Automating Materials Exploration with a Semantic Knowledge Graph for Li‐Ion Battery Cathodes

Abstract

The recent marriage of materials science and artificial intelligence has created the need to extract and collate materials information from the tremendous backlog of academic publications. However, this is notoriously hard to achieve in sophisticated application domains, such as Li-ion battery (LIB) cathodes, which require multiple variables for materials selection, making it challenging to automatically identify the critical terms in the text. Herein, a semantics representation framework, featuring a dual-attention module that refines word embeddings through multi-source information fusion, is proposed for literature mining of LIB cathodes. The word embeddings thus produced are biased toward domain-specific knowledge and can enable the detection of deep-seated associations among materials for targeted applications. Based on this framework, we establish a semantic knowledge graph dedicated to LIB cathodes, which allows us to unravel the latent materials relationships from scientific literature and even to discover candidate materials not yet exploited as cathodes before. This work provides a long-sought path to the realization of text-mining-based knowledge management for complicated materials systems with little dependence on domain expertise.

Crystal Structure Assignment for Unknown Compounds from X-ray Diffraction Patterns with Deep Learning

“…Additionally, with the present model serving as the structure-type recommendation platform, a combination with the Rietveld refinement method, as well as the deep-learning algorithms in previous studies on precise phase identification, , is therefore advocated to offer more robust XRD interpretation, especially in the analysis of multiphase mixtures. Drawing inspiration from the recent development of multimodal learning, , another intriguing future direction for autonomous XRD interpretation could be to integrate domain knowledge from multiple information sources including textual, − image, , and other types of data into the deep-learning model.…”

The Materials Experiment Knowledge Graph

“…35 Knowledge graphs for specific d omains o f materials science have been established for common industrial metals, 36 , nanocomposites, 37 metal organic frameworks, 38 and battery materials. 39,40 The value proposition for expanding the purview of such knowledge graphs has been made, 41 and the present work builds towards a global materials knowledge graph by establishing best practices for representing experiments and their associated (meta)data in a scalable manner. With the proliferation of graph neural networks, causal modeling, and attention based networks such as transformer models in machine learning writ large, and the expectation that increased deployment for materials dis- The extract, transform, load (ETL) process was carried out using a python library called DBgen (https://github.com/modelyst/dbgen), 12 which was specifically designed to instantiate complicated, scientific data pipelines.…”

Revisiting Electrocatalyst Design by a Knowledge Graph of Cu-Based Catalysts for CO2 Reduction

“…In a knowledge graph, discrete information comprising entities and relationships is represented in a graph-structured knowledge database, which converts the textual information into scientific knowledge. Natural language processing (NLP) techniques can realize the extraction and analysis of textual information from the scientific literature while preserving their syntactic and semantic features, which has shown the potential in biological and thermoelectric research very recently. − To the best of our knowledge, however, it has not yet been applied to the field of electrocatalysts. Catalytic science is a complicated system, in which the activity of catalysts is determined by multiple properties, and the NLP technique cannot be directly used in its archetypal pattern to extract and analyze critical terms from unstructured and heterogeneous scientific documents.…”