iScience, Journal Year: 2024, Volume and Issue: 27(4), P. 109451 - 109451
Published: March 7, 2024
This study explores the use of large language models (LLMs) in interpreting and predicting experimental outcomes based on given variables, leveraging human-like reasoning inference capabilities LLMs, using selective catalytic reduction NO
Language: Английский
JACS Au, Journal Year: 2024, Volume and Issue: 4(8), P. 3170 - 3182
Published: Aug. 12, 2024
In this study, we present the first example of using a machine learning (ML)-assisted design strategy to optimize synthesis formulation enzyme/ZIFs (zeolitic imidazolate framework) for enhanced performance. Glucose oxidase (GOx) and horseradish peroxidase (HRP) were chosen as model enzymes, while Zn(eIM)2 (eIM = 2-ethylimidazolate) was selected ZIF test our ML-assisted workflow paradigm. Through an iterative ML-driven training-design-synthesis-measurement workflow, efficiently discovered GOx/ZIF (G151) HRP/ZIF (H150) with their overall performance index (OPI) values (OPI represents product encapsulation efficiency (E in %), retained enzymatic activity (A thermal stability (T %)) at least 1.3 times higher than those systematic seed data studies. Furthermore, advanced statistical methods derived from trained random forest qualitatively quantitatively reveal relationship among synthesis, structure, enzyme/ZIF system, offering valuable guidance future studies on enzyme/ZIFs. Overall, proposed holds promise accelerating development other enzyme immobilization systems biocatalysis applications beyond, including drug delivery sensing, others.
Language: Английский
Citations
6
Journal of Chemical Education, Journal Year: 2024, Volume and Issue: 101(7), P. 2740 - 2748
Published: June 17, 2024
The effective and responsible educational application of ChatGPT other generative artificial intelligence (GenAI) tools constitutes an active area exploration. This study describes assesses the implementation a structured, GenAI-assisted scientific essay writing assignment in nucleic acid biochemistry. Briefly, students created, evaluated, iteratively refined essays response to feedback independent literature research, identifying several strengths shortcomings large language model citation practices. scaffolded structure aimed prepare for writing, majority class cohort ultimately indicated improved understanding GenAI functionality prompt engineering, as well interest additional usage applications. Moreover, valued instructional guidance on engagement with engineering opportunities afforded by this exercise. However, discontentment AI-produced citations was common, 26% supporting references were found be nonexistent. content evaluation generation strategies uncovered here may facilitate successful ChatGPT-guided assignments contexts.
Language: Английский
Citations
5
ACS Nano, Journal Year: 2024, Volume and Issue: unknown
Published: Sept. 24, 2024
Atomically precise metal nanoclusters (MNCs) represent a fascinating class of ultrasmall nanoparticles with molecule-like properties, bridging conventional metal-ligand complexes and nanocrystals. Despite their potential for various applications, synthesis challenges such as understanding varied synthetic parameters property-driven persist, hindering full exploitation wider application. Incorporating smart methodologies, including closed-loop framework automation, data interpretation, feedback from AI, offers promising solutions to address these challenges. In this perspective, we summarize the that has been demonstrated in nanomaterials explore research frontiers MNCs. Moreover, perspectives on inherent opportunities MNCs are discussed, aiming provide insights directions future advancements emerging field AI Science, while integration deep learning algorithms stands substantially enrich by offering enhanced predictive capabilities, optimization strategies, control mechanisms, thereby extending MNC synthesis.
Language: Английский
Citations
5
Nature Communications, Journal Year: 2024, Volume and Issue: 15(1)
Published: Nov. 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
Language: Английский
Citations
5
iScience, Journal Year: 2024, Volume and Issue: 27(4), P. 109451 - 109451
Published: March 7, 2024
This study explores the use of large language models (LLMs) in interpreting and predicting experimental outcomes based on given variables, leveraging human-like reasoning inference capabilities LLMs, using selective catalytic reduction NO
Language: Английский
Citations
4