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学者姓名：孙少瑞
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Abstract ：
The development of selective catalytic reduction (SCR) catalysts is often hindered by the complexity of experimental processes and the time-consuming trial-and-error approaches. Machine learning offers a promising solution by enabling more efficient and data-driven catalyst design. In this study, information was automatically extracted from the SCR-related scientific literature, including catalyst synthesis and catalyst properties, using rule-based techniques. These extracted data were then structured through feature engineering to build a machine learning-ready dataset. Models such as extreme gradient boosting regression (XGBR) and random forest (RF) were employed to predict catalyst performance and identify key factors influencing selectivity and conversion rates. To optimize synthesis routes, the designed synthesizable space was combined with the machine learning models to optimize key parameters and predict synthesis routes for SCR catalysts. Finally, synthesis information for SCR catalysts with high-performance was recommended. This work demonstrates the potential of using machine learning to accelerate SCR catalyst development, providing a scalable method for designing more efficient catalysts.
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| GB/T 7714 | Li, Shuyuan , Huang, Chenyu , Zhang, Yunjiang et al. Machine learning and text mining approaches to design selective catalyst reduction synthesis routes [J]. | CATALYSIS SCIENCE & TECHNOLOGY , 2025 , 15 (4) : 1217-1227 . |
| MLA | Li, Shuyuan et al. "Machine learning and text mining approaches to design selective catalyst reduction synthesis routes" . | CATALYSIS SCIENCE & TECHNOLOGY 15 . 4 (2025) : 1217-1227 . |
| APA | Li, Shuyuan , Huang, Chenyu , Zhang, Yunjiang , Li, Jing , Sun, Shaorui . Machine learning and text mining approaches to design selective catalyst reduction synthesis routes . | CATALYSIS SCIENCE & TECHNOLOGY , 2025 , 15 (4) , 1217-1227 . |
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Abstract ：
In the realm of drug discovery and design, the accurate prediction of protein-ligand binding affinity is of paramount importance as it underpins the functional interactions within biological systems. This study introduces a novel self-supervised learning (SSL) framework that combines contrastive learning and graph neural networks (CL-GNN) for predicting protein-ligand binding affinities, which is a critical aspect of drug discovery. Traditional methods for affinity prediction are expensive and time-consuming, prompting the development of more efficient computational approaches. CL-GNN utilizes a contrastive learning strategy, a form of SSL, to learn from a large data set of 371 458 unique unlabeled protein-ligand complexes. By employing graph neural networks and molecular graph enhancement techniques, the model effectively captures protein-ligand interactions in a self-supervised manner. The fine-tuned model demonstrates competitive performance, achieving high Pearson's correlation coefficients and low root-mean-square errors on benchmark data sets. The proposed method outperforms existing machine learning models, showcasing its potential for accelerating the drug development process. The method effectively quantifies the similarity between protein-ligand complex representations learned in the pretraining and downstream testing phases through cosine similarity assessment. This approach not only revealed potential connections between complexes in their binding properties but also provided new insights into the understanding of drug mechanisms of action. In addition, the transparency of the model is significantly improved by visualizing the importance of key protein residues and ligand atoms. This visualization tool provides insight into the model's predictive decision-making process, providing key biological insights for drug design and optimization.
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| GB/T 7714 | Zhang, Yunjiang , Huang, Chenyu , Wang, Yaxin et al. CL-GNN: Contrastive Learning and Graph Neural Network for Protein-Ligand Binding Affinity Prediction [J]. | JOURNAL OF CHEMICAL INFORMATION AND MODELING , 2025 , 65 (4) : 1724-1735 . |
| MLA | Zhang, Yunjiang et al. "CL-GNN: Contrastive Learning and Graph Neural Network for Protein-Ligand Binding Affinity Prediction" . | JOURNAL OF CHEMICAL INFORMATION AND MODELING 65 . 4 (2025) : 1724-1735 . |
| APA | Zhang, Yunjiang , Huang, Chenyu , Wang, Yaxin , Li, Shuyuan , Sun, Shaorui . CL-GNN: Contrastive Learning and Graph Neural Network for Protein-Ligand Binding Affinity Prediction . | JOURNAL OF CHEMICAL INFORMATION AND MODELING , 2025 , 65 (4) , 1724-1735 . |
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Abstract ：
Poly(ethylene terephthalate) (PET) is a prevalent single-use plastic, posing a significant threat to the environment and human health due to its nondegradable properties. To confront this pressing issue, there is an urgent need for a method of PET degradation that is not only efficient but also cost-effective. Recognizing the intricate interactions of parameters, we propose an innovative approach that integrates two-step machine learning (ML) techniques to achieve our objective while minimizing experimental costs. A dataset comprising 120 distinct PET degradation conditions was created using high-throughput experimentation (HTE). Initially, eight ML algorithms were employed to train and predict performance, with the decision tree model yielding the most favorable outcomes. Subsequently, the trained ML model was expanded to encompass an extensive array of hypothetical reaction conditions, facilitating the identification of degradation formulations that exhibit exceptional performance. Additionally, through the integration of feature importance analysis, we systematically reconstruct a relevant chemical space. Following regression training and prediction, we identified reaction conditions with significantly higher degradation rates at ambient temperature. The utilization of these conditions not only enhances the efficacy of research and development, leading to reduced experimental costs, but also provides valuable insights for future investigations into PET degradation. In contrast to conventional, resource-intensive trial-and-error approaches, our established platform facilitates the assessment of PET degradation rates across diverse reaction conditions, enabling a preliminary screening for process optimization. Consequently, this approach contributes to the mitigation of solid waste pollution and the advancement of sustainable economic development.
Keyword ：
machine learning machine learning poly(ethylene terephthalate) (PET) poly(ethylene terephthalate) (PET) degradation degradation high-throughputexperimentation high-throughputexperimentation
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| GB/T 7714 | Wang, Yaxin , Li, Shuyuan , Meng, Kong et al. Exploring the Ambient-Temperature Degradation Reactions of PET through Two-Step Machine Learning and High-Throughput Experimentation [J]. | ACS SUSTAINABLE CHEMISTRY & ENGINEERING , 2024 , 12 (14) : 5415-5426 . |
| MLA | Wang, Yaxin et al. "Exploring the Ambient-Temperature Degradation Reactions of PET through Two-Step Machine Learning and High-Throughput Experimentation" . | ACS SUSTAINABLE CHEMISTRY & ENGINEERING 12 . 14 (2024) : 5415-5426 . |
| APA | Wang, Yaxin , Li, Shuyuan , Meng, Kong , Zhang, Yunjiang , Fang, Zhaolin , Sun, Shaorui . Exploring the Ambient-Temperature Degradation Reactions of PET through Two-Step Machine Learning and High-Throughput Experimentation . | ACS SUSTAINABLE CHEMISTRY & ENGINEERING , 2024 , 12 (14) , 5415-5426 . |
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Abstract ：
The recycling of discarded polyethylene terephthalate (PET) plastics produced metal-organic frameworks can effectively minimize environmental pollution and promote sustainable economic development. In this study, we developed a method using NaOH in alcohol and ether solvent environments to degrade PET plastics for synthesizing terephthalic acid. The method achieved a 97.5% degradation rate of PET plastics under a reaction temperature of 80 degrees C for 60 min. We used terephthalic acid as a ligand from the degradation products to successfully synthesize two types of monometallic and bimetallic CoZn-MOF materials. We investigated the impact of different metal centers and solvents on the electrochemical performance of the MOF materials. The result showed that the MOF-DMF/H2O material maintained a specific capacity of 1485.5 mAh g(-1) after 100 cycles at a current density of 500 mA g(-1), demonstrating excellent rate capability and cycling stability. In addition, our finding showed that the performance difference might be attributed to the synergistic effect of bimetallic Co2+ and Zn2+ in MOF-DMF/H2O, rapid lithium-ion diffusion and electron transfer rates, and the absence of coordinating solvents. Additionally, the non-in situ X-ray powder diffraction, Fourier transform infrared spectroscopy, and X-ray photoelectron spectroscopy analysis results showed that lithium storage in the MOF-DMF/H2O electrode mainly depended on the aromatic C6 ring and carboxylate portions of the organic ligands in different charge and discharge states. Lithium ions can be reversibly inserted/removed into/from the electrode material. The physical adsorption on the MOF surface through electrostatic interactions enhanced both capacity and cycling stability. This research provides valuable insight for mitigating solid waste pollution, promoting sustainable economic development, and advancing the extensive applications of MOF materials in lithium-ion batteries.
Keyword ：
lithium-ion battery lithium-ion battery lithium storage lithium storage metal-organicframeworks metal-organicframeworks anode materials anode materials polyethyleneterephthalate polyethyleneterephthalate
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| GB/T 7714 | Wang, Yaxin , Meng, Kong , Wang, Huimin et al. Green Synthesis of CoZn-Based Metal-Organic Framework (CoZn-MOF) from Waste Polyethylene Terephthalate Plastic As a High-Performance Anode for Lithium-Ion Battery Applications [J]. | ACS APPLIED MATERIALS & INTERFACES , 2023 , 16 (1) : 819-832 . |
| MLA | Wang, Yaxin et al. "Green Synthesis of CoZn-Based Metal-Organic Framework (CoZn-MOF) from Waste Polyethylene Terephthalate Plastic As a High-Performance Anode for Lithium-Ion Battery Applications" . | ACS APPLIED MATERIALS & INTERFACES 16 . 1 (2023) : 819-832 . |
| APA | Wang, Yaxin , Meng, Kong , Wang, Huimin , Si, Yongheng , Bai, Kun , Sun, Shaorui . Green Synthesis of CoZn-Based Metal-Organic Framework (CoZn-MOF) from Waste Polyethylene Terephthalate Plastic As a High-Performance Anode for Lithium-Ion Battery Applications . | ACS APPLIED MATERIALS & INTERFACES , 2023 , 16 (1) , 819-832 . |
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Abstract ：
Recently, in the field of crystal property prediction, the graph neural network (GNN) model has made rapid progress. The GNN model can effectively capture high-dimensional crystal features from the crystal structure, thereby achieving optimal performance in property prediction. However, the existing GNN model faces limitations in handling the hidden layer after the pooling layer, which restricts the training performance of the model. In the present research, we propose a novel GNN model called the batch normalization multilayer perceptron crystal distance graph neural network (BNM-CDGNN). BNM-CDGNN encodes the crystal's geometry structure only based on the distance vector between atoms. The graph convolutional layer utilizes the radial basis function as the attention mask, ensuring the crystal's rotation invariance and adding the geometric information on the crystal. Subsequently, the average pooling layer is connected after the convolutional layer to enhance the model's ability to learn precise information. BNM-CDGNN connects multiple hidden layers after the average pooling layers, and these layers are processed by the batch normalization layer. Finally, the fully connected layer maps the results to the target property. BNM-CDGNN significantly enhances the accuracy of crystal property prediction compared with previous baseline models such as SchNet, MPNN, CGCNN, MEGNet, and GATGNN.
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| GB/T 7714 | Meng, Kong , Huang, Chenyu , Wang, Yaxin et al. BNM-CDGNN: Batch Normalization Multilayer Perceptron Crystal Distance Graph Neural Network for Excellent-Performance Crystal Property Prediction [J]. | JOURNAL OF CHEMICAL INFORMATION AND MODELING , 2023 , 63 (19) : 6043-6052 . |
| MLA | Meng, Kong et al. "BNM-CDGNN: Batch Normalization Multilayer Perceptron Crystal Distance Graph Neural Network for Excellent-Performance Crystal Property Prediction" . | JOURNAL OF CHEMICAL INFORMATION AND MODELING 63 . 19 (2023) : 6043-6052 . |
| APA | Meng, Kong , Huang, Chenyu , Wang, Yaxin , Zhang, Yunjiang , Li, Shuyuan , Fang, Zhaolin et al. BNM-CDGNN: Batch Normalization Multilayer Perceptron Crystal Distance Graph Neural Network for Excellent-Performance Crystal Property Prediction . | JOURNAL OF CHEMICAL INFORMATION AND MODELING , 2023 , 63 (19) , 6043-6052 . |
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Abstract ：
本发明提供了一种用于甲醇蒸汽重整制氢的Pd/ZnO‑ZrO2催化剂及其制备方法和应用。催化剂以低负载Pd作为活性组分，以ZnO和ZrO2的复合物作为载体，采用共沉淀法合成ZnO和ZrO2的复合载体，再使用湿法浸渍负载Pd。在甲醇蒸汽重整反应中，该种催化剂表现出高活性和高稳定性，尤其是0.1Pd/ZnO‑ZrO2(Pd含量：0.1％)表现出最佳活性，在反应5h后，甲醇转化率达到93.3％，产氢量高达1146.8mol·gPd‑1·h‑1，CO2选择性达到95.7％，CH4几乎为零，并且在5h内保持良好的稳定性，产氢活性几乎未下降。该催化剂贵金属含量低，Pd以单原子形式存在，比常规Pd基催化剂低了30倍，使成本大大降低。
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| GB/T 7714 | 孙少瑞 , 王慧敏 , 王亚鑫 . 一种用于甲醇蒸汽重整制氢的Pd/ZnO-ZrO2催化剂及其制备方法和应用 : CN202310299567.2[P]. | 2023-03-24 . |
| MLA | 孙少瑞 et al. "一种用于甲醇蒸汽重整制氢的Pd/ZnO-ZrO2催化剂及其制备方法和应用" : CN202310299567.2. | 2023-03-24 . |
| APA | 孙少瑞 , 王慧敏 , 王亚鑫 . 一种用于甲醇蒸汽重整制氢的Pd/ZnO-ZrO2催化剂及其制备方法和应用 : CN202310299567.2. | 2023-03-24 . |
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Abstract ：
The structured material synthesis route is crucial for chemists in performing experiments and modern applications such as machine learning material design. With the exponential growth of the chemical literature in recent years, manual extraction from the published literature is time-consuming and labor-intensive. This study focuses on developing an automated method for extracting Pd-based catalyst synthesis routes from the chemical literature. First, a paragraph classification model based on regular expressions is employed to identify paragraphs that contain material synthesis processes. The identified paragraphs are verified using machine learning techniques. Second, natural language processing techniques are applied to automatically parse the material synthesis routes from the identified paragraphs, generate regularized flowcharts, and output structured data. Lastly, we utilized the structured data of the synthesis routes to train machine learning models and predict the performance of the materials. The extracted material entities include the product, preparation method, precursor, support, loading, synthesis operation, and operation condition. This method avoids extensive manual data annotation and improves the scientific literature information acquisition efficiency. The accuracy of the 11 material entities exceeds 80%, and the accuracy of the method, support, precursor, drying time, and reduction time exceeds 90%.
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| GB/T 7714 | Li, Shuyuan , Zhang, Yunjiang , Fang, Zhaolin et al. Extracting the Synthetic Route of Pd-Based Catalysts in Methanol Steam Reforming from the Scientific Literature [J]. | JOURNAL OF CHEMICAL INFORMATION AND MODELING , 2023 . |
| MLA | Li, Shuyuan et al. "Extracting the Synthetic Route of Pd-Based Catalysts in Methanol Steam Reforming from the Scientific Literature" . | JOURNAL OF CHEMICAL INFORMATION AND MODELING (2023) . |
| APA | Li, Shuyuan , Zhang, Yunjiang , Fang, Zhaolin , Meng, Kong , Tian, Rui , He, Hong et al. Extracting the Synthetic Route of Pd-Based Catalysts in Methanol Steam Reforming from the Scientific Literature . | JOURNAL OF CHEMICAL INFORMATION AND MODELING , 2023 . |
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Abstract ：
Cu-based alloy catalysts are widely used in the field of carbon dioxide reduction reaction (CO2RR), due to the good selectivity and low overpotential. In order to achieve efficient exploration of alloy catalysts for CO2RR, a machine learning (ML) model, based on a gradient boosting regression (GBR) algorithm, is developed. By implementing a rigorous feature selection process, the dimensionality of feature space is reduced from thirteen to five, including work function (W), local electronegativity (Loc_EN), electronegativity (EN), interplanar spacing (D), and atomic number (Z), which is referred to as the WLEDZ model. The few-feature model has a high performance as that with many features, and the ML model successfully and rapidly predicts the adsorption energy of the key intermediates (HCOO, CO, and COOH) in the CO2RR process. In addition, eight Cu-based bimetallic catalysts are predicted with highly promising alternatives. This demonstrates that the WLEDZ few-feature ML model can screen highly promising bimetallic alloy for CO2RR and can also be used for the design of other types of catalysts.
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| GB/T 7714 | Xing, Miaojuan , Zhang, Yunjiang , Li, Shuyuan et al. Prediction of Carbon Dioxide Reduction Catalyst Using Machine Learning with a Few-Feature Model: WLEDZ [J]. | JOURNAL OF PHYSICAL CHEMISTRY C , 2022 . |
| MLA | Xing, Miaojuan et al. "Prediction of Carbon Dioxide Reduction Catalyst Using Machine Learning with a Few-Feature Model: WLEDZ" . | JOURNAL OF PHYSICAL CHEMISTRY C (2022) . |
| APA | Xing, Miaojuan , Zhang, Yunjiang , Li, Shuyuan , He, Hong , Sun, Shaorui . Prediction of Carbon Dioxide Reduction Catalyst Using Machine Learning with a Few-Feature Model: WLEDZ . | JOURNAL OF PHYSICAL CHEMISTRY C , 2022 . |
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Abstract ：
Recycling waste PET plastics into metal-organic frameworks is conducive to both pollution alleviation and sustainable economic development. Herein, we have utilized waste PET plastic to synthesize CoNi-MOF applied to lithium battery anode materials via a low-temperature solvothermal method for the first time. The preparation process is effortless, and the sources' conversion rate can reach almost 100%. In addition, the anode performance of MOFs with various Co/Ni mole ratios was investigated. The as-synthesized Co0.8Ni-MOF exhibits excellent crystallinity, purity, and electrochemical performance. The initial discharge and charge capacities are 2496 and 1729 mAh g(-1), respectively. Even after 200 cycles, the Co0.8Ni-MOF electrode can exhibit a high Coulombic efficiency of over 99%. Consequently, given the environmental and economic benefits, the Co0.8Ni-MOF derived from waste PET plastic is thought to be an appealing anode material for lithium-ion batteries.
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| GB/T 7714 | Wang, Yaxin , Wang, Huimin , Li, Shuyuan et al. Waste PET Plastic-Derived CoNi-Based Metal-Organic Framework as an Anode for Lithium-Ion Batteries [J]. | ACS OMEGA , 2022 . |
| MLA | Wang, Yaxin et al. "Waste PET Plastic-Derived CoNi-Based Metal-Organic Framework as an Anode for Lithium-Ion Batteries" . | ACS OMEGA (2022) . |
| APA | Wang, Yaxin , Wang, Huimin , Li, Shuyuan , Sun, Shaorui . Waste PET Plastic-Derived CoNi-Based Metal-Organic Framework as an Anode for Lithium-Ion Batteries . | ACS OMEGA , 2022 . |
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Abstract ：
一种针对不同靶点蛋白进行药物设计的通用性方法属于计算机人工智能与新药设计领域，包括训练分子生成模型、训练药物靶标亲和力预测模型，以亲和力模型输出作为奖励函数，通过强化学习使分子生成神经网络生成的分子与蛋白质有更好的亲和力。将某个靶点蛋白的氨基酸序列输入到已经训练好的药物设计机器学习模型中，得到针对这个靶点蛋白的小分子抑制剂化合物。
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| GB/T 7714 | 孙少瑞 , 张云江 , 何洪 . 一种针对不同靶点蛋白进行药物设计的通用性方法 : CN202210051113.9[P]. | 2022-01-17 . |
| MLA | 孙少瑞 et al. "一种针对不同靶点蛋白进行药物设计的通用性方法" : CN202210051113.9. | 2022-01-17 . |
| APA | 孙少瑞 , 张云江 , 何洪 . 一种针对不同靶点蛋白进行药物设计的通用性方法 : CN202210051113.9. | 2022-01-17 . |
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