Multi-Task Learning in Analyzing the Working capacity of MOFs

The screening and rational design of materials with optimal methane capture performance remain key challenges for environmental and energy applications. Metal–organic frameworks (MOFs) are considered among the most promising candidates for methane capture. However, existing studies often emphasize single-point adsorption capacity, overlooking the dynamic mechanisms associated with pressure swing adsorption (PSA) required for practical applications. The construction of this dataset aims to address this challenge and provide insights into PSA-related mechanisms in MOFs.This dataset comprises geometric and chemical descriptors, as well as methane adsorption capacities under six different pressures, calculated for chemically reasonable MOF structures screened from eight publicly available datasets. In addition, we provide our in-house multitask learning (MTL) algorithm for predicting the gravimetric and volumetric working capacities of MOFs.<b>CIF files</b>: CIF files for 252,352 MOFs;<b>Geometric descriptors</b>: 14 geometric descriptors;<b>Chemical descriptors</b>: 176 chemical descriptors;<b>Methane_v, Methane_g</b>: Volumetric and gravimetric working capacities for methane adsorption, including methane adsorption data under six pressures across three application scenarios (landfill gas treatment, methane purification, and methane storage);<b>MTL4MOFsWC</b>: Python code for training the MTL models to predict the working capacity of methane adsorption in MOFs;<b>best_model_v_full, best_model_v_sim, best_model_g_full, best_model_g_sim</b>: Pre-trained MTL models.Please cite the following literature when you use this database:DOI: https://doi.org/10.1039/d5ta10538b

开发兼具最优甲烷捕获性能的材料的筛选与合理设计，仍是环境与能源应用领域面临的核心挑战。金属有机框架（Metal–organic frameworks, MOFs）被认为是甲烷捕获领域最具潜力的候选材料之一。然而，现有研究往往仅关注单点吸附容量，却忽略了实际应用中所需的变压吸附（Pressure Swing Adsorption, PSA）相关动力学机制。本数据集的构建正是为了应对这一挑战，助力深入理解MOFs中与PSA相关的吸附机制。
本数据集包含针对从8个公开数据集筛选出的化学结构合理的MOFs所计算得到的几何与化学描述符，以及六种不同压力下的甲烷吸附容量。此外，本研究还提供自研的多任务学习（Multitask Learning, MTL）算法，用于预测MOFs的重量吸附工作容量与体积吸附工作容量。
<b>CIF文件</b>：涵盖252352个MOFs的CIF格式文件；
<b>几何描述符</b>：共14项几何描述符；
<b>化学描述符</b>：共176项化学描述符；
<b>Methane_v、Methane_g</b>：甲烷吸附的体积与重量吸附工作容量；
<b>MTL4MOFsWC</b>：用于训练MTL模型以预测MOFs中甲烷吸附工作容量的Python代码；
<b>best_model_v_full、best_model_v_sim、best_model_g_full、best_model_g_sim</b>：预训练完成的MTL模型。