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RESEARCH PAPER · Nature Chemistry · 2022

Making the collective knowledge of chemistry open and machine actionable

Kevin Maik Jablonka, Luc Patiny, Berend Smit

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Making the collective knowledge of chemistry open and machine actionableOriginalData storage architectures to accelerate chemical discovery: data accessibility for individual laboratories and the community21Exploring electronic lab notebooks (ELNs) at a R1 institution in the Southeast USA2Machine Learning Yield Prediction from NiCOlit, a Small-Size Literature Data Set of Nickel Catalyzed C–O Couplings87Machine Learning in Tissue Engineering73CRIPT: A Scalable Polymer Material Data Structure6Into the Unknown: How Computation Can Help Explore Uncharted Material Space65Developing Catalysts via Structure‐Property Relations Discovered by Machine Learning: An Industrial Perspective6Mindestinformationsstandards in der Chemie: Ein Appell zum besseren Umgang mit Forschungsdaten2Minimum Information Standards in Chemistry: A Call for Better Research Data Management Practices37Using genetic algorithms to systematically improve the synthesis conditions of Al-PMOF3114 examples of how LLMs can transform materials science and chemistry: a reflection on a large language model hackathon214Engaging with Researchers and Raising Awareness of FAIR and Open Science through the FAIR+ Implementation Survey Tool (FAIRIST)0GitHub as an open electronic laboratory notebook for real-time sharing of knowledge and collaboration6Machine learning for industrial processes: Forecasting amine emissions from a carbon capture plant49Machine Learning and Digital Manufacturing Approaches for Solid‐State Materials Development5Overview of AI in the Understanding and Design of Nanoporous Materials2Is GPT-3 all you need for low-data discovery in chemistry?26Community Resource for Innovation in Polymer Technology (CRIPT): A Scalable Polymer Material Data Structure67An Ecosystem for Digital Reticular Chemistry52LinChemIn: SynGraph—a data model and a toolkit to analyze and compare synthetic routes12LabInform ELN: A lightweight and flexible electronic laboratory notebook for academic research based on the open-source software DokuWiki3AI4Green: An Open-Source ELN for Green and Sustainable Chemistry12Achieving Digital Catalysis: Strategies for Data Acquisition, Storage and Use37Achieving Digital Catalysis: Strategies for Data Acquisition, Storage and Use614 Examples of How LLMs Can Transform Materials Science and Chemistry: A Reflection on a Large Language Model Hackathon29Artificial intelligence driven design of catalysts and materials for ring opening polymerization using a domain-specific language39ML meets MLn: Machine learning in ligand promoted homogeneous catalysis20Revealing factors influencing polymer degradation with rank-based machine learning18Results of a Three-Year Survey on the Implementation of Research Data Management and the Electronic Laboratory Notebook (ELN) Chemotion in an Advanced Inorganic Lab Course9FAIRifying STEM Data Ecosystem to Enhance Data Reuse1Chemical Species Ontology for Data Integration and Knowledge Discovery38Covalent Organic Frameworks: Opportunities for Rational Materials Design in Cancer Therapy5Covalent Organic Frameworks: Opportunities for Rational Materials Design in Cancer Therapy72Twenty years of nmrshiftdb2: A case study of an open database for analytical chemistry33When Yield Prediction Does Not Yield Prediction: An Overview of the Current Challenges64Automated approaches, reaction parameterisation, and data science in organometallic chemistry and catalysis: towards improving synthetic chemistry and accelerating mechanistic understanding20Chemistry in a graph: modern insights into commercial organic synthesis planning2Digital chemistry: navigating the confluence of computation and experimentation – definition, status quo, and future perspective11Knowledge graph representation of zeolitic crystalline materials17Transforming research laboratories with connected digital twins19Leveraging large language models for predictive chemistry367Leveraging Text-to-Text Pretrained Language Models for Question Answering in Chemistry13LinChemIn: Route Arithmetic─Operations on Digital Synthetic Routes4Digital Futures of Learning Pharmacology and Medicinal and Organic Chemistry5Reaction Rebalancing: A Novel Approach to Curating Reaction Databases2Reaction Rebalancing: A Novel Approach to Curating Reaction Databases0Advancing materials science through next-generation machine learning30Artificial Intelligence for Retrosynthetic Planning Needs Both Data and Expert Knowledge34Promises and Perils of Big Data: Philosophical Constraints on Chemical Ontologies6Multiscale Modeling of Physical Properties of Nanoporous Frameworks: Predicting Mechanical, Thermal, and Adsorption Behavior15Synergistic sorption strategies: Ionic liquids-modified MOF matrices for adsorption processes42Data After Life: A data donor card to provoke questions concerning post-mortem data use5Reaction rebalancing: a novel approach to curating reaction databases6Logic and symbolism of switchable porous framework materials20Recent advances in artificial intelligent strategies for tissue engineering and regenerative medicine84Catalysing (organo-)catalysis: Trends in the application of machine learning to enantioselective organocatalysis17Assessment of fine-tuned large language models for real-world chemistry and material science applications52Building a roadmap for safer and sustainable material chemistries: Addressing the PFAS problem through informatics and data-driven chemistry5Activity recognition in scientific experimentation using multimodal visual encoding2What I Learned from Analyzing Accurate Mass Data of 3000 Supporting Information Files5On the Importance of Data – Introducing Perspectives as New Manuscript Type to SYNLETT0Accelerating compound synthesis in drug discovery: the role of digitalisation and automation4twa: The World Avatar Python package for dynamic knowledge graphs and its application in reticular chemistry10Synthetic Aspects and Characterization Needs in MOF Chemistry – from Discovery to Applications59A Holistic Data-Driven Approach to Synthesis Predictions of Colloidal Nanocrystal Shapes14Foundation models for materials discovery – current state and future directions102Discovering organic reactions with a machine-learning-powered deciphering of tera-scale mass spectrometry data19Carbohydrate Synthesis is Entering the Data‐Driven Digital Era3Negative chemical data boosts language models in reaction outcome prediction21Knowledge graphs in heterogeneous catalysis: Recent advances and future opportunities7Large Language Model in Materials Science: Roles, Challenges, and Strategic Outlook10Probing the limitations of multimodal language models for chemistry and materials research39Automating structure–activity analysis for electrochemical nitrogen reduction catalyst design through multi-agent collaborations19Toward Reliable and Reproducible Research in Organic Photocatalysis by Carbon Nitride4Advances in low-dimensional graphene and conductive polymers for tissue engineering and regenerative applications0Organic synthesis in the 21st century0Artificial intelligence and machine learning for plasmonic and surface-enhanced sensing37Clever materials: when models identify good materials for the wrong reasons0General-Purpose Models for the Chemical Sciences: LLMs and Beyond6Enabling open and FAIR catalysis data with standardized data structures3Harnessing Artificial Intelligence (AI) for a greener future: a review of AI advancements in green chemistry, chemical processes and sustainable materials3A five-level cascade workflow for chemical reaction balancing and completion: Bridging deterministic graph algorithms and large language models0

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  2. Exploring electronic lab notebooks (ELNs) at a R1 institution in the Southeast USA

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  3. Machine Learning Yield Prediction from NiCOlit, a Small-Size Literature Data Set of Nickel Catalyzed C–O Couplings

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  4. Machine Learning in Tissue Engineering

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  6. Into the Unknown: How Computation Can Help Explore Uncharted Material Space

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  7. Developing Catalysts via Structure‐Property Relations Discovered by Machine Learning: An Industrial Perspective

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  10. Using genetic algorithms to systematically improve the synthesis conditions of Al-PMOF

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  11. 14 examples of how LLMs can transform materials science and chemistry: a reflection on a large language model hackathon

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  12. Engaging with Researchers and Raising Awareness of FAIR and Open Science through the FAIR+ Implementation Survey Tool (FAIRIST)

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  13. GitHub as an open electronic laboratory notebook for real-time sharing of knowledge and collaboration

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  14. Machine learning for industrial processes: Forecasting amine emissions from a carbon capture plant

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  15. Machine Learning and Digital Manufacturing Approaches for Solid‐State Materials Development

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  16. Overview of AI in the Understanding and Design of Nanoporous Materials

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  17. Is GPT-3 all you need for low-data discovery in chemistry?

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  19. An Ecosystem for Digital Reticular Chemistry

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  25. 14 Examples of How LLMs Can Transform Materials Science and Chemistry: A Reflection on a Large Language Model Hackathon

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