Machine Learning-Driven Discovery and Structure-Activity Relationship Analysis of Conductive Metal-Organic Frameworks

英文原文与中文译文逐段对照 · English original with Chinese translation

Jinglong Lin, Huibao Zhang, Mojgan Asadi, Kai Zhao, Luming Yang, Yunlong Fan, Jintao Zhu, Qianyi Liu, Lei Sun, Wen Jun Xie, Chenru Duan, Fanyang Mo, and Jin-Hu Dou*

林京龙，张惠宝，莫贾甘·阿萨迪，赵凯，杨路明，范云龙，朱锦涛，刘谦益，孙雷，谢文君，段晨茹，莫凡洋，窦金虎*

Supporting Information

支持信息

ABSTRACT: Electrically conductive metal-organic frameworks (MOFs) are a class of materials with emergent applications in fields such as electrocatalysis, electrochemical energy storage, and chemiresistive sensors due to their unique combination of porosity and conductivity. However, due to the structural complexity and versatility, rational design of conductive MOFs is still challenging, which limits their further development and applications. To overcome this limitation, we established a database of 224 conductive MOFs, covering all of the reported conductive MOFs as far as we know, and utilized a combination of machine learning (ML) models and density functional theory (DFT) calculations to develop structure-conductivity relationship models. The interpretability of the models provided guidelines for the design of these materials and allowed us to identify new conductive MOFs through rapid screening. Subsequent experiments confirmed the model’s reliability and viability by synthesizing and validating a conductive MOF, CuTTPD, selected via the ML screening. Our results demonstrate that ML models are powerful tools for prescreening new conductive MOFs, thereby accelerating the development of this field.

摘要:导电金属有机框架(MOF)是一类由于其孔隙率和导电性的独特组合而在电催化、电化学储能和化学电阻传感器等领域具有新兴应用的材料。然而，由于结构的复杂性和多样性, 导电MOF的合理设计仍然具有挑战性，这限制了它们的进一步发展和应用。为了克服这一限制，我们建立了一个包含224种导电MOF的数据库，据我们所知涵盖了所有已报道的导电MOF，并利用机器学习(ML)模型和密度泛函理论(DFT)计算相结合的方法来建立结构-电导率关系模型。这些模型的可解释性为这些材料的设计提供了指导，并使我们能够通过快速筛选识别出新的导电MOF。随后的实验通过合成和验证通过ML筛选选择的导电MOF CuTTPD，证实了模型的可靠性和可行性。我们的结果表明，ML模型是预筛选新型导电MOF的强大工具，从而加速了该领域的发展。

INTRODUCTION

<## 引言>

Metal-organic frameworks (MOFs) are self-assembled porous materials consisting of metal ions and organic ligands. They have found applications in catalysis, ${}^{1,2}$ energy storage, ${}^{3}$ and gas separation ${}^{4,5}$ due to their high porosity and capacity of incorporating chemically active sites. Recently, the emergent research field of electrically conductive MOFs has attracted much interest because of their unique capability to conduct electricity in lattices with subnanometer-scale pores. Consequently, these nanoporous conductors have been applied in various fields such as electrocatalysis, ${}^{6}$ supercapacitors, ${}^{7}$ and transistors. ${}^{8}$ However, the development of this field is greatly hindered by the underexploration of electrical properties of MOFs: electrical conductivity has been reported for only several hundred materials among the tens of thousands of known MOFs. With these limited number of studies, several qualitative design principles have been proposed to improve carrier concentration and mobility, two crucial factors for achieving high electrical conductivity, mainly through the use of redox-active ligands and postsynthetic doping through donor-acceptor interaction. ${}^{9 - {11}}$ Nonetheless, deviations from these rules often occur, introducing uncertainty into the rational design of new conductive MOFs. Screening of known MOFs may help to articulate their structure-conductivity relationship. However, such screening is a complex and inefficient task due to the vast design space spanning over factors including metal-ligand combination, topology, and pore size. Consequently, more predictive guidelines are required for the efficient screening toward conductive MOFs.

金属有机框架(MOF)是由金属离子和有机配体自组装而成的多孔材料。由于其高孔隙率和容纳化学活性位点的能力，它们已在催化、${}^{1,2}$ 能量存储 ${}^{3}$ 和气体分离 ${}^{4,5}$ 等领域得到应用。最近，导电MOF这一新兴研究领域因其在具有亚纳米级孔隙的晶格中独特的导电能力而备受关注。因此，这些纳米多孔导体已被应用于各种领域，如电催化、${}^{6}$ 超级电容器 ${}^{7}$ 和晶体管 ${}^{8}$。然而，该领域的发展受到MOF电学性质研究不足的极大阻碍:在数以万计的已知MOF中，仅有几百种材料被报道具有导电性。基于这些有限的研究，人们提出了一些定性的设计原则，主要通过使用氧化还原活性配体和通过供体 - 受体相互作用进行后合成掺杂来提高载流子浓度和迁移率，这两个是实现高电导率的关键因素。${}^{9 - {11}}$ 尽管如此，这些规则经常出现偏差，给新型导电MOF的合理设计带来了不确定性。筛选已知的MOF可能有助于阐明它们结构与电导率的关系。然而，由于跨越金属 - 配体组合、拓扑结构和孔径等因素的巨大设计空间，这种筛选是一项复杂且低效的任务。因此，需要更多预测性指导方针来高效筛选导电MOF。

Machine learning (ML) is a method that empowers computers to learn from data and iteratively improve performance through the application of adaptive algorithms. Amid its rapid development, ML has been implemented by many researchers in the prediction of MOF properties, such as water stability and heat capacity. Generative models based on ML have also been utilized to design new MOFs with specific desired properties. ${}^{{12} - {17}}$ In this work, we developed efficient ML models for screening new conductive MOFs. We first compiled the conductive MOF (CM) database consisting of 224 experimentally tested MOFs along with their respective conductivity data, mined from the literature. We then categorized these MOFs into either two or three categories based on their level of conductivity using a binning algorithm and trained eight ML models derived from different algorithms for each category. The accuracy of ensemble model reached 90.48% on a binary classification test set and 85.71% on a ternary classification test set. Additionally, we developed three regression models, among which the artificial neural network (ANN) performed the best, achieving an ${R}^{2}$ value of 0.737 on test set. Searching in another MOF database, the QMOF database, ${}^{{18},{19}}$ using the trained ensemble model and ANN allowed us to both identify highly conductive MOFs with known experimental conductivity values and predict ones not yet characterized experimentally. Furthermore, our significance analysis revealed several factors influencing the conductivity of MOFs, some of which were understated in previous literature and can be rationalized in the context of existing theory for solid-state conductive materials. This ML-based prescreening method is capable of facilitating conductive MOF research by reducing overall experimental duration and costs. The insights gained through this study would contribute to the development of more efficient and effective methods for the design and synthesis of MOFs with the desired conductive properties.

机器学习(ML)是一种使计算机能够从数据中学习并通过应用自适应算法迭代提高性能的方法。在其快速发展过程中，许多研究人员已将ML用于预测MOF的性质，如水稳定性和热容量。基于ML的生成模型也已被用于设计具有特定所需性质的新型MOF。${}^{{12} - {17}}$ 在这项工作中，我们开发了用于筛选新型导电MOF的高效ML模型。我们首先编译了由224种经过实验测试的MOF及其各自的电导率数据组成的导电MOF(CM)数据库，这些数据来自文献。然后，我们使用分箱算法根据它们的导电水平将这些MOF分为两类或三类，并针对每个类别训练了八个源自不同算法的ML模型。在二元分类测试集上，集成模型的准确率达到90.48%，在三元分类测试集上达到85.71%。此外，我们开发了三个回归模型，其中人工神经网络(ANN)表现最佳，在测试集上的${R}^{2}$ 值达到0.737。在另一个MOF数据库QMOF数据库 ${}^{{18},{19}}$ 中搜索时，使用训练好的集成模型和ANN使我们能够识别具有已知实验电导率值的高导电MOF，并预测尚未经过实验表征的MOF。此外，我们的显著性分析揭示了几个影响MOF电导率的因素，其中一些在以前的文献中未被充分强调，并且可以在固态导电材料的现有理论背景下得到合理解释。这种基于ML的预筛选方法能够通过减少总体实验持续时间和成本来促进导电MOF的研究。通过这项研究获得的见解将有助于开发更高效、更有效的方法来设计和合成具有所需导电性质的MOF。

Received: January 26, 2024

收到日期:January 26, 2024

Revised: May 8, 2024

修订日期:May 8, 2024

Accepted: May 9, 2024

接受日期:May 9, 2024

Published: May 20, 2024

发表日期:May 20, 2024

Figure 1. Feature engineering of the MOFs. The descriptors of MOF are divided into three aspects. The metal descriptor considers electronic property, redox activity, and physical properties of the metal ions. The ligand descriptor contains physical and chemical information and the degree of conjugation of the ligand. The overlap descriptor is calculated by density functional calculation and reflects the degree of metal-ligand orbitals overlap.

图1. MOF的特征工程。MOF的描述符分为三个方面。金属描述符考虑金属离子的电子性质、氧化还原活性和物理性质。配体描述符包含配体的物理和化学信息以及共轭程度。重叠描述符通过密度泛函计算得出，反映金属 - 配体轨道重叠程度。

RESULTS AND DISCUSSION

结果与讨论

Feature Engineering of MOFs. To obtain ML models with high accuracy, it is crucial to provide a reasonable description of the physical and chemical properties of the MOF building blocks. To date, three charge transport models are usually invoked to explain the conduction mechanism of MOFs, including through-bond, ${}^{{10},{20} - {22}}$ through-space, ${}^{{11},{23}}$ and guest-promoted ${}^{9,{24},{25}}$ charge transport. The through-bond mechanism allows charge transport through the coordination bonds between the metal and linker with suitable spatial and energetic orbital overlap. The through-space mechanism, on the other hand, enables charge transport through noncovalent interactions between electroactive molecular moieties, such as those involved in $\pi - \pi$ stacking. Finally, the guest-promoted mechanism facilitates charge transport within an MOF through the host-guest redox interaction. Based on this knowledge, we implement three multidimensional vectors to describe the conductive MOF properties, including a metal descriptor, a ligand descriptor, and an overlap descriptor (Figure 1). The metal descriptor is a six-dimensional vector that contains the electronegativity $\left( \chi \right)$ , valence, atomic radius $\left( r\right)$ , softness, hardness, and coordination number (CN) of the metal centers. Among these, softness and hardness together describe the polarizability and electron accepting capacity of metal ions according to the classical Hard and Soft Acids and Bases (HSAB) theory. ${}^{{26},{27}}$ For example, metal ions with higher softness and lower hardness are more likely to form covalent interactions. CN describes the metal-ligand connectivity and can be obtained from topological analysis. Other features are obtained from the Mendeleev Python package. ${}^{28}$ The ligand descriptor is also a 6-dimensional vector, including molecular weight (MW), topological polar surface area (TPSA), lipid-water partition coefficient (LogP), number of hydrogen bond donors (HBD), number of hydrogen bond acceptors (HBA), and number of rotatable bonds (NROTB). TPSA represents the total surface area of polar atoms in a molecule. HBD and HBA are identified as atoms that can donate or accept hydrogen bonds, respectively. The overlap descriptor is a four-dimensional vector including the acid dissociation constant $\left( {\mathrm{p}{K}_{\mathrm{a}}}\right)$ , energy of the highest occupied molecular orbital (HOMO), energy of the lowest unoccupied molecular orbital (LUMO), and the HOMO-LUMO gap (GAP). Among all features, the $\mathrm{p}{K}_{\mathrm{a}}$ values were obtained from a trained prediction model ${}^{29}$ ; HOMO, LUMO, and GAP were obtained from DFT calculation; others were obtained using the RDkit Python package. ${}^{30}$ In the case where an MOF contains multiple types of metal ions or ligands, the descriptor is calculated as an average of each type of metal or ligand weighted by their respective stoichiometry. To more clearly understand the positive and negative correlations between the aforementioned different features, we calculated the feature linear correlation matrix (Supplementary Figure S2).

金属有机框架材料的特征工程。为了获得高精度的机器学习模型，合理描述金属有机框架材料(MOF)构建单元的物理和化学性质至关重要。迄今为止，通常采用三种电荷传输模型来解释MOF的传导机制，包括通过化学键、${}^{{10},{20} - {22}}$ 通过空间、${}^{{11},{23}}$ 以及客体促进的 ${}^{9,{24},{25}}$ 电荷传输。通过化学键机制允许电荷通过金属与连接体之间具有合适空间和能量轨道重叠的配位键进行传输。另一方面，通过空间机制使电荷能够通过电活性分子部分之间的非共价相互作用进行传输，例如参与 $\pi - \pi$ 堆积的那些相互作用。最后，客体促进机制通过主客体氧化还原相互作用促进MOF内部的电荷传输。基于这些知识，我们实现了三个多维向量来描述导电MOF的性质，包括金属描述符、配体描述符和重叠描述符(图1)。金属描述符是一个六维向量，包含电负性 $\left( \chi \right)$ 、化合价、原子半径 $\left( r\right)$ 、软度、硬度和金属中心的配位数(CN)。其中，根据经典的软硬酸碱(HSAB)理论，软度和硬度共同描述了金属离子的极化率和电子接受能力。${}^{{26},{27}}$ 例如，软度较高且硬度较低的金属离子更有可能形成共价相互作用。CN描述了金属 - 配体的连接性，可以通过拓扑分析获得。其他特征是从Mendeleev Python包中获取的。${}^{28}$ 配体描述符也是一个六维向量，包括分子量(MW)、拓扑极性表面积(TPSA)、脂水分配系数(LogP)、氢键供体数量(HBD)、氢键受体数量(HBA)和可旋转键数量(NROTB)。TPSA表示分子中极性原子的总表面积。HBD和HBA分别被确定为可以提供或接受氢键的原子。重叠描述符是一个四维向量，包括酸解离常数 $\left( {\mathrm{p}{K}_{\mathrm{a}}}\right)$ 、最高占据分子轨道(HOMO)的能量、最低未占据分子轨道(LUMO)的能量以及HOMO - LUMO能隙(GAP)。在所有特征中，$\mathrm{p}{K}_{\mathrm{a}}$ 值是从训练好的预测模型 ${}^{29}$ 中获得的；HOMO、LUMO和GAP是通过密度泛函理论(DFT)计算获得的；其他是使用RDkit Python包获得的。${}^{30}$ 在MOF包含多种类型的金属离子或配体的情况下，描述符按每种类型的金属或配体的化学计量加权平均值计算。为了更清楚地理解上述不同特征之间的正相关和负相关，我们计算了特征线性相关矩阵(补充图S2)。

CM Database and QMOF Database. The CM database encompasses 224 MOFs with respective conductivity data gathered from the literature (see SI 2-9 for details). These MOFs feature both 2D and 3D topological forms. As depicted in Figure 2a, the CM database comprises 67 distinct ligands and 33 metal nodes, with the five most common metals being Cu (20.09%), Ni (14.73%), Fe (12.50%), Cd (9.38%), and Zn (8.93%). Because conductivity spans up to 15 orders of magnitude, we used logarithmic values to enhance data stationarity while reducing collinearity and heteroskedasticity in ML models. To establish an unbiased model, we employed a binning algorithm that divides the MOFs into either three categories (low, medium, and high conductivity) or two categories (low and high conductivity), with each category containing roughly equal numbers of samples (see SI 2-9 for details). It is important to note that the “highly conductive MOF” mentioned in this article merely represents the “high”

CM数据库和QMOF数据库。CM数据库包含224种金属有机框架材料，其各自的电导率数据均从文献中收集(详情见补充信息2-9)。这些金属有机框架材料具有二维和三维拓扑形式。如图2a所示，CM数据库包含67种不同的配体和33种金属节点，其中五种最常见的金属分别是铜(20.09%)、镍(14.73%)、铁(12.50%)、镉(9.38%)和锌(8.93%)。由于电导率跨度高达15个数量级，我们使用对数数值来增强数据平稳性，同时减少机器学习模型中的共线性和异方差性。为了建立一个无偏模型，我们采用了一种分箱算法，将金属有机框架材料分为三类(低、中、高电导率)或两类(低和高电导率)，每类包含大致相等数量的样本(详情见补充信息2-9)。需要注意的是，本文中提到的“高导电金属有机框架材料”仅代表“高”

Figure 2. CM and QMOF databases. (A) CM database contains 67 different ligands that are divided into three types according to the ring number (i.e., the degree of conjugation) in the figure. The outermost, middle, and innermost circles correspond to ligands with 0-2, 3-7, and >8 rings. Within each circle, the ligands are sorted by their functional group type. (B) Data distribution of the CM database and the QMOF database in 3-dimensional principal component space. The color bars show how the 2-category conductivity labels and the 3-category conductivity labels are divided. For the two categories, the conductivity threshold is set at ${10}^{-{5.15}}\mathrm{\;S}/\mathrm{{cm}}$ . In the case of the three-category conductivity, two thresholds are established at ${10}^{-{3.34}}$ and ${10}^{-{6.70}}\mathrm{\;S}/\mathrm{{cm}}$ . The aforementioned thresholds are effectuated via the KBinsDiscretizer methodology in Scikit-learn, thereby circumventing issues pertaining to data imbalance.

图2. CM和QMOF数据库。(A) CM数据库包含67种不同的配体，在图中根据环数(即共轭程度)分为三种类型。最外层、中间层和最内层的圆圈分别对应环数为0 - 2、3 - 7和>8的配体。在每个圆圈内，配体按其官能团类型排序。(B) CM数据库和QMOF数据库在三维主成分空间中的数据分布。色条显示了两类电导率标签和三类电导率标签的划分方式。对于两类情况，电导率阈值设置为${10}^{-{5.15}}\mathrm{\;S}/\mathrm{{cm}}$ 。对于三类电导率情况，在${10}^{-{3.34}}$ 和${10}^{-{6.70}}\mathrm{\;S}/\mathrm{{cm}}$ 处设置了两个阈值。上述阈值通过Scikit-learn中的KBinsDiscretizer方法实现，从而避免了与数据不平衡相关的问题。

Figure 3. Performance of ML models. (A) Comparison of the classification performance of eight ML models. (B) Confusion matrix of the ensemble model predicting the results of the test set. (C) Prediction results of the ensemble algorithm on the test set under different noise levels and different training set ratios. (D) Performance of three ML regression models on the test set.

图3. ML模型的性能。(A) 八个ML模型的分类性能比较。(B) 预测测试集结果的集成模型的混淆矩阵。(C) 集成算法在不同噪声水平和不同训练集比例下对测试集的预测结果。(D) 三个ML回归模型在测试集上的性能。

Figure 4. Model interpretability. (A) Feature importance distributions and ratios (calculated based on ensemble model) for the three MOF descriptors. The two most important features in each descriptor are selected for annotation. (B) For the four significant features, the distribution of the CM database is under six categories. The most conductive class of MOFs is represented by purple dots, and the remaining classes are represented by cyan dots. The gray region indicates the aggregations of highly conductive MOFs. $\chi$ , Electronegativity; $r$ , Atomic radius. (C) lrsI and confidence level for different features. Negatively correlated features are represented by cyan bars, and positively correlated features are represented by orange bars.

图4. 模型可解释性。(A) 三种MOF描述符的特征重要性分布和比率(基于集成模型计算)。每个描述符中选择两个最重要的特征进行注释。(B) 对于四个重要特征，CM数据库的分布分为六类。MOFs中导电性最强的类别由紫色点表示，其余类别由青色点表示。灰色区域表示高导电性MOFs的聚集。$\chi$ ，电负性；$r$ ，原子半径。(C) 不同特征的lrsI和置信水平。负相关特征由青色条表示，正相关特征由橙色条表示。

Figure 4. continued

图4. 续

When $\left| {rs}\right| < {0.3}$ , the feature is weakly correlated with conductivity. When $P < {0.05}$ , equivalent to $- \lg P > - \lg \left( {0.05}\right)$ , the result is deemed statistically significant. Irsl, Absolute value of the Spearman correlation coefficient. classification threshold of the ML model, rather than its absolute level of conductivity.

当$\left| {rs}\right| < {0.3}$ 时，该特征与电导率弱相关。当$P < {0.05}$ ，等同于$- \lg P > - \lg \left( {0.05}\right)$ 时，结果被认为具有统计学意义。Irsl，斯皮尔曼相关系数的绝对值。ML模型的分类阈值，而非其电导率的绝对水平。

Upon training the ML models with known conductive MOFs in the CM database, we applied these models to the QMOF database, which contains a large collection of computed MOF electronic structures. To investigate the data distribution of the CM database, we executed principal component analysis (PCA) for dimensionality reduction on both the CM and QMOF databases (Figure 2b). The CM database exhibits broad coverage and even distribution in the three-dimensional PCA space, suggesting that our training data possess a degree of representativeness.

在用CM数据库中已知导电MOFs训练ML模型后，我们将这些模型应用于QMOF数据库，该数据库包含大量计算得到的MOF电子结构。为了研究CM数据库的数据分布，我们对CM和QMOF数据库都执行了主成分分析(PCA)以进行降维(图2b)。CM数据库在三维PCA空间中表现出广泛的覆盖范围和均匀分布，这表明我们的训练数据具有一定程度的代表性。

Model Training and Testing. In this study, we employed an extensive suite of ML models for our analysis. Specifically, for classification problems, we utilized K-Nearest Neighbors ${}^{31}$ (KNN), Support Vector Machines ${}^{32}$ (SVM), Random Forests ${}^{33}$ (RF), eXtreme Gradient Boosing ${}^{34}$ (XGBoost), Light Gradient Boosting Machine ${}^{35}$ (LightGBM), and Category Boosting ${}^{36}$ (CatBoost), all implemented through the scikit-learn Python package. ${}^{37}$ Additionally, we developed an ${\mathrm{{ANN}}}^{38}$ using the PyTorch framework ${}^{39}$ (see SI 2-7 for details). In addition to these individual models, we also implemented an ensemble model that combined predictions from the CatBoost, RF, and ANN models. This ensemble model uses a voting mechanism to determine the final classification, with the class receiving the majority vote being assigned as the output (see SI 2-8 for details). This method leverages the strengths of multiple models to provide more reliable and robust predictions. We selected 21 of 224 conductive MOF datasets as test sets through random stratified sampling, excluding them from model training. For regression problems, we developed three ML algorithms (XGBoost, LightGBM, and ANN) to predict the logarithmic values of conductivity. To reduce overfitting, we designed an ANN architecture suitable for small-sample learning given limited training data. This architecture is “funnel-shaped,” with a decreasing number of neurons in each hidden layer, and the output of each layer is processed through batch normalization and dropout methods (see SI 2-7 for details).

模型训练与测试。在本研究中，我们采用了一系列广泛的机器学习模型进行分析。具体而言，对于分类问题，我们使用了K近邻算法${}^{31}$(KNN)、支持向量机${}^{32}$(SVM)、随机森林${}^{33}$(RF)、极端梯度提升算法${}^{34}$(XGBoost)、轻量级梯度提升机${}^{35}$(LightGBM)和类别提升算法${}^{36}$(CatBoost)，所有这些算法均通过Python的scikit-learn包实现。${}^{37}$此外，我们使用PyTorch框架${}^{39}$开发了一个${\mathrm{{ANN}}}^{38}$(详见补充信息2-7)。除了这些单个模型外，我们还实现了一个集成模型，该模型结合了CatBoost、RF和人工神经网络(ANN)模型的预测结果。这个集成模型使用投票机制来确定最终分类，获得多数票的类别被指定为输出(详见补充信息2-8)。这种方法利用了多个模型的优势，以提供更可靠和稳健的预测。我们通过随机分层抽样从224个导电金属有机框架(MOF)数据集中选择了21个作为测试集，将它们排除在模型训练之外。对于回归问题，我们开发了三种机器学习算法(XGBoost、LightGBM和ANN)来预测电导率的对数值。为了减少过拟合，鉴于训练数据有限，我们设计了一种适合小样本学习的人工神经网络架构。这种架构是“漏斗形”的，每个隐藏层中的神经元数量逐渐减少，并且每层的输出通过批量归一化和随机失活方法进行处理(详见补充信息2-7)。

Figure 3 presents the classification and regression performance of the different ML models. Specifically, Figure 3a highlights the accuracy of eight distinct ML models, among which the ensemble algorithm emerges with the highest accuracy. To analyze the prediction accuracy of the eight models, we calculated the area (AUC) under the receiver operating characteristic (ROC) curves, which illustrate the relationship between sensitivity and specificity. A higher AUC value corresponds to a larger area under the ROC curve and better trade-off between sensitivity and specificity. Analysis of ROC curves of different ML models (Supplementary Figure S1) reveals that the ANN model has the highest AUC value.

图3展示了不同机器学习模型的分类和回归性能。具体而言，图3a突出了八个不同机器学习模型的准确率，其中集成算法的准确率最高。为了分析这八个模型的预测准确率,我们计算了接收器操作特征(ROC)曲线下的面积(AUC)，它说明了敏感性和特异性之间的关系。AUC值越高，ROC曲线下的面积越大，敏感性和特异性之间的权衡越好。对不同机器学习模型的ROC曲线分析(补充图S1)表明，人工神经网络模型的AUC值最高。

Figure 3b showcases the confusion matrix for both binary and ternary classifications using the ensemble model. The binary ensemble model yields an accuracy of 90.48% on the test set whereas the ternary model achieves an 85.71% accuracy. In addition to accuracy, recall and precision are vital evaluation indices for ML classification models. Recall, which focuses on model comprehensiveness, represents the proportion of samples correctly predicted as positive examples by the model among the actual positive examples. The ensemble model has recall values 77.78% (two categories) and 85.71% (three categories) for screening highly conductive MOFs, meaning that this model is capable of identifying most of the highly conductive MOFs (conductivity $> {10}^{-{3.34}}\mathrm{\;S}/\mathrm{{cm}}$ ) in the data set. Meanwhile, implementation of this model to conductive MOF research also requires high precision, which evaluates the fraction of predicted positives that are true positives. As demonstrated in Figure 3b, the ensemble model has precision of 88.89% and 85.71% in screening highly conductive MOFs when two and three categories were investigated, respectively. These values reflect that the model has a high reliability in screening highly conductive MOFs. Additionally, the three-category ensemble algorithm was able to fully capture the chemical commonalities of MOFs with lower conductivity, as indicated by a precision of 100% when applied to screen these materials. These analyses of MOFs with both high and low conductivity demonstrate that the ML model exhibits high accuracy, recall, and precision, which, in turn, indicate the successful learning of high-quality surrogate models.

图3b展示了使用集成模型进行二元和三元分类的混淆矩阵。二元集成模型在测试集上的准确率为90.48%，而三元模型的准确率为85.71%。除了准确率外，召回率和精确率是机器学习分类模型的重要评估指标。召回率关注模型的全面性，它表示模型在实际正例中正确预测为正例的样本比例。集成模型在筛选高导电MOF时的召回率分别为77.78%(两类)和85.71%(三类)，这意味着该模型能够识别数据集中的大多数高导电MOF(电导率$> {10}^{-{3.34}}\mathrm{\;S}/\mathrm{{cm}}$)。同时，将该模型应用于导电MOF研究还需要高精度，高精度评估预测为正例的样本中真正为正例的比例。如图3b所示，在研究两类和三类高导电MOF时，集成模型的精确率分别为88.89%和85.71%。这些值反映了该模型在筛选高导电MOF方面具有很高的可靠性。此外，三类集成算法能够充分捕捉低导电率MOF的化学共性，应用于筛选这些材料时的精确率为100%。对高导电率和低导电率MOF的这些分析表明，机器学习模型具有很高的准确率、召回率和精确率，这反过来表明成功学习到了高质量的替代模型。

Figure 3c presents the performance of the ensemble model under varying noise levels and training data ratios (see SI 2-9 for details). As the noise level increases, the model’s performance on the test set tends to decrease. The accuracies for the two-category and the three-category models remain above 80% with a Gaussian noise level of 50%, reflecting the stability and robust antinoise capability of the model. On the other hand, performance of the model on the test dataset generally improves with an increase in the training data ratio, indicating that the model learns more chemical intuition. However, the changes in the data points are not monotonic, which is a result of the limited sample number and the high degree of randomness of the database.

图3c展示了集成模型在不同噪声水平和训练数据比例下的性能(详见补充信息2-9)。随着噪声水平的增加，模型在测试集上的性能趋于下降。在高斯噪声水平为50%时，两类和三类模型的准确率仍保持在80%以上，这反映了模型的稳定性和强大的抗噪声能力。另一方面，随着训练数据比例的增加，模型在测试数据集上的性能通常会提高，这表明模型学习到了更多的化学直观知识。然而，数据点的变化不是单调的，这是由于样本数量有限和数据库的高度随机性导致的。

Figure 3d delineates the efficacy of the three ML regression models in the test set. We meticulously quantified the ${R}^{2}$ metrics for each algorithm. Moreover, leveraging the bifurcation thresholds of 2-class models, we scrutinized the classification precision of our regression frameworks. Notably, although the classification accuracy modestly trails the benchmarks set in Figure 3b, the principal misclassifications predominantly cluster around the threshold $\left( {{10}^{-{5.15}}\mathrm{\;S}/\mathrm{{cm}}}\right)$ . Remarkably, the ANN model outshines its counterparts in regression prowess, achieving an ${R}^{2}$ value of 0.737, eclipsing conventional boosting methodologies. This observation underscores the robustness and adaptability of our novel model in scenarios characterized by a limited number of data samples.

图3d描绘了三个机器学习回归模型在测试集中的效果。我们精心量化了每种算法的${R}^{2}$指标。此外，利用二分类模型的分叉阈值，我们仔细研究了回归框架的分类精度。值得注意的是，尽管分类准确率略低于图3b中设定的基准，但主要的错误分类主要集中在阈值$\left( {{10}^{-{5.15}}\mathrm{\;S}/\mathrm{{cm}}}\right)$附近。值得注意的是，人工神经网络模型在回归能力方面优于其他模型，其${R}^{2}$值达到0.737，超过了传统的提升方法。这一观察结果强调了我们的新模型在数据样本数量有限的情况下的稳健性和适应性。

Exploring the Conduction Mechanism of MOFs through ML Model. In our quest to unravel the conductive mechanisms in MOFs, we deployed data-driven ML algorithms and established feature importance via the ensemble model. This encompassed the extraction of feature importance directly from the built-in mechanisms of both the CatBoost and Random Forest models. Typically, these mechanisms operate by gauging the total decrease in the model’s prediction error attributable to each feature. Concurrently, for the ANN, we adopted SHAP (SHapley Additive exPlanations) values, a technique based on the game theory that attributes an importance score to each feature for a specific prediction, achieved by assessing the feature’s contribution to the prediction output. Consequently, we computed what we refer to as ‘collective feature importance scores.’ These are the averaged feature importance values derived from multiple models (CatBoost, Random Forest, and ANN), allowing for a more comprehensive and robust evaluation of each feature’s relative importance. Figure 4a displays the average feature contribution computed by the ensemble model. We found that the ligand descriptor importance accounts for the largest proportion (39.28%), whereas HBD in the ligand descriptor and $\mathrm{p}{K}_{\mathrm{a}}$ in the overlap descriptor are the two most significant features, contributing 12.25% and 10.93%, respectively.

通过机器学习模型探索金属有机框架的传导机制。在我们探索金属有机框架传导机制的过程中，我们部署了数据驱动的机器学习算法，并通过集成模型确定了特征重要性。这包括直接从CatBoost和随机森林模型的内置机制中提取特征重要性。通常，这些机制通过衡量每个特征导致的模型预测误差的总减少来运行。同时，对于人工神经网络，我们采用了SHAP(Shapley加法解释)值，这是一种基于博弈论的技术，通过评估特征对预测输出的贡献，为特定预测的每个特征赋予一个重要性分数。因此，我们计算了我们所谓的“集体特征重要性分数”。这些是从多个模型(CatBoost、随机森林和人工神经网络)得出的平均特征重要性值，从而能够更全面、稳健地评估每个特征的相对重要性。图4a展示了集成模型计算出的平均特征贡献。我们发现配体描述符重要性占比最大(39.28%)，而配体描述符中的HBD和重叠描述符中的$\mathrm{p}{K}_{\mathrm{a}}$是两个最重要的特征，分别贡献了12.25%和10.93%。

Figure 5. Experimental verification. The figure illustrates five potential conductive MOFs, identified from the QMOF database employing ML models, with the corresponding ligands and metal nodes showcased. The experimentally determined conductivity of the CuTTPD MOF (10 the regime of the highest conductive MOFs, relatively close to the predicted value ( ${10}^{-{3.30}}\mathrm{\;S}/\mathrm{{cm}}$ ). Additionally, the electronegativities of these metals all reside within the range of 1.6-2.0, in agreement with the aforementioned principles.

图5. 实验验证。该图展示了从QMOF数据库中使用机器学习模型识别出的五个潜在导电金属有机框架，并展示了相应的配体和金属节点。实验测定的CuTTPD金属有机框架的电导率(10处于最高导电金属有机框架的范围内，相对接近预测值(${10}^{-{3.30}}\mathrm{\;S}/\mathrm{{cm}}$)。此外，这些金属的电负性都在1.6 - 2.0范围内，与上述原则一致。

To better understand the relationship between these two features and the metal descriptors, we examined features in the metal descriptor with the largest distributions in the conductive MOFs dataset (Figure 4b). For this purpose, the most conductive MOFs were divided into six groups based on their conductivity to ensure statistical significance. For the group with the highest conductivity (between ${10}^{-{1.63}}$ and ${10}^{0.96}\mathrm{\;S}/\mathrm{{cm}}$ ), the most important contributions come from $\chi$ (with range of 1.6- 1.93,97.4%) and $r$ (with range of 135-140 pm,92.1%) of the metal, as well as $\mathrm{p}{K}_{\mathrm{a}}$ (with range of 2.1-10,63.1%) and HBD of the ligands (with range of 1-6, 92.1%). These distribution rules provide guidelines for the rational design of highly conductive MOFs. For instance, metal nodes with $\chi$ in the range of 1.6- 1.93, including Ni, Cu, Co, Zn, and Al, are advised.

为了更好地理解这两个特征与金属描述符之间的关系，我们研究了导电金属有机框架数据集中分布最广的金属描述符中的特征(图4b)。为此，根据电导率将导电性最强的金属有机框架分为六组，以确保统计显著性。对于电导率最高的组(在${10}^{-{1.63}}$和${10}^{0.96}\mathrm{\;S}/\mathrm{{cm}}$之间)，最重要的贡献来自金属的$\chi$(范围为1.6 - 1.93，占97.4%)和$r$(范围为135 - 140皮米，占92.1%)，以及配体的$\mathrm{p}{K}_{\mathrm{a}}$(范围为2.1 - 10，占63.1%)和HBD(范围为1 - 6，占92.1%)。这些分布规则为高导电金属有机框架的合理设计提供了指导。例如，建议使用$\chi$范围在1.6 - 1.93之间的金属节点，包括镍、铜、钴、锌和铝。

In order to further explore the correlation between each feature and the MOF conductivity, we calculated their Spearman correlation ${}^{40}$ coefficients (rs) and $P$ values (Figure 4c), which are nonparametric correlation measurement methods for evaluating the monotonic relationship between two variables. Perfect correlation is represented by $\left| {rs}\right| = 1$ and $P = 0$ , whereas $\mid \; \mathrm{{rsl}} = 0$ and $P = 1$ represent the absence of correlation. Positive and negative correlations are represented by $\mathrm{{rs}} > 0$ and $\mathrm{{rs}} < 0$ , respectively. In order to maintain robustness and control for spurious correlations, we set the confidence threshold at $P =$ 0.05. A correlation between variables can be considered significant if $P$ is less than this threshold. A correlation is further considered strong if $\left| {1\mathrm{\;s}\mathrm{\;l}}\right| > {0.3}$ .

为了进一步探究各特征与金属有机框架(MOF)电导率之间的相关性，我们计算了它们的斯皮尔曼相关${}^{40}$系数(rs)和$P$值(图4c)，这是用于评估两个变量之间单调关系的非参数相关测量方法。完全相关由$\left| {rs}\right| = 1$和$P = 0$表示，而$\mid \; \mathrm{{rsl}} = 0$和$P = 1$表示不存在相关性。正相关和负相关分别由$\mathrm{{rs}} > 0$和$\mathrm{{rs}} < 0$表示。为了保持稳健性并控制虚假相关性，我们将置信阈值设置为$P =$0.05。如果$P$小于此阈值，则变量之间的相关性可被视为显著。如果$\left| {1\mathrm{\;s}\mathrm{\;l}}\right| > {0.3}$，则相关性进一步被视为强相关。

As shown in Figure 4c, five parameters, including NROTB, $\mathrm{p}{K}_{\mathrm{a}},\mathrm{{HBD}},\mathrm{{HOMO}}$ , and $\chi$ , experience strong correlation (lrs $| >$ 0.3) with the MOF conductivity, with a negative correlation for NROTB and positive correlations for the other four parameters. The correlations observed for NROTB, HOMO, and $\chi$ have already been discussed and rationalized in existing literature on conductive MOFs. Specifically, lower NROTB, corresponding to more rigid and conjugated ligands, allows more stable $\pi - \pi$ stacking and metal-ligand orbital overlap while reducing phonon scattering. ${}^{41}$ Higher HOMO energy for the ligand and larger $\chi$ for the metal allow better ligand-metal energy overlap, which leads to more diffuse bands and, accordingly, higher charge mobility. Additionally, higher HOMO facilitates ligand oxidation during MOF formation, which could lead to higher carrier density in resulting MOFs, for instance, in the case of ligands functionalized with hydroxyl and amino groups. ${}^{{41},{42}}$ In contrast, the influence of parameters such as the ligand $\mathrm{p}{K}_{\mathrm{a}}$ and HBD on the MOF conductivity has rarely been acknowledged in the literature so far. Nevertheless, these correlations can still be rationalized within existing conductive MOF theories. For the ligands investigated here, a larger HBD is characteristic of those functionalized with hydroxyl, amino, thiol, and carboxylic acid groups. Among these functional groups, hydroxyl and amino groups exhibit $\mathrm{p}{K}_{\mathrm{a}}$ values larger than those with carboxylic acid groups. These groups are also highly redox active, giving rise to the highly delocalized semiquinone or iminosemiquinone forms during MOF formation, which leads to higher conductivity. ${}^{{42} - {44}}$ Thus, for the ligands investigated here, HBD and $\mathrm{p}{K}_{\mathrm{a}}$ together reflect the redox activity of the coordination sites. We note that care must be taken when evaluating the redox activity of the ligand since redox properties of the ligand core could mask those of the coordination sites. Altogether, the above evidence shows that our ML models identify descriptors that are directly relevant to the metal-ligand energy overlap.

如图4c所示，包括NROTB、$\mathrm{p}{K}_{\mathrm{a}},\mathrm{{HBD}},\mathrm{{HOMO}}$和$\chi$在内的五个参数与MOF电导率呈现强相关性(|rs|$| >$0.3)，其中NROTB呈负相关，其他四个参数呈正相关。对于NROTB、最高占据分子轨道(HOMO)和$\chi$所观察到的相关性，在关于导电MOF的现有文献中已经进行了讨论并给出了合理解释。具体而言，较低的NROTB对应于更刚性和共轭的配体，允许更稳定的$\pi - \pi$堆积以及金属 - 配体轨道重叠，同时减少声子散射。${}^{41}$配体具有更高的HOMO能量以及金属具有更大的$\chi$，允许更好的配体 - 金属能量重叠，这导致更弥散的能带，因此电荷迁移率更高。此外，更高的HOMO促进MOF形成过程中配体的氧化，这可能导致所得MOF中更高的载流子密度，例如在用羟基和氨基官能化的配体的情况下。${}^{{41},{42}}$相比之下，到目前为止，配体$\mathrm{p}{K}_{\mathrm{a}}$和给体数(HBD)等参数对MOF电导率的影响在文献中很少被提及。然而，这些相关性仍然可以在现有的导电MOF理论中得到合理解释。对于此处研究的配体，较大的HBD是那些用羟基、氨基、硫醇和羧酸基团官能化的配体的特征。在这些官能团中，羟基和氨基的$\mathrm{p}{K}_{\mathrm{a}}$值大于羧酸基团的$\mathrm{p}{K}_{\mathrm{a}}$值。这些基团也是高度氧化还原活性的，在MOF形成过程中产生高度离域的半醌或亚氨基半醌形式，这导致更高的电导率。${}^{{42} - {44}}$因此，对于此处研究的配体，HBD和$\mathrm{p}{K}_{\mathrm{a}}$共同反映了配位位点的氧化还原活性。我们注意到在评估配体的氧化还原活性时必须小心，因为配体核心的氧化还原性质可能掩盖配位位点的氧化还原性质。总之，上述证据表明我们的机器学习模型识别出了与金属 - 配体能量重叠直接相关的描述符。

Experimental Verification. To further validate the reliability of our ML models, we synthesized and characterized one exemplary candidate for a conductive MOF, obtained from searching the QMOF database, with known structure but lacking conductivity data. By employing the trained ensemble models of both 2-category and 3-category to the QMOF library, we first identified five potential candidates, which were then ranked based on their scores of conductivity using the trained ANN model (Figure 5). As a proof of concept, we synthesized the candidate with the highest score, which is composed of ${\mathrm{{Cu}}}^{2 + }$ and 5,5’-(1,3,6,8-tetraoxo-1,3,6,8-tetrahydrobenzo[lmn][3,8]- phenanthroline-2,7-diyl)diisophthalic acid (TTPD). ${}^{45}$ In CuTTPD, copper ions and oxygen atoms on carboxyl function groups from TTPD form a pyramid $\left\lbrack {\mathrm{{CuO}}}_{5}\right\rbrack$ configuration. The chemical bond between two adjacent copper ions endows bimetallic paddlewheel ${\mathrm{{Cu}}}_{2}{\left( \mathrm{{COO}}\right) }_{4}$ secondary building units (SBUs), which themselves connect to each other through oxygen atoms to form 1D Cu-O chains. These chains are further linked through the conjugated naphthalene diimide core of the TTPD ligand, rendering a three-dimensional structure (Supplementary Figure S6). This MOF fulfills several criteria of highly conductive MOFs predicted by our ML models: the electronegativity of copper is 1.90 , and the HBD of TTPD is 4, both of which are within the above-mentioned optimal ranges. Thus, our model predicts CuTTPD to exhibit high conductivity.

实验验证。为了进一步验证我们的机器学习模型的可靠性，我们合成并表征了一种从QMOF数据库中搜索到的具有已知结构但缺乏电导率数据的导电金属有机框架的示例性候选物。通过将训练好的二分类和三分类集成模型应用于QMOF库，我们首先识别出五个潜在候选物，然后使用训练好的人工神经网络模型根据它们的电导率得分进行排名(图5)。作为概念验证，我们合成了得分最高的候选物，它由${\mathrm{{Cu}}}^{2 + }$和5,5’-(1,3,6,8-四氧代-1,3,6,8-四氢苯并[lmn][3,8]-菲咯啉-2,7-二基)二间苯二甲酸(TTPD)组成。${}^{45}$在CuTTPD中，TTPD羧基官能团上的铜离子和氧原子形成金字塔$\left\lbrack {\mathrm{{CuO}}}_{5}\right\rbrack$构型。两个相邻铜离子之间的化学键赋予双金属桨轮${\mathrm{{Cu}}}_{2}{\left( \mathrm{{COO}}\right) }_{4}$二级结构单元(SBUs)，这些二级结构单元本身通过氧原子相互连接形成一维Cu-O链。这些链通过TTPD配体的共轭萘二亚胺核心进一步连接，形成三维结构(补充图S6)。这种金属有机框架满足了我们的机器学习模型预测的高导电金属有机框架的几个标准:铜的电负性为1.90，TTPD的HBD为4，两者都在上述最佳范围内。因此，我们的模型预测CuTTPD具有高电导率。

Remarkably, the experimentally determined conductivity of CuTTPD $\left( {{10}^{-{5.77}}\mathrm{\;S}/\mathrm{{cm}}}\right)$ is relatively proximate to the predicted value $\left( {{10}^{-{3.30}}\mathrm{\;S}/\mathrm{{cm}}}\right)$ . Such conductivity could be rationalized by the structure of this material. The conjugated core of TTPD facilitates charge delocalization, the reductive naphthalene diimide moiety and the oxidative ${\mathrm{{Cu}}}^{2 + }$ enable ligand-to-metal charge transfer, and the Cu-O chains along the crystallographic c-direction promote charge transport thanks to the strong overlap between d orbitals of $\mathrm{{Cu}}$ and $\mathrm{p}$ orbitals of $\mathrm{O}$ (Supplementary Figure S6). We note that our model successfully captures some of these features. For instance, the high electronegativity of $\mathrm{{Cu}}$ gives rise to an efficient energy overlap between metal ions and ligands. Collectively, these findings substantiate the robust performance of our predictive model. Beyond database screening, researchers can apply our ML model proactively in materials design. In this context, ML algorithms evaluate the designed MOFs, enabling informed decisions regarding material synthesis. Overall, our method streamlines the design and development process of 2D electronic materials.

值得注意的是，实验测定的CuTTPD$\left( {{10}^{-{5.77}}\mathrm{\;S}/\mathrm{{cm}}}\right)$ 的电导率相对接近预测值$\left( {{10}^{-{3.30}}\mathrm{\;S}/\mathrm{{cm}}}\right)$ 。这种电导率可以通过这种材料 的结构来解释。TTPD的共轭核心促进了电荷离域，还原性萘二亚胺部分和氧化性${\mathrm{{Cu}}}^{2 + }$ 使得配体到金属的电荷转移成为可能，并且沿着晶体学c方向 的Cu-O链由于$\mathrm{{Cu}}$ 的d轨道与$\mathrm{p}$ $\mathrm{O}$ 的轨道之间的强重叠而促进了电荷传输(补充图S6)。我们注意到我们的模型成功地捕捉到了其中一些特征。例如，$\mathrm{{Cu}}$ 的高电负性导致金属离子和配体之间的有效能量重叠。总的来说，这些发现证实了我们预测模型的强大性能。除了数据库筛选之外，研究人员可以在材料设计中主动应用我们的机器学习模型。在这种情况下，机器学习算法评估设计的金属有机框架，从而能够就材料合成做出明智的决策。总体而言，我们的方法简化了二维电子材料的设计和开发过程。

CONCLUSIONS

结论

In summary, we trained classification and regression models using data on 224 conductive MOFs collected from the literature. By integrating DFT calculations with the trained models for feature engineering, we discovered that the ensemble model performs best in classification, achieving an accuracy of 90.48%, while ANN shows the best performance in regression, with an ${R}^{2}$ value of 0.737 . Further investigations of the structure-property relationships of the conductive MOFs based on the ML models allowed for the prediction of MOFs with potential conductivity within the QMOF database. We envision that our ML model can serve as a supplementary tool for experimental investigations of conductive MOFs, reducing trial and error for experimentalists and expediting scientific discoveries.

总之，我们使用从文献中收集的224种导电金属有机框架(MOF)的数据训练了分类和回归模型。通过将密度泛函理论(DFT)计算与训练好的模型进行特征工程整合，我们发现集成模型在分类中表现最佳，准确率达到90.48%，而人工神经网络(ANN)在回归中表现最佳，${R}^{2}$值为0.737。基于机器学习模型对导电MOF的结构-性质关系进行的进一步研究，使得我们能够在QMOF数据库中预测具有潜在导电性的MOF。我们设想，我们的机器学习模型可以作为导电MOF实验研究的辅助工具，减少实验人员的试错次数并加速科学发现。

BY EXPERIMENTAL SECTION

实验部分

Synthesis of TTPD-Cu-MOF. In a typical synthesis of TTPD-Cu- $\mathrm{{MOF}}$ , a ${20}\mathrm{\;{mL}}$ vial was charged with $\mathrm{{Cu}}{\left( {\mathrm{{NO}}}_{3}\right) }_{2} \cdot 3{\mathrm{H}}_{2}\mathrm{O}\left( {{24.9}\mathrm{{mg}}}\right)$ and TTPD (30.5 mg). To this mixture, DMA (3 mL) and water (1 mL) were added. The mixture was then subjected to ultrasonication for 5 min. Subsequently, the vial was sealed and heated at ${80}{}^{ \circ }\mathrm{C}$ for ${24}\mathrm{\;h}$ . After cooling to room temperature, the resulting green precipitate was collected by filtration, washed three times with DMA, and dried under vacuum. The yield of the green powder was 62% based on TTPD.

TTPD-Cu-MOF的合成。在TTPD-Cu-$\mathrm{{MOF}}$的典型合成中，向一个${20}\mathrm{\;{mL}}$小瓶中加入$\mathrm{{Cu}}{\left( {\mathrm{{NO}}}_{3}\right) }_{2} \cdot 3{\mathrm{H}}_{2}\mathrm{O}\left( {{24.9}\mathrm{{mg}}}\right)$和TTPD(30.5毫克)。向该混合物中加入DMA(3毫升)和水(1毫升)。然后将混合物超声处理5分钟。随后，将小瓶密封并在${80}{}^{ \circ }\mathrm{C}$加热${24}\mathrm{\;h}$。冷却至室温后，通过过滤收集所得绿色沉淀，用DMA洗涤三次，并在真空下干燥。基于TTPD，绿色粉末的产率为62%。

ASSOCIATED CONTENT

相关内容

Data Availability Statement

数据可用性声明

All data related to this study are available from the corresponding author on reasonable request.

与本研究相关的所有数据可在合理请求下从通讯作者处获得。

(c) Supporting Information

(c) 支持信息

The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.chemmater.4c00229.

支持信息可在https://pubs.acs.org/doi/10.1021/acs.chemmater.4c00229免费获取。

Details of the model and experiments (principles of the machine learning model, settings of the model hyper-parameters, DFT calculation methods, experimental methods, and spectroscopic data) (PDF)

模型和实验的详细信息(机器学习模型原理、模型超参数设置、DFT计算方法、实验方法和光谱数据)(PDF)

AUTHOR INFORMATION

作者信息

Corresponding Author

通讯作者

Jin-Hu Dou - School of Materials Science and Engineering, Peking University, Beijing 100871, China; (Corcid.org/0000- 0002-6920-9051;

窦金虎 - 北京大学材料科学与工程学院，北京 100871，中国；(Corcid.org/0000- 0002-6920-9051；邮箱:

Authors

作者

Jinglong Lin - School of Materials Science and Engineering, Peking University, Beijing 100871, China

Huibao Zhang - School of Electronics Engineering and Computer Science, Peking University, Beijing 100871, China

Mojgan Asadi - Department of Chemistry, University of Southern California, Los Angeles, California 90089, United States

Kai Zhao - School of Materials Science and Engineering, Peking University, Beijing 100871, China

Luming Yang - Research Group EPR Spectroscopy, Max Planck Institute for Multidisciplinary Sciences, Göttingen 37077, Germany

Yunlong Fan - School of Materials Science and Engineering, Peking University, Beijing 100871, China

Jintao Zhu - Center for Quantitative Biology, Peking University, Beijing 100871, China

Qianyi Liu - Department of Chemistry, University of Chicago, Chicago, Illinois 60637, United States

Lei Sun - Department of Chemistry and Department of Physics, School of Science, Westlake University, Hangzhou 310030, China; Institute of Natural Sciences, Westlake Institute for Advanced Study, Hangzhou 310024, China; (Corcid.org/ 0000-0001-8467-6750

Wen Jun Xie - Department of Medicinal Chemistry, University of Florida, Gainesville, Florida 32610, United States; © orcid.org/0000-0002-3982-9305

Chenru Duan - Department of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States

Fanyang Mo - School of Materials Science and Engineering, Peking University, Beijing 100871, China; © orcid.org/0000- 0002-4140-3020

Complete contact information is available at:

完整的联系方式如下:

https://pubs.acs.org/10.1021/acs.chemmater.4c00229

https://pubs.acs.org/10.1021/acs.chemmater.4c00229

Author Contributions

作者贡献

J.D. and Y.F. constructed the conductive MOFs (CM) database. J.L. and F.M. performed chemoinformatic and machine learning studies. J.L. and H.Z. analyzed the data. K.Z. performed experiments and measurements. J.Z. and Q.L. performed the DFT calculations. All authors wrote the manuscript. J.D. supervised the whole project.

J.D. 和 Y.F. 构建了导电金属有机框架(CM)数据库。J.L. 和 F.M. 进行了化学信息学和机器学习研究。J.L. 和 H.Z. 分析了数据。K.Z. 进行了实验和测量。J.Z. 和 Q.L. 进行了密度泛函理论(DFT)计算。所有作者撰写了手稿。J.D. 监督了整个项目。

Funding

资金资助

This work was financially supported by the National Key R&D Program of China (grant no. 2023YFE0206400), National Natural Science Foundation of China (grant no. 22171185), and the Clinical Medicine Plus X-Young Scholars Project at Peking University, backed by the Fundamental Research Funds for the Central Universities. The authors extend their deepest gratitude for the invaluable assistance these grants provided.

本研究得到了中国国家重点研发计划(项目编号:2023YFE0206400)、国家自然科学基金(项目编号:22171185)以及北京大学临床医学+X青年学者项目的资助，该项目由中央高校基本科研业务费支持。作者对这些资助所提供的宝贵帮助表示最深切的感谢。

Notes

注释

The authors declare no competing financial interest.

作者声明不存在相互竞争的财务利益。

All ML algorithms and methods, including the experiment details are included in the Supporting Information.

所有机器学习算法和方法，包括实验细节，均包含在补充信息中。

ACKNOWLEDGMENTS

致谢

J.L. acknowledges the invaluable assistance in $\mathrm{p}{K}_{\mathrm{a}}$ prediction provided by Professor S.Z. Luo, Researcher L. Zhang, and Ph.D. student S.Y. Liu from the Department of Chemistry, Tsinghua University.

J.L. 感谢清华大学化学系的罗教授、张研究员和博士生 S.Y. 刘在$\mathrm{p}{K}_{\mathrm{a}}$预测方面提供的宝贵帮助。

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精准导航通路、靶点和疾病

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