Extracting recalcitrant redox data on fluorophores to pair with optical data for predicting small-molecule, ionic isolation lattices†

IF 6.2 Q1 CHEMISTRY, MULTIDISCIPLINARY Digital discovery Pub Date : 2024-09-03 DOI:10.1039/D4DD00137K
Michaela K. Loveless, Minwei Che, Alec J. Sanchez, Vikrant Tripathy, Bo W. Laursen, Sudhakar Pamidighantam, Krishnan Raghavachari and Amar H. Flood
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Abstract

Redox and optical data of organic fluorophores are essential for using design rules and property screening to identify new candidate dyes capable of forming optical materials. One such optical material is small-molecule, ionic isolation lattices (SMILES), which have properties defined by the optical and electrochemical properties of the fluorophores used. While optical data are available and readily extracted, the promise of digital discovery to mine the data and identify new dye candidates for making new fluorescent compounds is limited by experimental electrochemical data, which is reported with varying quality. We report methods to extract data from 20 000+ literature-reported dyes for generating a library of both redox and optical data constituted by 206 dye-solvent entries. Wide heterogeneity in data collection and reporting practices predicated use of a workflow involving manual data extraction, expert annotations of data quality and validation. Chemometric analysis shows distributions of solvents, electrolytes, and reference electrodes used in electrochemistry and the distributions of dye families and molecular weights. Data were extracted and screened to identify fluorophores predicted to form fluorescent solids based on SMILES. Screening used three design rules requiring dyes to be cationic, have a redox window within −1.9 and +1.5 V (vs. ferrocene), and a size less than 2 nm. A set of 47 dyes are compliant with all design rules showcasing the potential for using paired electrochemical-optical data in a workflow for designing optical materials.

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提取荧光团的氧化还原数据与光学数据配对,以预测小分子离子隔离晶格
有机荧光团的氧化还原和光学数据对于利用设计规则和特性筛选来确定能够形成光学材料的新候选染料至关重要。小分子离子隔离晶格(SMILES)就是这样一种光学材料,其特性由所用荧光团的光学和电化学特性决定。虽然光学数据可以获得并很容易提取,但数字发现技术在挖掘数据并确定用于制造新荧光化合物的新候选染料方面的前景却受到实验电化学数据的限制,而实验电化学数据的质量参差不齐。我们报告了从 20,000 多种文献报道的染料中提取数据的方法,以生成一个由 206 个染料-溶剂条目组成的氧化还原和光学数据库。由于数据收集和报告方法存在很大的差异,因此需要使用一种工作流程,其中包括手动数据提取、专家对数据质量的注释和验证。化学计量分析显示了电化学中使用的溶剂、电解质和参比电极的分布情况,以及染料家族和分子量的分布情况。对数据进行提取和筛选,以确定根据 SMILES 预测可形成荧光固体的荧光团。筛选采用了三条设计规则,要求染料必须是阳离子,氧化还原窗口在 -1.9 和 +1.5 V 之间(与二茂铁相比),且尺寸小于 2 nm。一组 47 种染料符合所有设计规则,展示了在设计光学材料的工作流程中使用成对电化学-光学数据的潜力。
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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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