.png){kind=icon}
返回
Artificial Intelligence (AI) Workflow for Catalyst Design and Optimization
.png){kind=icon}
OA
AI
DOI：10.1021/acs.iecr.3c02520.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In the pursuit of novel catalyst development to address pressing environmental concerns and energy demand, conventional design and optimization methods often fall short due to the complexity and vastness of the catalyst parameter space. The advent of Machine Learning (ML) has ushered in a new era in the field of catalyst optimization, offering potential solutions to the shortcomings of traditional techniques. However, existing methods fail to effectively harness the vast information contained within the expanding body of scientific literature on catalyst synthesis. To address this gap, this study proposes an innovative Artificial Intelligence (AI) workflow that integrates large-language models (LLMs), Bayesian optimization, and an active learning loop to expedite and enhance catalyst optimization. Our methodology combines advanced language understanding with robust optimization strategies, effectively translating knowledge extracted from the diverse literature into actionable parameters for practical experimentation and optimization. In this article, we demonstrate the application of this AI workflow in the optimization of catalyst synthesis for ammonia production. The results underscore the workflow's ability to streamline the catalyst development process, offering a swift, resource-efficient, and high-precision alternative to conventional methods.
Keyword:
BAYESIAN OPTIMIZATION
期刊
I
IF:
3.9
论文数:
4.0W
被引数:
9.6W
机构
引用论文
分享收藏Energy-optimal mobile application execution: Taming resource-poor mobile devices with cloud clones能源优化的移动应用程序执行: 使用云克隆驯服资源贫乏的移动设备
PREAI
分享收藏Exploring the Optimal Alloy for Nitrogen Activation by Combining Bayesian Optimization with Density Functional Theory Calculations.png)
ACS OMEGA
OAAI
ACS OMEGAIF4.3
OAAI分享收藏
查看更多内容