ChemFluor

We establish a machine learning-based method to predict emission/absorption wavelength and PLQY of organic fluorescent materials.A platform has been establised for experimenters to use, as well as used for potential high-throughput screening.<br><b>[1]</b><i>The ChemFluor_v0.1.zip</i> is the platform based on python, which contain trained models, can be used for the prediction directly.<b>[2]</b><i>Fingerprints_for_prediction.zip</i> is the fingerprints used in our work.<b>[3]</b><i>Materials_Real-World_Problem.zip</i> is the molecules collected from recent published work and TD-DFT benchmark studies, which can be seen as real world problem. The molecules are stored in the form of SMILES. <b>[4]</b><i>Alldata_SMILES_v0.1.xlsx</i> contains all the molecules in our dataset as well as the references.<br><b>[5]</b>ML-models we used in our paper for real world problems have been saved and uploaded, as<i> model_in_paper.zip.</i><b>[6]</b><i>Molecule-based_partition_withFP.zip </i>contain the training set and test set mentioned in our updated manuscript. We split our dataset based on molecules but not data-points in this file. <i><br></i><i>-----</i><i>update in 2020.07.21</i>[1] QY regressor model have been supported.

我们提出了一种基于机器学习的方法，用于预测有机荧光材料的发射/吸收波长与光致发光量子产率（Photoluminescence Quantum Yield, PLQY）。本研究构建了一款可供实验人员使用、并可用于潜在高通量筛选的平台。<br><b>[1]</b><i>ChemFluor_v0.1.zip</i> 为基于Python开发的平台，内置已训练完成的模型，可直接用于预测任务。<b>[2]</b><i>Fingerprints_for_prediction.zip</i> 包含本研究中使用的分子指纹。<b>[3]</b><i>Materials_Real-World_Problem.zip</i> 收录了从近期已发表研究及含时密度泛函理论（Time-Dependent Density Functional Theory, TD-DFT）基准测试研究中获取的分子，可视为真实世界测试场景；所有分子以简化分子线性输入规范（Simplified Molecular-Input Line-Entry System, SMILES）格式存储。<b>[4]</b><i>Alldata_SMILES_v0.1.xlsx</i> 收录了本数据集的全部分子及其相关参考文献。<b>[5]</b> 本研究论文中用于真实世界场景测试的机器学习模型已保存并上传，压缩包名为<i>model_in_paper.zip</i>。<b>[6]</b><i>Molecule-based_partition_withFP.zip</i> 包含本研究更新版手稿中提及的训练集与测试集；该文件基于分子而非数据点对数据集进行划分。<br><i>——</i> 2020年7月21日更新：<br>[1] 新增量子产率（Quantum Yield, QY）回归模型支持。

搜集汇总

背景与挑战

背景概述

ChemFluor是一个用于预测有机荧光材料光物理性质（如发射/吸收波长和光致发光量子产率）的机器学习数据集。它包含分子数据（SMILES格式）、预训练模型、指纹特征以及训练和测试集划分，旨在支持实验者进行预测和高通量筛选。数据集发布于2020年，属于化学信息学和有机化学领域，适用于机器学习在材料科学中的应用。