Machine Learning and GenAI datasets for the accelerated design of homogeneous catalysts for CO2 reduction - HPCvsCO2

The HPCvsCO2 project proposes a protocol to accelerate the discovery of new catalysts for the CO2 capture, addressing the computational resource limitations of traditional quantum chemistry methods. The core idea is to integrate Machine Learning (ML) techniques with computational chemistry to predict chemical properties, specifically the HOMO (Highest Occupied Molecular Orbital) and LUMO (Lowest Unoccupied Molecular Orbital) energy, drastically reducing the need to screen vast numbers of molecular configurations computationally. Furthermore, the project investigates the use of Genenerative AI (GenAI) to boost the performance of the ML algorithms within the workflow and generate molecules with a target structure. As part of the HPCvsCO2 project, two datasets of metal-centered catalyst complexes were produced: - The first dataset was used to train and test the UniMol machine learning model for predicting HOMO/LUMO values. - The second dataset was used for fine-tuning the REINVENT4 generative model for generating structurally valid complexes for the project. -The top 50 candidates were obtained by considering minimizing the HOMO-epoxide LUMO catalyst gap. In the folder, there are the xyz files and an .xlsx file with the HOMO and LUMO values of the molecules. Note: the HOMO and LUMO values and the HOMO-LUMO gap refer to the catalysts themselves, not to the HOMO_epox-LUMO_catalyst gap.HPCvsCO2项目提出了一套研究协议，旨在加速二氧化碳捕获用新型催化剂的研发进程，以解决传统量子化学方法面临的计算资源限制难题。 该项目的核心思路是将机器学习（Machine Learning，ML）技术与计算化学相结合，用于预测化学性质——具体为最高占据分子轨道（Highest Occupied Molecular Orbital, HOMO）与最低未占据分子轨道（Lowest Unoccupied Molecular Orbital, LUMO）的能量，从而大幅降低对海量分子构型进行计算筛选的需求。此外，该项目还探索了生成式AI（Generative AI, GenAI）的应用，以提升工作流内机器学习算法的性能，并生成具备目标结构的分子。 作为HPCvsCO2项目的组成部分，研究团队生成了两类金属中心催化剂配合物数据集： - 第一类数据集用于训练与测试UniMol机器学习模型，以预测HOMO与LUMO数值。 - 第二类数据集用于微调REINVENT4生成式模型，从而为本项目生成结构合规的配合物。 - 通过最小化HOMO-环氧化物LUMO催化剂间隙，筛选得到了前50名候选催化剂。该文件夹中包含各分子的xyz格式文件，以及一份记录了分子HOMO与LUMO数值的.xlsx文件。请注意：此处的HOMO、LUMO数值及HOMO-LUMO间隙均针对催化剂本身，而非HOMO_环氧物-LUMO_催化剂间隙。