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RESEARCH PAPER · Journal of Chemical Information and Modeling · 2020

Inorganic Materials Synthesis Planning with Literature-Trained Neural Networks

Edward Kim, Zach Jensen, Alexander van Grootel, Kejun Huang, Matthew Staib, Sheshera Mysore, Haw-Shiuan Chang, Emma Strubell, Andrew McCallum, Stefanie Jegelka, Elsa Olivetti

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Inorganic Materials Synthesis Planning with Literature-Trained Neural NetworksOriginalOn the Limits of Learning to Actively Learn Semantic Representations8The Materials Science Procedural Text Corpus: Annotating Materials Synthesis Procedures with Shallow Semantic Structures9A Machine Learning Approach to Zeolite Synthesis Enabled by Automatic Literature Data Extraction282The materials science procedural text corpus: Annotating materials synthesis procedures with shallow semantic structures88Autonome Entdeckung in den chemischen Wissenschaften, Teil I: Fortschritt12Autonomous Discovery in the Chemical Sciences Part I: Progress317Representing Multiword Chemical Terms through Phrase-Level Preprocessing and Word Embedding13Компьютерные технологии против коронавируса: первые результаты6Debiased Contrastive Learning15Automated extraction of chemical synthesis actions from experimental procedures202A universal system for digitization and automatic execution of the chemical synthesis literature270Emerging materials intelligence ecosystems propelled by machine learning441Data-driven materials research enabled by natural language processing and information extraction302Can we predict materials that can be synthesised?73Artificial intelligence and machine learning in design of mechanical materials690Strategies for search of pharmacological drugs against SARS-CoV-2 on the base of studying the structural-genetic features of coronaviruses SARS-CoV, MERS-CoV and SARS-CoV-22Analyzing Research Trends in Inorganic Materials Literature Using NLP8Deep Learning Models for Intelligent Healthcare: Implementation and Challenges15MS-Mentions: Consistently Annotating Entity Mentions in Materials Science Procedural Text13The Impact of Domain-Specific Pre-Training on Named Entity Recognition Tasks in Materials Science33Predicting the Outcomes of Material Syntheses with Deep Learning13Machine learning in polymer informatics200Artificial intelligence and machine learning in glass science and technology: 21 challenges for the 21 st century55What Can Text Mining Tell Us About Lithium‐Ion Battery Researchers’ Habits?34Opportunities and challenges of text mining in materials research180Theoretical Prediction of Two-Dimensional Materials, Behavior, and Properties66Discovering Relationships between OSDAs and Zeolites through Data Mining and Generative Neural Networks105Prediction of solar‐chargeable battery materials: A text‐mining and first‐principles investigation17Device Fabrication Knowledge Extraction from Materials Science Literature3Looking through glass: Knowledge discovery from materials science literature using natural language processing60An automated domain-independent text reading, interpreting and extracting approach for reviewing the scientific literature0Genetic Algorithms and Machine Learning for Predicting Surface Composition, Structure, and Chemistry: A Historical Perspective and Assessment22Applied Machine Learning for Developing Next‐Generation Functional Materials72Toward Predictive Chemical Deformulation Enabled by Deep Generative Neural Networks7Computer-aided retrosynthetic design: fundamentals, tools, and outlook37Challenges and Advances in Information Extraction from Scientific Literature: a Review67Using Machine Learning and Data Mining to Leverage Community Knowledge for the Engineering of Stable Metal–Organic Frameworks198Machine-Guided Polymer Knowledge Extraction Using Natural Language Processing: The Example of Named Entity Normalization28Dataset of Solution-based Inorganic Materials Synthesis Recipes Extracted from the Scientific Literature2Machines for Materials and Materials for Machines: Metal-Insulator Transitions and Artificial Intelligence6Audacity of huge: overcoming challenges of data scarcity and data quality for machine learning in computational materials discovery98Towards Predictive Synthesis of Inorganic Materials Using Network Science5Autonomous retrosynthesis of gold nanoparticles via spectral shape matching34Biomimicry for natural and synthetic composites and use of machine learning in hierarchical design2Evaluation of Performance of Chlorinated Polyethylene Using Wireless Network and Artificial Intelligence Technology5Extracting and Analyzing Inorganic Material Synthesis Procedures in the Literature10Measurement Extraction with Natural Language Processing: A Review14Machine Learning and Natural Language Processing Enable a Data-Oriented Experimental Design Approach for Producing Biochar and Hydrochar from Biomass67Automated pipeline for superalloy data by text mining83Unsupervised machine learning for solar cell materials from the literature19MOFSimplify, machine learning models with extracted stability data of three thousand metal–organic frameworks135Artificial Intelligence and Machine Learning in Medicinal Chemistry and Validation of Emerging Drug Targets20Text Mining for Energy Materials3Quantifying the advantage of domain-specific pre-training on named entity recognition tasks in materials science163Recent advances and applications of deep learning methods in materials science1161Challenges in Information-Mining the Materials Literature: A Case Study and Perspective21Dataset of solution-based inorganic materials synthesis procedures extracted from the scientific literature83Distilling seed-assisted zeolite synthesis conditions by machine learning9Data-Driven Mapping of Inorganic Chemical Space for the Design of Transition Metal Complexes and Metal-Organic Frameworks0Materials information extraction via automatically generated corpus30Machine-Learning Rationalization and Prediction of Solid-State Synthesis Conditions79Uncertainty Informed Screening for Safer Solvents used in the Synthesis of Perovskite Based Solar Cells via Machine Learning1Machine learning for a sustainable energy future517Artificial intelligence for materials research at extremes20Toward autonomous laboratories: Convergence of artificial intelligence and experimental automation118Development of a baseline model for MAX/MXene synthesis recipes extraction via pre-trained model with domain knowledge5Deep learning of electrochemical CO 2 conversion literature reveals research trends and directions18Machine learning in energy chemistry: introduction, challenges and perspectives31MatSci-NLP: Evaluating Scientific Language Models on Materials Science Language Tasks Using Text-to-Schema Modeling32Realizing the cooking recipe of materials synthesis through large language models14Target and Precursor Named Entities Recognition from Scientific Texts of High-Temperature Steel Using Deep Neural Network3Text-to-Ontology Mapping via Natural Language Processing with Application to Search for Relevant Ontologies in Catalysis20Knowledge-integrated machine learning for materials: lessons from gameplaying and robotics137Materials informatics for developing new restorative dental materials: a narrative review17A Fourier-transformed feature engineering design for predicting ternary perovskite properties by coupling a two-dimensional convolutional neural network with a support vector machine (Conv2D-SVM)7Interpretable Machine Learning Enabled Inorganic Reaction Classification and Synthesis Condition Prediction41Extracting Accurate Materials Data from Research Papers with Conversational Language Models and Prompt Engineering10A corpus of CO2 electrocatalytic reduction process extracted from the scientific literature18Natural Language Processing Techniques for Advancing Materials Discovery: A Short Review18Precursor recommendation for inorganic synthesis by machine learning materials similarity from scientific literature65Material Property Prediction by Integrating Literature-mining and Crossed Feature Selection0Predicting organic structures directing agents for zeolites with conditional deep learning generative model17Predicting the synthesizability of crystalline inorganic materials from the data of known material compositions38A literature-mining method of integrating text and table extraction for materials science publications15Glassomics: An omics approach toward understanding glasses through modeling, simulations, and artificial intelligence7Alloy synthesis and processing by semi-supervised text mining28VOLTA: a tool for battery screening bridging the gap between virtual electrode materials and practical applications0Development of a Flux-Method Process Informatics System and Its Application in Growth Control for Layered Perovskite Ba 5 Nb 4 O 15 Crystals3Predicting synthesis recipes of inorganic crystal materials using elementwise template formulation15Artificial intelligence-powered electronic skin255Text Mining the Literature to Inform Experiments and Rationalize Impurity Phase Formation for BiFeO312Accelerating materials research with a comprehensive data management tool: a case study on an electrochemical laboratory13Automated extraction of synthesis parameters of pulsed laser-deposited materials from scientific literature8Flexible, model-agnostic method for materials data extraction from text using general purpose language models42Natural Language Processing7Novelty Detection in the Design of Synthesis of Energy Storage Materials: a Case Study of Garnet-Structured Solid Electrolytes1Understanding Hot Injection Quantum Dot Synthesis Outcomes Using Automated High-Throughput Experiment Platforms and Machine Learning24VOLTA: A tool for battery screening bridging the gap between virtual electrode materials and practical applications4Extracting accurate materials data from research papers with conversational language models and prompt engineering354Data-driven exploration of terbium-doped tungsten oxide for Ultra-Precise detection of 3H-2B: Implications for gas sensor applications13ZeoSyn: A Comprehensive Zeolite Synthesis Dataset Enabling Machine-Learning Rationalization of Hydrothermal Parameters53Novelty detection in the design of synthesis of garnet-structured solid electrolytes6Learning Design Rules for Catalysts Through Computational Chemistry and Machine Learning1A materials terminology knowledge graph automatically constructed from text corpus21Leveraging natural language processing to curate the tmCAT, tmPHOTO, tmBIO, and tmSCO datasets of functional transition metal complexes10Large Language Models for Inorganic Synthesis Predictions73A critical reflection on attempts to machine-learn materials synthesis insights from text-mined literature recipes16MPNTEXT: An Interactive Platform for Automatically Extracting Metal-Polyphenol Networks and Their Applications from Scientific Literature3ZeoReader: Automated extraction of synthesis steps from zeolite synthesis literature for autonomous experiments5Impact of data bias on machine learning for crystal compound synthesizability predictions16Large Language Model-Driven Knowledge Discovery for Designing Advanced Micro/Nano Electrocatalyst Materials4Solid-state synthesizability predictions using positive-unlabeled learning from human-curated literature data8Discovering emergent connections in quantum physics research via dynamic word embeddings2Advanced theoretical modeling methodologies for electrocatalyst design in sustainable energy conversion28Foundation models for materials discovery – current state and future directions102LLM-Driven Synthesis Planning for Quantum Dot Materials Development7Deep Learning-Based Framework for Efficient Design of Multicomponent High Hardness High Entropy Alloys4Applications of natural language processing and large language models in materials discovery132Large Language Models Assisted Materials Development: Case of Predictive Analytics for Oxygen Evolution Reaction Catalysts of (Oxy)hydroxides16From pages to patterns: Towards extracting catalytic knowledge from structure and text for transition-metal complexes and metal-organic frameworks6Language models for materials discovery and sustainability: Progress, challenges, and opportunities26Materials Informatics Opens the Door for Dental Materials Development9L2D: A Versatile Literature-to-Data set Pipeline for Complex, User-Specific Data Extraction in Environmental Research6Enabling large language models for real-world materials discovery39Uncertainty-Informed Screening for Safer Solvents Used in the Synthesis of Perovskites via Language Models5Autonomous Materials Synthesis Laboratories: Integrating Artificial Intelligence with Advanced Robotics for Accelerated Discovery4Chat-rgie: precision extraction of rice germplasm data using large language models and prompt engineering1Ultrafast synthesis of ternary oxides via a design-test-make-analyze workflow1Text Mining for Process–Structure–Properties Relationships in Metals1Synthesis-Aware Materials Redesign via Large Language Models12Language Models Enable Data-Augmented Synthesis Planning for Inorganic Materials8Large Language Models (LLMs) for Materials Design14Digitalisation of inorganic chemistry with LLMs1Quantum Mechanics and Machine Learning0Artificial intelligence for energy materials research: From classical machine learning to large models4Space Series: Leveraging Geometric Features of Heterostructures for Cross-Composition Property Prediction of Core–Shell Quantum Dots0Machine Learning and Autonomous Systems for Accelerated Synthesis1Quantitative analysis of operating conditions and performance trends in CO2 capture from large-scale literature0Predicting Inorganic Material Synthesis Pathways Based on Language Models0Enhancing Generalization in Synthesizability Prediction of Structurally Dissimilar Materials0Transformative power of AI-driven knowledge extraction in materials science: From fragmented pipelines to unified understanding0A brief journey through aromaticity indices0

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  3. A Machine Learning Approach to Zeolite Synthesis Enabled by Automatic Literature Data Extraction

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  4. The materials science procedural text corpus: Annotating materials synthesis procedures with shallow semantic structures

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