Chemspyd:用于 Chemspeed 机器人化学和材料平台的开源 python 界面

IF 6.2 Q1 CHEMISTRY, MULTIDISCIPLINARY Digital discovery Pub Date : 2024-05-22 DOI:10.1039/D4DD00046C
Martin Seifrid, Felix Strieth-Kalthoff, Mohammad Haddadnia, Tony C. Wu, Emre Alca, Leticia Bodo, Sebastian Arellano-Rubach, Naruki Yoshikawa, Marta Skreta, Rachel Keunen and Alán Aspuru-Guzik
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引用次数: 0

摘要

我们介绍的 Chemspyd 是一款轻量级开源 Python 软件包,用于操作 Chemspeed Technologies 公司的流行实验室机器人平台。作为现有专有软件套件的附加组件,Chemspyd 实现了与自动化平台的动态通信,为将其模块化集成到可定制的、更高级别的实验室工作流程中奠定了基础。我们在一组化学和材料科学案例研究中展示了 Chemspyd 的适用性。我们展示了该软件包如何与大型语言模型一起使用,以提供自然语言界面。通过为商用机器人平台提供开源软件接口,我们希望能够激励开放接口的开发,从而促进现有实验室设备灵活、自适应地集成到自动化实验室中。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Chemspyd: an open-source python interface for Chemspeed robotic chemistry and materials platforms†

We introduce Chemspyd, a lightweight, open-source Python package for operating the popular laboratory robotic platforms from Chemspeed Technologies. As an add-on to the existing proprietary software suite, Chemspyd enables dynamic communication with the automated platform, laying the foundation for its modular integration into customizable, higher-level laboratory workflows. We show the applicability of Chemspyd in a set of case studies from chemistry and materials science. We demonstrate how the package can be used with large language models to provide a natural language interface. By providing an open-source software interface for a commercial robotic platform, we hope to inspire the development of open interfaces that facilitate the flexible, adaptive integration of existing laboratory equipment into automated laboratories.

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Back cover ArcaNN: automated enhanced sampling generation of training sets for chemically reactive machine learning interatomic potentials. Sorting polyolefins with near-infrared spectroscopy: identification of optimal data analysis pipelines and machine learning classifiers†‡ High accuracy uncertainty-aware interatomic force modeling with equivariant Bayesian neural networks† Correction: A smile is all you need: predicting limiting activity coefficients from SMILES with natural language processing
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