For more details on the methodologies of DeepMoleNet, please read the references cited below.

[1] Ziteng Liu, Liqiang Lin, Qingqing Jia, Zheng Cheng, Yanyan Jiang, Yanwen Guo, Jing Ma, Transferable Multilevel Attention Neural Network for Accurate Prediction of Quantum Chemistry Properties via Multitask Learning. J. Chem. Inf. Model., 2021, 61, 1066-1082.

[2] Ziteng Liu, Yinghuan Shi, Hongwei Chen, Tiexin Qin, Xuejie Zhou, Jun Huo, Hao Dong, Xiao Yang, Xiangdong Zhu, Xuening Chen, Li Zhang, Mingli Yang, Yang Gao, Jing Ma, Machine learning on properties of multiscale multisource hydroxyapatite nanoparticles datasets with different morphologies and sizes. npj Comput. Mater., 2021, 7, 142.

For more details on the methodologies of LSQC, please read the references cited below.

[1] Wei Li, Chihong Chen, Dongbo Zhao, Shuhua Li, LSQC: Low scaling quantum chemistry program. Int. J. Quantum Chem., 2015, 115, 641-646.

[2] Shuhua Li, Wei Li, Yuansheng Jiang, Jing Ma, Tao Fang, Weijie Hua, Shugui Hua, Hao Dong, Dongbo Zhao, Kang Liao, Wentian Zou, Zhigang Ni, Yuqi Wang, Xiaoling Shen, Benkun Hong, LSQC Program, Version 2.4, Nanjing University, Nanjing, 2019, see http://itcc.nju.edu.cn/lsqc.

[3] Wei Li, Shuhua Li, Yuansheng Jiang, Generalized Energy-Based Fragmentation Approach for Computing the Ground-State Energies and Properties of Large Molecules J. Phys. Chem. A., 2007, 111, 2193-2199.

[4] Weijie Hua, Tao Fang, Wei Li, Jian-Guo Yu, Shuhua Li, Geometry Optimizations and Vibrational Spectra of Large Molecules from a Generalized Energy-Based Fragmentation Approach. J. Phys. Chem. A, 2008, 112, 10864–10872.

[5] Shugui Hua, Weijie Hua, Shuhua Li, An Efficient Implementation of the Generalized Energy-Based Fragmentation Approach for General Large Molecules. J. Phys. Chem. A, 2010, 114, 8126–8134.

[6] Shuhua Li, Wei Li, Jing Ma, Generalized Energy-Based Fragmentation Approach and Its Applications to Macromolecules and Molecular Aggregates. Acc. Chem. Res., 2014, 47, 2712–2720.

[7] Shuhua Li, Jing Ma, Yuansheng Jiang, Linear scaling local correlation approach for solving the coupled cluster equations of large systems. J. Comput. Chem., 2002, 23, 237-244.

[8] Shuhua Li, Jun Shen, Wei Li, Yuansheng Jiang, An efficient implementation of the “cluster-in-molecule” approach for local electron correlation calculations. J. Chem. Phys., 2006, 125, 074109.

[9] Wei Li, Piotr Piecuch, Jeffrey R. Gour, Shuhua Li, Local correlation calculations using standard and renormalized coupled-cluster approaches. J. Chem. Phys., 2009, 131, 114109.

[10] Wei Li, Piotr Piecuch, Improved Design of Orbital Domains within the Cluster-in-Molecule Local Correlation Framework: Single-Environment Cluster-in-Molecule Ansatz and Its Application to Local Coupled-Cluster Approach with Singles and Doubles. J. Phys. Chem. A, 2010, 114, 8644–8657.

[11] Wei Li, Zhigang Ni, Shuhua Li, Cluster-in-molecule local correlation method for post-Hartree–Fock calculations of large systems. Mol. Phys., 2016, 114, 1447-1460..

[12] Zhigang Ni, Wei Li, Shuhua Li, Fully optimized implementation of the cluster-in-molecule local correlation approach for electron correlation calculations of large systems. J. Comput. Chem., 2019, 40, 1130-1140.

For more details on the methodologies of MLFFOpt, please read the reference cited below.

[1] Yang Ge, Xueping Wang, Qiang Zhu, Yuqin Yang, Hao Dong, Jing Ma*. Machine Learning-Guided Adaptive Parameterization for Coupling Terms in a Mixed United-atom/Coarse-Grained Model for Diphenylalanine Self-assembly in Aqueous Ionic Liquids. J. Chem. Theory Comput., 2023, 19, 6718–6732.

For more details on the Crystalline Conjugated Polymers Dataset, please read the references cited below.

[1] Kun Fan, Cheng Fu, Yuan Chen, Chenyang Zhang, Guoqun Zhang, Linnan Guan, Minglei Mao, Jing Ma, Wenping Hu, Chengliang Wang*, Framework Dimensional Control Boosting Charge Storage in Conjugated Coordination Polymers. Adv. Sci., 2022, 220576.

[2] Kun Fan#, Jian Li#, Yongshan Xu, Cheng Fu, Yuan Chen, Chenyang Zhang, Guoqun Zhang, Jing Ma, Tianyou Zhai, Chengliang Wang*, Single crystals of a highly conductive three-dimensional conjugated coordination polymer. J. Am. Chem. Soc., 2023, 145, 12682–12690

[3] Jincheng Zou#, Cheng Fu#, Yong Zhang#, Kun Fan, Yuan Chen, Chenyang Zhang, Guoqun Zhang, Huichao Dai, Yueyue Cao, Jing Ma*, Chengliang Wang*, A Novel Hexaazatriphenylene Carboxylate with Compatible Binder as Anode for High-Performance Organic Potassium-Ion Batteries. Adv. Funct. Mater., 2023, 202303678.

Website is http://www.webace-i3c.com/ATTRMaterialDatabase/home/home，NOT https！！！For more details on the HA nanoparticles Database, please read the reference cited below.

[1] Ziteng Liu, Yinghuan Shi, Hongwei Chen, Tiexin Qin, Xuejie Zhou, Jun Huo, Hao Dong, Xiao Yang, Xiangdong Zhu, Xuening Chen, Li Zhang, Mingli Yang, Yang Gao*, Jing Ma*, Machine learning on properties of multiscale multisource hydroxyapatite nanoparticles datasets with different morphologies and sizes. npj Comput. Mater., 2021, 7, 142.

For more details on the Log P Database, please read the references cited below.

[1] Qingqing Jia, Yifan Ni, Ziteng Liu, Xu Gu, Ziyi Cui, Mengting Fan, Qiang Zhu*, Yi Wang*, Jing Ma*, Fast Prediction of Lipophilicity of Organofluorine Molecules: Deep Learning Derived Polarity Characters and Experimental Tests. J. Chem. Inf. Model., 2022, 62, 4928–4936.

[2] Qiang Zhu, Qingqing Jia, Ziteng Liu, Yang Ge, Xu Gu, Ziyi Cui, Mengting Fan, Jing Ma*, Molecular Partition Coefficient from Machine Learning with Polarization and Entropy Embedded Atom-Centered Symmetry Functions. Phys. Chem. Chem. Phys., 2022, 24, 23082.

For more details on the Nanocluster Dataset, please read the reference cited below.

[1] Yuming Gu#, Shisi Tang#, Xu Liu, Xinyi Liang, Qin Zhu, Hongfeng Wu, Xiao Yang, Weihao Jin, Hongwei Chen, Chunyan Liu*, Yan Zhu*, Jing Ma*, Stability Prediction of Gold Nanoclusters with Different Ligands and Doped Metals: Deep Learning and Experimental Tests. J. Mater. Chem. A, 2024, 12, 4460-4472.

For more details on the Viscosity Database, please read the reference cited below.

[1] Qiang Zhu, Yuming Gu, Limu Hu, Théophile Gaudin, Mengting Fan, Jing Ma, Shear viscosity prediction of alcohols, hydrocarbons, halogenated, carbonyl, nitrogen-containing, and sulfur compounds using the variable force fields. J. Chem. Phys., 2021, 154, 074502.

For more details on the Zeolite Activation Dataset, please read the references cited below.

[1] Yuming Gu, Ziteng Liu, Changzhou Yu, Xu Gu, Lili Xu, Yang Gao, Jing Ma, Zeolite Adsorption Isotherms Predicted by Pore Channel and Local Environmental Descriptors: Feature Learning on DFT Binding Strength. J. Phys. Chem. C, 2020, 124, 9314-9328.

[2] Yuming Gu#, Qin Zhu#, Ziteng Liu#, Cheng Fu, Jiayue Wu, Qiang Zhu, Qingqing Jia, Jing Ma*, Nitrogen Reduction Reaction Energy and Pathway in Metal-zeolites: Deep Learning and Explainable Machine Learning with Local Acidity and Hydrogen Bonding Features. J. Mater. Chem. A, 2022, 10, 14976-14988.

[3] Qin Zhu#, Yuming Gu#, Xinyi Liang, Xinzhu Wang, Jing Ma*, A Machine Learning Model To Predict CO2 Reduction Reactivity and Products Transferred from Metal-Zeolites. ACS Catal., 2022, 12, 12336−12348.

[4] Ya-Ting Gu, Yu-Ming Gu*, Qiantu Tao, Xinzhu Wang, Qin Zhu, Jing Ma*, Machine Learning for Prediction of CO2/N2/H2O Selective Adsorption and Separation in Metal-zeolites. J. Mater. Inf., 2023, 3, 19.