Chinese Science Bulletin (Chinese Version), Год журнала: 2024, Номер 69(32), С. 4730 - 4747
Опубликована: Июль 5, 2024
Язык: Английский
Nature, Год журнала: 2024, Номер 635(8040), С. 890 - 897
Опубликована: Ноя. 6, 2024
Abstract Autonomous laboratories can accelerate discoveries in chemical synthesis, but this requires automated measurements coupled with reliable decision-making 1,2 . Most autonomous involve bespoke equipment 3–6 , and reaction outcomes are often assessed using a single, hard-wired characterization technique 7 Any algorithms 8 must then operate narrow range of data 9,10 By contrast, manual experiments tend to draw on wider instruments characterize products, decisions rarely taken based one measurement alone. Here we show that synthesis laboratory be integrated into an by mobile robots 11–13 make human-like way. Our modular workflow combines robots, platform, liquid chromatography–mass spectrometer benchtop nuclear magnetic resonance spectrometer. This allows share existing human researchers without monopolizing it or requiring extensive redesign. A heuristic decision-maker processes the orthogonal data, selecting successful reactions take forward automatically checking reproducibility any screening hits. We exemplify approach three areas structural diversification chemistry, supramolecular host–guest chemistry photochemical synthesis. strategy is particularly suited exploratory yield multiple potential as for assemblies, where also extend method function assay evaluating binding properties.
Язык: Английский
Процитировано
27
Digital Discovery, Год журнала: 2025, Номер unknown
Опубликована: Янв. 1, 2025
Automation of electrochemical measurements can accelerate the discovery new electroactive materials.
Язык: Английский
Процитировано
1
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
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
Опубликована: Июль 30, 2024
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 multi-agent system, ChemAgents, based on-board Llama-3-70B LLM, capable executing complex, multi-step 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. Our multi-agent-driven showcases potential on-demand drive unprecedented efficiencies, accelerate discovery, democratize access advanced across academic disciplines industries.
Язык: Английский
Процитировано
3
Nature, Год журнала: 2025, Номер 637(8047), С. 1008 - 1011
Опубликована: Янв. 20, 2025
Язык: Английский
Процитировано
0
Current Opinion in Electrochemistry, Год журнала: 2025, Номер unknown, С. 101679 - 101679
Опубликована: Март 1, 2025
Язык: Английский
Процитировано
0
Опубликована: Апрель 1, 2025
Язык: Английский
Процитировано
0
Nano Convergence, Год журнала: 2025, Номер 12(1)
Опубликована: Апрель 18, 2025
Abstract Intuitive design strategies, primarily based on literature research and trial-and-error efforts, have significantly contributed to advancements in the electrocatalyst field. However, inherently time-consuming inconsistent nature of these methods presents substantial challenges accelerating discovery high-performance electrocatalysts. To this end, guided approaches, including in-situ experimental techniques data mining, emerged as powerful catalyst optimization tools. The former offers valuable insights into reaction mechanisms, while latter identifies patterns within large databases. In review, we first present examples using techniques, emphasizing a detailed analysis their strengths limitations. Then, explore data-mining-driven development, highlighting how data-driven approaches complement accelerate catalysts. Finally, discuss current possible solutions for design. This review aims provide comprehensive understanding methodologies inspire future innovations electrocatalytic research.
Язык: Английский
Процитировано
0
Nature Communications, Год журнала: 2025, Номер 16(1)
Опубликована: Апрель 24, 2025
Язык: Английский
Процитировано
0