Graph neural networks for materials science and chemistry

Communications Materials, Journal Year: 2022, Volume and Issue: 3(1)

Published: Nov. 26, 2022

Abstract Machine learning plays an increasingly important role in many areas of chemistry and materials science, being used to predict properties, accelerate simulations, design new structures, synthesis routes materials. Graph neural networks (GNNs) are one the fastest growing classes machine models. They particular relevance for as they directly work on a graph or structural representation molecules therefore have full access all relevant information required characterize In this Review, we provide overview basic principles GNNs, widely datasets, state-of-the-art architectures, followed by discussion wide range recent applications GNNs concluding with road-map further development application GNNs.

Language: Английский

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ChatGPT Chemistry Assistant for Text Mining and the Prediction of MOF Synthesis

Journal of the American Chemical Society, Journal Year: 2023, Volume and Issue: 145(32), P. 18048 - 18062

Published: Aug. 7, 2023

We use prompt engineering to guide ChatGPT in the automation of text mining metal-organic frameworks (MOFs) synthesis conditions from diverse formats and styles scientific literature. This effectively mitigates ChatGPT's tendency hallucinate information -- an issue that previously made Large Language Models (LLMs) fields challenging. Our approach involves development a workflow implementing three different processes for mining, programmed by itself. All them enable parsing, searching, filtering, classification, summarization, data unification with tradeoffs between labor, speed, accuracy. deploy this system extract 26,257 distinct parameters pertaining approximately 800 MOFs sourced peer-reviewed research articles. process incorporates our ChemPrompt Engineering strategy instruct resulting impressive precision, recall, F1 scores 90-99%. Furthermore, dataset built we constructed machine-learning model over 86% accuracy predicting MOF experimental crystallization outcomes preliminarily identifying important factors crystallization. also developed reliable data-grounded chatbot answer questions on chemical reactions procedures. Given using reliably mines tabulates unified format, while only narrative language requiring no coding expertise, anticipate Chemistry Assistant will be very useful across various other chemistry sub-disciplines.

Language: Английский

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Metalated covalent organic frameworks: from synthetic strategies to diverse applications

Chemical Society Reviews, Journal Year: 2022, Volume and Issue: 51(15), P. 6307 - 6416

Published: Jan. 1, 2022

This review highlights the recent advances of metalated covalent organic frameworks, including synthetic strategies and applications, discusses current challenges future directions.

Language: Английский

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MOF Synthesis Prediction Enabled by Automatic Data Mining and Machine Learning**

Angewandte Chemie International Edition, Journal Year: 2022, Volume and Issue: 61(19)

Published: Feb. 1, 2022

Abstract Despite rapid progress in the field of metal–organic frameworks (MOFs), potential using machine learning (ML) methods to predict MOF synthesis parameters is still untapped. Here, we show how ML can be used for rationalization and acceleration discovery process by directly predicting conditions a based on its crystal structure. Our approach on: i) establishing first database via automatic extraction from literature, ii) training optimizing models employing database, iii) new structures. The models, even at an initial stage, exhibit good prediction performance, outperforming human expert predictions, obtained through survey. automated available web‐tool https://mof‐synthesis.aimat.science .

Language: Английский

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Recent advances in computational modeling of MOFs: From molecular simulations to machine learning

Coordination Chemistry Reviews, Journal Year: 2023, Volume and Issue: 484, P. 215112 - 215112

Published: March 21, 2023

The reticular chemistry of metal–organic frameworks (MOFs) allows for the generation an almost boundless number materials some which can be a substitute traditionally used porous in various fields including gas storage and separation, catalysis, drug delivery. MOFs their potential applications are growing so quickly that, when novel synthesized, testing them all possible is not practical. High-throughput computational screening approaches based on molecular simulations have been widely to investigate identify optimal specific application. Despite resources, given enormous MOF material space, identification promising requires more efficient terms time effort. Leveraging data-driven science techniques offer key benefits such as accelerated design discovery pathways via establishment machine learning (ML) models interpretation complex structure-performance relationships that reach beyond expert intuition. In this review, we present scientific breakthroughs propelled modeling discuss state-of-the-art extending from ML algorithms. Finally, provide our perspective opportunities challenges future big discovery.

Language: Английский

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MOFs-Based Materials for Solid-State Hydrogen Storage: Strategies and Perspectives

Chemical Engineering Journal, Journal Year: 2024, Volume and Issue: 485, P. 149665 - 149665

Published: Feb. 15, 2024

Language: Английский

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A GPT‐4 Reticular Chemist for Guiding MOF Discovery**

Angewandte Chemie International Edition, Journal Year: 2023, Volume and Issue: 62(46)

Published: Oct. 6, 2023

We present a new framework integrating the AI model GPT-4 into iterative process of reticular chemistry experimentation, leveraging cooperative workflow interaction between and human researcher. This Reticular Chemist is an integrated system composed three phases. Each these utilizes in various capacities, wherein provides detailed instructions for chemical experimentation feedback on experimental outcomes, including both success failures, in-context learning next iteration. human-AI enabled to learn from much like experienced chemist, by prompt-learning strategy. Importantly, based natural language development operation, eliminating need coding skills, thus, make it accessible all chemists. Our collaboration with guided discovery isoreticular series MOFs, each synthesis fine-tuned through expert suggestions. presents potential broader applications scientific research harnessing capability large models enhance feasibility efficiency activities.

Language: Английский

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ChatGPT Research Group for Optimizing the Crystallinity of MOFs and COFs

ACS Central Science, Journal Year: 2023, Volume and Issue: 9(11), P. 2161 - 2170

Published: Nov. 10, 2023

We leveraged the power of ChatGPT and Bayesian optimization in development a multi-AI-driven system, backed by seven large language model-based assistants equipped with machine learning algorithms, that seamlessly orchestrates multitude research aspects chemistry laboratory (termed Research Group). Our approach accelerated discovery optimal microwave synthesis conditions, enhancing crystallinity MOF-321, MOF-322, COF-323 achieving desired porosity water capacity. In this human researchers gained assistance from these diverse AI collaborators, each unique role within environment, spanning strategy planning, literature search, coding, robotic operation, labware design, safety inspection, data analysis. Such comprehensive enables single researcher working concert to achieve productivity levels analogous those an entire traditional scientific team. Furthermore, reducing biases screening experimental conditions deftly balancing exploration exploitation parameters, our search precisely zeroed on pool 6 million significantly shortened time scale. This work serves as compelling proof concept for AI-driven revolution laboratory, painting future where becomes efficient collaborator, liberating us routine tasks focus pushing boundaries innovation.

Language: Английский

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Biomedical Metal–Organic Framework Materials: Perspectives and Challenges

Advanced Functional Materials, Journal Year: 2023, Volume and Issue: 34(43)

Published: Nov. 21, 2023

Abstract Metal–organic framework (MOF) materials are gaining significant interest in biomedical research, owing to their high porosity, crystallinity, and structural compositional diversity. Their versatile hybrid organic/inorganic chemistry endows MOFs with the capacity retain organic (drug) molecules, metals, gases, effectively channel electrons photons, survive harsh physiological conditions such as low pH, even protect sensitive biomolecules. Extensive preclinical research has been carried out treat several pathologies and, recently, integration other stents implants demonstrated promising performance regenerative medicine. However, there remains a gap between MOF translation into clinically societally relevant medicinal products. Here, intrinsic features of outlined suitability specific applications detoxification, drug gas delivery, or (combination) therapy platforms is discussed. Furthermore, examples how have engineered evaluated different medical indications, including cancer, microbial, inflammatory diseases described. Finally, challenges facing clinic critically examined, goal establishing directions more realistic approaches that can bridge translational MOF‐containing (nano)materials.

Language: Английский

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Fabrication of Oriented Polycrystalline MOF Superstructures

Advanced Materials, Journal Year: 2023, Volume and Issue: 36(1)

Published: Nov. 28, 2023

Abstract The field of metal‐organic frameworks (MOFs) has progressed beyond the design and exploration powdery single‐crystalline materials. A current challenge is fabrication organized superstructures that can harness directional properties individual constituent MOF crystals. To date, progress in methods polycrystalline led to close‐packed structures with defined crystalline orientation. By controlling orientation, pore channels crystals be aligned along specific directions: these systems possess anisotropic including enhanced diffusion directions, preferential orientation guest species, protection functional guests. In this perspective, we discuss status research oriented focusing on directions Three are examined detail: assembly from colloidal solutions, use external fields for alignment particles, heteroepitaxial ceramic‐to‐MOF growth. This perspective aims at promoting inspiring development new protocols preparation channels, enable advanced MOF‐based devices properties.

Language: Английский

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