Nature Synthesis, Journal Year: 2024, Volume and Issue: 3(11), P. 1327 - 1339
Published: Oct. 10, 2024
Language: Английский
Digital Discovery, Journal Year: 2024, Volume and Issue: 3(7), P. 1319 - 1326
Published: Jan. 1, 2024
We introduce Chemspyd , a lightweight, open-source Python package for operating the popular laboratory robotic platforms from Chemspeed Technologies.
Language: Английский
Citations
3
Published: July 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.
Language: Английский
Citations
3
Journal of Materials Chemistry A, Journal Year: 2024, Volume and Issue: 12(18), P. 10773 - 10783
Published: Jan. 1, 2024
Compliance with good research data management practices means trust in the integrity of data, and it is achievable by full control gathering process. In this work, we demonstrate tooling which bridges these two aspects, illustrate its use a case study automated battery cycling. We successfully interface off-the-shelf cycling hardware computational workflow software AiiDA, allowing us to experiments, while ensuring tracking provenance. design user interfaces compatible tooling, span inventory, experiment design, result analysis stages. Other features, including monitoring workflows import externally generated legacy are also implemented. Finally, stack required for work made available set open-source packages.
Language: Английский
Citations
2
Published: July 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.
Language: Английский
Citations
2
Nature Synthesis, Journal Year: 2024, Volume and Issue: 3(11), P. 1327 - 1339
Published: Oct. 10, 2024
Language: Английский
Citations
2