracted materials of the 38 materials, the remaining 28 materials were not extracted for the following reasons. First, because the chemical formula of a substance includes an unknown term, it is impossible to extract a specific value without additional substitution steps. For example, Li5La3M2O12 (M = Ta, Nb) cannot specify an element because of the unknown term M and Li7–3xGaxLa3Zr2O12 cannot specify a substance because of the unknown term x. Second, text-mining technology cannot be applied when ionic conductivity is visualized as a graph. Third, because it is difficult to obtain all papers in the HTML format, some papers in the portable document format are converted to the HTML format. However, most document conversion programs convert tables to be expressed using tags such as <div> and <span> rather than tags like <table>, <th>, <tr>, or <td>. This is because it provides a visually equivalent output even if the table is expressed using tags such as <div> and <span>. However, because of this conversion, the hierarchical and logical structure of the table is lost, and the model developed in this study cannot recognize the table. Next, we confirmed that the model proposed in this study could accurately extract only the ionic conductivity and build a database from the 223,877 papers used to construct the battery database in a previous study. A total of 292,313 records were extracted from 223,877 papers in the existing battery database, of which 7168 were related to conductivity. Figure 2 shows the specific properties of 7168 records. From Figure 2, we can see that unlike records where the specifier is ‘electrical conductivity,’ ‘Electronic conductivity,’ ‘electronic conductivity,’ ‘Electrical conductivity,’ ‘electrical conductivities,’ ‘electronic conductivities,’ or ‘Electrical conductivity,’ ionic and electrical conductivity could not be distinguished in records where the specifier is ‘None,’ ‘conductivity,’ ‘conductivities,’ ‘Conductivity,’ or ‘Conductivities.’ The specifier ‘none’ means that there is no specifier. These indistinguishable records totaled 6666, accounting for 93% of the conductivity records. After removing duplicates from the Digital Object Identifier (DOI) of 6666 records, it was confirmed that they were extracted from 3258 papers. This identification of DOI redundancy is performed because it helps prevent papers from being processed by the current program in duplicate. Therefore, in this study, ionic conductivity was extracted from these 3258 papers, and a database was built in the JavaScript Object Notation (JSON) format. Finally, the ionic conductivity was extracted from 1457 of the 3258 papers, and 4924 records were extracted. Figure 3 shows the composition of the 7168 conductivity-related records. Figure 3a shows the composition of the records before applying our program, and Figure 3b shows the composition of the records after applying our program. Through this, it can be confirmed that 68.7% of the records where ionic and electrical conductivities could not be distinguished were actually ionic conductivity. Thus, the ratio of unknown records was greatly reduced from 93 to 24.3%. In addition, among the 4924 records, the number of records and ionic conductivities for records belonging to eight types were visualized: LISCON-like, Argyrodite, Garnet, NASICON-like, Li-Nitride, Li-Hydride, Perovskite, and Li-Halide. The results are shown in Figures 4 and 5. Figure 4 shows that, among the extracted records, NASICON-like-type records are the most common (136 substances), followed by Garnet-type records with 90. When a solid-state battery is designed using NASICON-like- or Garnet-type materials, it has the advantage of high energy density; therefore, research on these types has been actively conducted. LISICON-like and perovskite types were the third and fourth most types, with 54 and 26 records, respectively. These are characterized by relatively lower ionic conductivity and lower electrochemical stability for the lithium metal anode than NASICON-like and Garnet-structure materials. We speculate that the reason for the low number of records corresponding to the Li-Halide type is that it has started to attract attention recently compared to other types. Next, it can be confirmed that the extracted ionic conductivities fell within the normal range because the ionic conductivity distribution shown in Figure 5 is similar to the distribution shown in the previous literature. In addition, the material with the highest ionic conductivity was the NASICON-like type; its ionic conductivity was 9.0 × 10–1 S/cm (Li1.3La3Zr2Ga0.15O12). The material with the lowest ionic conductivity was the perovskite type, whose ionic conductivity was 1.0 × 10–13 S/cm (Li0.34Nd0.55TiO3). The mean ionic conductivity for each of the eight types is as follows: 5.52 × 10–3 S/cm for the LISICON-like type, 2.01 × 10–3 S/cm for the Argyrodite type, 1.05 × 10–4 S/cm for the Garnet type, 3.51 × 10–2 S/cm for the NASICON-like type, 2.03 × 10–4 S/cm for the Li-Nitride type, 1.37 × 10–2 S/cm for the Li-Hydride type, 2.45 × 10–4 S/cm for the perovskite type, and 6.02 × 10–4 S/cm for the Li-Halide type. This data confirms that the average ionic conductivity of the NASICON-like type is the largest and the average ionic conductivity of the Garnet type is the smallest. The reasons for these results are as follows. The NASICON-like type is one of the most promising and extensively studied SSEs due to its high ionic conductivity and low thermal expansion. On the other hand, among the most notable garnet-type materials, a tetragonal phase (t-LLZO) of Li7La3Zr2O12 (LLZO) has a relatively low ionic conductivity value. In addition, the ionic conductivity of the NASICON-like type, which had the largest number of records, ranged most widely from 1.0 × 10–12 to 9.0 × 10–1 S/cm, while the ionic conductivity of the Li-Nitride type, which had the second lowest number of records, was the most narrowly distributed, from 1.0 × 10–6 to 1.0 × 10–3 S/cm. Finally, the battery database was reconstructed based on whether the ionic conductivity was extracted and the information on the eight structure types. The code developed in this study and the reconstructed battery database, including the ionic conductivities, are available at the following address: https://github.com/shonyeajin/Ionical_conductivity_database. The natural language processing-based model proposed in this study is capable of automatically extracting ionic conductivity data from a vast number of papers. This provides a quick and accurate summary of previous research results, enabling a comprehensive examination of the ionic conductivities of various materials. Additionally, it is possible to maintain an ionic conductivity database that reflects the latest research results by extracting meaningful information from newly published papers and updating the database. Because researchers can access the latest technology trends through this database, we expect that research to develop solid electrolytes with high ionic conductivities will be actively conducted. Furthermore, it is expected that promising materials can be predicted by developing an AI-based prediction model trained on our database.

Ionic conductivity is one of the most important material properties in the search for solid electrolytes and is attracting attention as a next-generation battery material for electric vehicles. However, it is not easy to find a well-organized database for the ionic conductivities of these materials, and it is time- and cost-intensive for researchers to manually build such a database. Therefore, we propose a method for automatically extracting the ionic conductivities from a vast number of previous papers on the Internet. The process of extracting the ionic conductivities of chemicals from paper consists of four steps: (1) document processing, (2) preprocessing and natural language processing, (3) snowball processing, and (4) data cleaning. ChemDataExtractor, a text-mining tool for chemical literature, was used for document processing and natural language processing, and the parsing rule was redefined to extract only the ionic conductivity from among the various characteristics. Next, the proper extraction of the ionic conductivity using the developed model was verified in two ways. First, the ionic conductivity was extracted from 38 papers cited in a paper that summarized the ionic conductivity of various materials; the extracted conductivities were compared with the conductivities in the papers to confirm that the model extracts valid conductivities. Second, it was confirmed that the model proposed in this study accurately extracted only the ionic conductivities for the 223,877 papers used to construct the existing battery database. The extraction result was processed into JSON format data. Furthermore, the battery database was reconstructed by adding information on ionic conductivity and eight structure types. It was subsequently disclosed online so that related researchers could utilize it. In the future, we plan to improve record extraction performance by defining parsing rules that reflect expertise in the field of materials science. In addition, we plan to add an optical character recognition (OCR) function to extract ionic conductivity from visualized data as well as text.