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基于机器学习的材料设计

河南师范大学学报（自然科学版） 2024年第4期52卷 120-131,F0002页

作者：陆文聪吴炎淼刘太昂卢天纪晓波邢雷凯上海大学理学院化学系上海200444 上海数之微信息科技有限公司上海200444

材料创新一直是推动人类文明进步的重要引擎,随着现代科技对高性能材料日益增长的需求,材料科学的重要性也逐渐凸显.传统的“试错法”和第一性原理应用于复杂的材料设计还有较大的局限性.机器学习已发展成为材料科学研究的新范式,通过... 详细信息

材料创新一直是推动人类文明进步的重要引擎,随着现代科技对高性能材料日益增长的需求,材料科学的重要性也逐渐凸显.传统的“试错法”和第一性原理应用于复杂的材料设计还有较大的局限性.机器学习已发展成为材料科学研究的新范式,通过分析大量数据,可实现高通量筛选、性能预测、晶体结构预测和材料配方优化等方面的功能.机器学习结合第一性原理方法的材料设计,为材料研究带来了崭新的思路.回顾了机器学习在材料设计领域的应用,探讨了其加速材料创新、降低试错成本、定制化材料设计等方面的应用潜力,展望了其对材料科学领域带来的机遇和挑战.

关键词：机器学习材料设计配方优化

儿童清咽解热口服液治疗甲型H1N1流感患儿的临床效果

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中外医学研究 2024年第9期22卷 103-106页

作者：陆文聪广东省妇幼保健院广东广州511442

目的:探讨儿童清咽解热口服液治疗甲型H1N1流感患儿的临床效果及安全性。方法:选取2023年1—3月广东省妇幼保健院收治的91例甲型H1N1流感患儿作为研究对象,采用随机数表法将患儿分为A组(n=45)与B组(n=46)。A组在临床一般治疗基础上使用... 详细信息

目的:探讨儿童清咽解热口服液治疗甲型H1N1流感患儿的临床效果及安全性。方法:选取2023年1—3月广东省妇幼保健院收治的91例甲型H1N1流感患儿作为研究对象,采用随机数表法将患儿分为A组(n=45)与B组(n=46)。A组在临床一般治疗基础上使用奥司他韦颗粒,B组在A组基础上予儿童清咽解热口服液治疗,两组均治疗3~5 d。观察并比较两组疾病愈显率,咽部充血、咽痛和发热症状缓解情况,布洛芬使用情况及不良反应发生情况。结果:治疗后,B组疾病愈显率为93.5%,高于A组的77.8%,差异有统计学意义(P<0.05)。B组治疗后咽部充血、咽部疼痛症状轻于A组,差异有统计学意义(P<0.05)。B组完全退热时间和退热起效时间短于A组,布洛芬日使用剂量和日使用频次少于A组,差异有统计学意义(P<0.05)。两组均未出现不良反应。结论:儿童清咽解热口服液治疗甲型H1N1流感患儿可明显提高疗效,减轻咽部充血、咽部疼痛和发热症状,安全性佳。

关键词：甲型 H1N1 流感儿童清咽解热口服液奥司他韦颗粒临床效果安全性

Machine learning for perovskite materials design and discovery

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npj Computational Materials 2021年第1期7卷 171-188页

作者： Qiuling Tao Pengcheng Xu Minjie Li wencong lu Department of Chemistry College of SciencesShanghai UniversityShanghaiChina Materials Genome Institute Shanghai UniversityShanghaiChina

The development of materials is one of the driving forces to accelerate modern scientific progress and technological *** learning(ML)technology is rapidly developed in many fields and opening blueprints for the discovery and rational design of *** this review,we retrospected the latest applications of ML in assisting perovskites ***,the development tendency of ML in perovskite materials publications in recent years was organized and ***,the workflow of ML in perovskites discovery was *** the applications of ML in various properties of inorganic perovskites,hybrid organic–inorganic perovskites and double perovskites were briefly *** the end,we put forward suggestions on the future development prospects of ML in the field of perovskite materials.

关键词： materials perovskite inorganic

Small data machine learning in materials science

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npj Computational Materials 2023年第1期9卷 1920-1934页

作者： Pengcheng Xu Xiaobo Ji Minjie Li wencong lu Materials Genome Institute Shanghai University200444 ShanghaiChina Department of Chemistry College of SciencesShanghai University200444 ShanghaiChina Zhejiang Laboratory Hangzhou 311100China

This review discussed the dilemma of small data faced by materials machine ***,we analyzed the limitations brought by small ***,the workflow of materials machine learning has been ***,the methods of dealing with small data were introduced,including data extraction from publications,materials database construction,high-throughput computations and experiments from the data source level;modeling algorithms for small data and imbalanced learning from the algorithm level;active learning and transfer learning from the machine learning strategy ***,the future directions for small data machine learning in materials science were proposed.

关键词： database dealing directions

基于机器学习的羰基铁/四氧化三铁复合吸波材料的优化设计

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中国材料进展 2024年第7期43卷 652-657页

作者：仲陆祎权斌车仁超陆文聪上海大学理学院化学系上海200444 复旦大学先进材料实验室上海200433

吸波材料多为复合材料,在抵御电磁干扰和电磁辐射等方面发挥着重要作用,其磁导率与材料对磁能的储存和消耗以及材料的阻抗匹配有关。以羰基铁/四氧化三铁复合吸波材料为研究对象、6个球磨工艺参数为特征变量,分别运用随机森林回归(rando... 详细信息

吸波材料多为复合材料,在抵御电磁干扰和电磁辐射等方面发挥着重要作用,其磁导率与材料对磁能的储存和消耗以及材料的阻抗匹配有关。以羰基铁/四氧化三铁复合吸波材料为研究对象、6个球磨工艺参数为特征变量,分别运用随机森林回归(random forest regression,RFR)算法和支持向量回归(support vector regression,SVR)算法,构建了磁导率实部积分值和虚部积分值的机器学习模型。通过两步高通量筛选,设计了3个兼顾磁导率实部积分值和虚部积分值的虚拟样本,并对其中一个样本进行了实验验证。结果表明,磁导率实部积分值和虚部积分值的相对预测误差分别为3.14%和-6.56%。该研究方法能够挖掘工艺参数和材料吸波性能之间的关系,加快新材料的研发,为运用机器学习优化设计吸波材料提供了思路。

关键词：吸波材料磁导率实部积分值磁导率虚部积分值机器学习高通量筛选

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Machine learning aided design of perovskite oxide materials for photocatalytic water splitting

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Journal of Energy Chemistry 2021年第9期30卷 351-359页

作者： Qiuling Tao Tian lu Ye Sheng Long Li wencong lu Minjie Li Department of Chemistry College of SciencesShanghai UniversityShanghai 200444China Materials Genome Institute Shanghai UniversityShanghai 200444China

Suffering from the inefficient traditional trial-and-error methods and the huge searching space filled by millions of candidates, discovering new perovskite visible photocatalysts with higher hydrogen production rate(RH_(2)) still remains a challenge in the field of photocatalytic water splitting(PWS). Herein, we established structural-property models targeted to RH_(2) and the proper bandgap(Eg) via machine learning(ML) technology to accelerate the discovery of efficient perovskite photocatalysts for PWS. The Pearson correlation coefficients(R) of leave-one-out cross validation(LOOCV) were adopted to compare the performances of different algorithms including gradient boosting regression(GBR), support vector regression(SVR), backpropagation artificial neural network(BPANN), and random forest(RF). It was found that the BPANN model showed the highest R values from LOOCV and testing data of 0.9897 and 0.9740 for RH_(2),while the GBR model had the best values of 0.9290 and 0.9207 for Eg. Furtherly, 14 potential PWS perovskite candidates were screened out from 30,000 ABO3-type perovskite structures under the criteria of structural stability, Eg, conduction band energy, valence band energy and RH_(2). The average RH_(2) of these14 perovskites is 6.4% higher than the highest value in the training data set. Moreover, the online web servers were developed to share our prediction models, which could be accessible in http://***/ocpmdm/material_api/ahfga3d9puqlknig(E_g prediction) and http://***/ocpmdm/material_api/i0 ucuyn3 wsd14940(RH_(2) prediction).

关键词： Perovskite Machine learning Online web service Photocatalytic water splitting Bandgap Hydrogen production rate

Accelerated discovery of boron-dipyrromethene sensitizer for solar cells by integrating data mining and first principle

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Journal of Materiomics 2021年第4期7卷 790-801页

作者： Tian lu Minjie Li Zhenpeng Yao wencong lu Materials Genome Institute Shanghai UniversityShanghai200444China Department of Chemistry College of ScienceShanghai UniversityShanghai200444China Department of Chemistry University of TorontoTorontoOntarioM5S 3H6Canada

Boron-dipyrromethene(BODIPY)is one promising class of sensitizers for dye-sensitized solar cells(DSSCs)due to unique merits of high absorption coefficient and versatile structural modification ***,such derivatives usually suffer from limited power conversion efficiencies(PCEs)because of narrow light absorption band and low electron *** aid the discovery of BODIPY sensitizers,we employ an inverse design method to design efficient sensitizers by integrating data mining and firstprinciple *** establish robust data-mining models using genetic algorithm and multiple linear regression,where the features are filtered from 5515 descriptors and their meanings are explicitly explored for next inverse *** on the features’understanding,we design candidates NH1-6 and predict their PCEs,demonstrating remarkable enhancements(58%maximum)compared to previous ***,their optoelectronic properties including maximum absorption wavelengths,oscillator strengths,bandgaps,transferred charges,charge transferred distances,TiO_(2) conduction band shifts,short-circuit currents and electron injection efficiencies simulated via first-principle calculations indicate significant increasements(93 nm,122.41%,23.70%,36.36%,471.17%,63.64%,28.55%,107.86%maximum),which testifies the corresponding highly predicted PCEs and may overcome BODIPY dyes’*** as-designed BODIPY sensitizers can be promising candidates for DSSCs,and such method could help accelerate the discovery of other energy materials.

关键词： Boron-dipyrromethene sensitizers Quantitative structure-property relationships Optoelectronic properties Electron injection dynamics Dye-sensitized solar cells

Phase diagram of the CsBr-CaBr_2 system

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Rare Metals 2006年第3期25卷 293-296页

作者： BAO Xinhua CHEN Nianyi lu wencong CHENG Zhixuan luO Yunyun lu Weiying XIA Yiben School of Materials Science and Engineering Shanghai University Shanghai 200072 China School of Sciences Shanghai University Shanghai 200444 China

The phase diagram of the CsBr-CaBr2 system was re-determined by using differential thermal analysis and high ternperature and room ternperature X-ray diffraction analysis. It is concluded that there are three intermediate compounds in this system： *** melting compound, CsCaBr3, with a melting point of 823℃ and two incongruently melting compounds, Cs2CaBr4 and Cs3Ca2Br7, whose peritectic points being 597℃ and 635℃, respectively. X-ray diffraction analysis indicated that compound CsCaBr3 is of slightly distorted perovskite structure.

关键词： phase diagram CsBr-CaBr2 system differential thermal analysis (DTA) X-ray diffraction analysis perovskite structure

Active learning for the power factor prediction in diamond-like thermoelectric materials

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npj Computational Materials 2020年第1期6卷 254-260页

作者： Sheng Yasong Wu Jiong Yang wencong lu Pierre Villars Wenqing Zhang Materials Genome Institute Shanghai University200444 ShanghaiChina Qianweichang College Shanghai University200444 ShanghaiChina Department of Chemistry College of ScienceShanghai University200444 ShanghaiChina Material Phases Data System CH-6354 VitznauSwitzerland Department of Physics and Shenzhen Institute for Quantum Science&Technology Southern University of Science and Technology518055 ShenzhenGuangdongChina Guangdong Provincial Key Lab for Computational Science and Material Design and Shenzhen Municipal Key Lab for Advanced Quantum Material and DeviceSouthern University of Science and Technology518055 ShenzhenGuangdongChina

The Materials Genome Initiative requires the crossing of material calculations,machine learning,and experiments to accelerate the material development *** recent years,data-based methods have been applied to the thermoelectric field,mostly on the transport *** this work,we combined data-driven machine learning and first-principles automated calculations into an active learning loop,in order to predict the p-type power factors(PFs)of diamond-like pnictides and *** active learning loop contains two procedures(1)based on a high-throughput theoretical database,machine learning methods are employed to select potential candidates and(2)computational verification is applied to these candidates about their transport *** verification data will be added into the database to improve the extrapolation abilities of the machine learning *** strategies of selecting candidates have been tested,finally the Gradient Boosting Regression model of Query by Committee strategy has the highest extrapolation accuracy(the Pearson R=0.95 on untrained systems).Based on the prediction from the machine learning models,binary pnictides,vacancy,and small atom-containing chalcogenides are predicted to have large *** bonding analysis reveals that the alterations of anionic bonding networks due to small atoms are beneficial to the PFs in these compounds.

关键词： learning bonding prediction