LLM-Driven Synthesis Planning for Quantum Dot Materials Development DOI
So Eun Choi,

Minyong Jang,

Shinsook Yoon

и другие.

Journal of Chemical Information and Modeling, Год журнала: 2025, Номер unknown

Опубликована: Март 11, 2025

The application of large language models in materials science has opened new avenues for accelerating development. Building on this advancement, we propose a novel framework leveraging to optimize experimental procedures synthesizing quantum dot with multiple desired properties. Our integrates the synthesis protocol generation model and property prediction model, both fine-tuned open-source using parameter-efficient training techniques in-house data. Once target properties masked reference is generated, it undergoes validation through models, followed by assessments its novelty human evaluation. experiments demonstrate that among six protocols derived from entire framework, three successfully update Pareto front, all improve at least one property. Through empirical validation, confirm effectiveness our model-driven planning, showcasing strong performance under multitarget optimization.

Язык: Английский

ChatGPT Research Group for Optimizing the Crystallinity of MOFs and COFs DOI Creative Commons
Zhiling Zheng, Oufan Zhang, Ha L. Nguyen

и другие.

ACS Central Science, Год журнала: 2023, Номер 9(11), С. 2161 - 2170

Опубликована: Ноя. 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.

Язык: Английский

Процитировано

61
Development of the design and synthesis of metal–organic frameworks (MOFs) – from large scale attempts, functional oriented modifications, to artificial intelligence (AI) predictions DOI Creative Commons
Zongsu Han, Yihao Yang, Joshua Rushlow

и другие.

Chemical Society Reviews, Год журнала: 2024, Номер unknown

Опубликована: Ноя. 25, 2024

The design and synthesis of MOFs have evolved from traditional large-scale approaches to function-oriented modifications, recently AI predictions, which save time, reduce costs, enhance the efficiency achieving target functions.

Язык: Английский

Процитировано

19
Large language models for reticular chemistry DOI
Zhiling Zheng, Nakul Rampal,

Theo Jaffrelot Inizan

и другие.

Nature Reviews Materials, Год журнала: 2025, Номер unknown

Опубликована: Янв. 31, 2025

Язык: Английский

Процитировано

12
Agentic Large Language Models for Healthcare: Current Progress and Future Opportunities DOI Creative Commons
Han Yuan

Medicine Advances, Год журнала: 2025, Номер unknown

Опубликована: Март 3, 2025

Язык: Английский

Процитировано

3
A Multiagent-Driven Robotic AI Chemist Enabling Autonomous Chemical Research On Demand DOI
Tao Song, Man Luo, Xiaolong Zhang

и другие.

Journal of the American Chemical Society, Год журнала: 2025, Номер unknown

Опубликована: Март 8, 2025

The successful integration of large language models (LLMs) into laboratory workflows has demonstrated robust capabilities in natural processing, autonomous task execution, and collaborative problem-solving. This offers an exciting opportunity to realize the dream chemical research on demand. Here, we report a robotic AI chemist powered by hierarchical multiagent system, ChemAgents, based on-board Llama-3.1-70B LLM, capable executing complex, multistep experiments with minimal human intervention. It operates through Task Manager agent that interacts researchers coordinates four role-specific agents─Literature Reader, Experiment Designer, Computation Performer, Robot Operator─each leveraging one foundational resources: comprehensive Literature Database, extensive Protocol Library, versatile Model state-of-the-art Automated Lab. We demonstrate its versatility efficacy six experimental tasks varying complexity, ranging from straightforward synthesis characterization more complex exploration screening parameters, culminating discovery optimization functional materials. Additionally, introduce seventh task, where ChemAgents is deployed new chemistry lab environment autonomously perform photocatalytic organic reactions, highlighting ChemAgents's scalability adaptability. Our multiagent-driven showcases potential on-demand accelerate democratize access advanced across academic disciplines industries.

Язык: Английский

Процитировано

2
Synthetic Aspects and Characterization Needs in MOF Chemistry – from Discovery to Applications DOI Creative Commons
Bastian Achenbach, Aysu Yurduşen, Norbert Stock

и другие.

Advanced Materials, Год журнала: 2025, Номер unknown

Опубликована: Янв. 7, 2025

Even if MOFs are recently developed for large-scale applications, the road to applications of is long and rocky. This requires overcome challenges associated with phase discovery, synthesis optimization, basic advanced characterization, computational studies. Lab-scale results need be transferred processes, which often not trivial, life-cycle analyses techno-economic performed realistically assess their potential industrial relevance. Based on experience in field stable, functional combining synthesis, modeling, this mini-review gives recommendations especially non-specialists, example, from chemical engineers medical doctors, accelerate facilitate knowledge transfer will ultimately lead application MOFs. The include reporting characterization data as well standardization detailed information required mining machine learning techniques, increasingly used discovery new materials analysis. Once a suitable MOF identified its key properties determined, translational studies shall finally carried out collaboration end-users validate performance under real conditions allow understanding processes involved.

Язык: Английский

Процитировано

1
Artificial Intelligence Meets Laboratory Automation in Discovery and Synthesis of Metal–Organic Frameworks: A Review DOI
Yiming Zhao,

Yongjia Zhao,

Jian Wang

и другие.

Industrial & Engineering Chemistry Research, Год журнала: 2025, Номер 64(9), С. 4637 - 4668

Опубликована: Фев. 24, 2025

This review discusses the transformative impact of convergence artificial intelligence (AI) and laboratory automation on discovery synthesis metal–organic frameworks (MOFs). MOFs, known for their tunable structures extensive applications in fields such as energy storage, drug delivery, environmental remediation, pose significant challenges due to complex processes high structural diversity. Laboratory has streamlined repetitive tasks, enabled high-throughput screening reaction conditions, accelerated optimization protocols. The integration AI, particularly Transformers large language models (LLMs), further revolutionized MOF research by analyzing massive data sets, predicting material properties, guiding experimental design. emergence self-driving laboratories (SDLs), where AI-driven decision-making is coupled with automated experimentation, represents next frontier research. While remain fully realizing potential this synergistic approach, AI heralds a new era efficiency innovation engineering materials.

Язык: Английский

Процитировано

1
Selectivity in Chemiresistive Gas Sensors: Strategies and Challenges DOI Creative Commons
Peresi Majura Bulemo, Dong‐Ha Kim, Hamin Shin

и другие.

Chemical Reviews, Год журнала: 2025, Номер unknown

Опубликована: Апрель 8, 2025

The demand for highly functional chemical gas sensors has surged due to the increasing awareness of human health monitor metabolic disorders or noncommunicable diseases, safety measures against harmful greenhouse and/or explosive gases, and determination food freshness. Over years dedicated research, several types chemiresistive have been realized with appreciable sensitivities toward various gases. However, critical issues such as poor selectivity sluggish response/recovery speeds continue impede their widespread commercialization. Specifically, mechanisms behind selective response some materials specific analytes remain unclear. In this review, we discuss state-of-the-art strategies employed attain gas-selective materials, particular emphasis on design, surface modification functionalization catalysts, defect engineering, material structure control, integration physical/chemical filtration media. nature surface-gas interactions supporting are elucidated, opening opportunities optimizing fine-tuning sensing performance, guiding selection most appropriate accurate detection This review concludes recommendations future research directions potential further improvements.

Язык: Английский

Процитировано

1
Symmetry is the Key to the Design of Reticular Frameworks DOI Creative Commons
Andrea Darù, John S. Anderson, Davide Μ. Proserpio

и другие.

Advanced Materials, Год журнала: 2025, Номер unknown

Опубликована: Май 2, 2025

Abstract De novo prediction of reticular framework structures is a challenging task for chemists and materials scientists. Herein, computational workflow that predicts list possible frameworks based on only the connectivity symmetry node linker building blocks presented. This ranked occurrence topologies in known structures, thus providing manageable number can be optimized using density functional theory, inform future experiments. broadly applicable, correctly materials, furthermore identifies novel unknown phases some systems. available online at https://rationaldesign.pythonanywhere.com/ .

Язык: Английский

Процитировано

1
An automatic end-to-end chemical synthesis development platform powered by large language models DOI Creative Commons
Yixiang Ruan,

Chenyin Lu,

Ning Xu

и другие.

Nature Communications, Год журнала: 2024, Номер 15(1)

Опубликована: Ноя. 23, 2024

The rapid emergence of large language model (LLM) technology presents promising opportunities to facilitate the development synthetic reactions. In this work, we leveraged power GPT-4 build an LLM-based reaction framework (LLM-RDF) handle fundamental tasks involved throughout chemical synthesis development. LLM-RDF comprises six specialized agents, including Literature Scouter, Experiment Designer, Hardware Executor, Spectrum Analyzer, Separation Instructor, and Result Interpreter, which are pre-prompted accomplish designated tasks. A web application with as backend was built allow chemist users interact automated experimental platforms analyze results via natural language, thus, eliminating need for coding skills ensuring accessibility all chemists. We demonstrated capabilities in guiding end-to-end process copper/TEMPO catalyzed aerobic alcohol oxidation aldehyde reaction, literature search information extraction, substrate scope condition screening, kinetics study, optimization, scale-up product purification. Furthermore, LLM-RDF's broader applicability versability validated on various three distinct reactions (SNAr photoredox C-C cross-coupling heterogeneous photoelectrochemical reaction). rise offers new advancing synthesis. Here, authors developed copilot design

Язык: Английский

Процитировано

9