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>The ChemFluor.zip is the platform based on python, which contain trained models, can be used for the prediction directly.Fingerprints_for_prediction.zip is the fingerprints used in our work.Materials_Real-World_Problem.zip 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.<br>Alldata_SMILES.xlsx contains all the molecules in our dataset as well as the references. <br>本研究构建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/吸收波长与光致量子产率（Photoluminescence Quantum Yield, PLQY）。同时搭建了可供实验人员使用的专属平台，可用于潜在的高通量筛选工作。<br>ChemFluor.zip 为基于Python开发的平台，内置训练完成的模型，可直接用于预测任务。<br>Fingerprints_for_prediction.zip 为本研究中使用的分子指纹数据集。<br>Materials_Real-World_Problem.zip 包含从近期已发表研究及含时密度泛函理论（Time-Dependent Density Functional Theory, TD-DFT）基准测试研究中收集的分子，可视为真实世界应用场景下的测试集，所有分子以SMILES格式存储。<br>Alldata_SMILES.xlsx 包含本研究数据集内的全部分子及其参考文献信息。