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| 1 | 机器学习在深冲钢质量自动判级中的应用显示文摘在流程工业中,生产过程需根据客户对产品质量要求进行判级,以满足客户提出的产品质量需求.目前,企业主要采用“事后”抽检方式,但因无法对所有产品实现在线自动判级,常发生索赔和退货,导致我国钢铁企业每年近100亿元损失.为了实现产品质量在线自动判级,提出基于高维数据非线性同等缩放与核简支集类边界确定相结合的质量在线智能判级方法.首先,将高维的工艺参数通过非线性同等缩放算法变换成低维的数据集,并对缩放后数据集进行聚类,分析工艺参数的类分布特征.然后,根据分类后样本的质量指标值分布,采用核简支集类边界算法来确定不同产品质量级别的类边界.最后,依据已确定的类边界,通过质量指标预测实现产品在线判级.通过深冲钢(IF钢)应用实例,证实该方法在训练阶段的在线自动判级准确率达到97.2%,测试阶段的准确率为96%. | 徐钢 黎敏 徐金梧 | 2022 | 工程科学学报2022，44，6： | 3 |
| 2 | Prediction of mechanical properties for deep drawing steel by deep learning显示文摘At present,iron and steel enterprises mainly use“after spot test ward”to control final product quality.However,it is impossible to realize on-line quality predetermining for all products by this traditional approach,hence claims and returns often occur,resulting in major eco-nomic losses of enterprises.In order to realize the on-line quality predetermining for steel products during manufacturing process,the predic-tion models of mechanical properties based on deep learning have been proposed in this work.First,the mechanical properties of deep drawing steels were predicted by using LSTM(long short team memory),GRU(gated recurrent unit)network,and GPR(Gaussian process regression)model,and prediction accuracy and learning efficiency for different models were also discussed.Then,on-line re-learning methods for transfer learning models and model parameters were proposed.The experimental results show that not only the prediction accuracy of optimized trans-fer learning models has been improved,but also predetermining time was shortened to meet real time requirements of on-line property prede-termining.The industrial production data of interstitial-free(IF)steel was used to demonstrate that R2 value of GRU model in training stage reaches more than 0.99,and R2 value in testing stage is more than 0.96. | Gang Xu Jinshan He Zhimin Lü Min Li Jinwu Xu | 2023 | International Journal of Minerals,Metallurgy and Materials2023，30，1： | 1 |
| 3 | Exploring the configuration space of elemental carbon with empirical and machine learned interatomic potentials显示文摘We demonstrate how the many-body potential energy landscape of carbon can be explored with the nested sampling algorithm,allowing for the calculation of its pressure-temperature phase diagram.We compare four interatomic potential models:Tersoff,EDIP,GAP-20 and its recently updated version,GAP-20U.Our evaluation is focused on their macroscopic properties,melting transitions,and identifying thermodynamically stable solid structures up to at least 100 GPa.The phase diagrams of the GAP models show good agreement with experimental results.However,we find that the models’description of graphite includes thermodynamically stable phases with incorrect layer spacing.By adding a suitable selection of structures to the database and re-training the potential,we have derived an improved model—GAP-20U+gr—that suppresses erroneous local minima in the graphitic energy landscape.At extreme high pressure nested sampling identifies two novel stable structures in the GAP-20 model,however,the stability of these is not confirmed by electronic structure calculations,highlighting routes to further extend the applicability of the GAP models. | George A.Marchant Miguel A.Caro Bora Karasulu Livia B.Pártay | 2023 | npj Computational Materials2023，，1： | 0 |
| 4 | Linear Jacobi-Legendre expansion of the charge density for machine learning-accelerated electronic structure calculations显示文摘Kohn–Sham density functional theory(KS-DFT)is a powerful method to obtain key materials’properties,but the iterative solution of the KS equations is a numerically intensive task,which limits its application to complex systems.To address this issue,machine learning(ML)models can be used as surrogates to find the ground-state charge density and reduce the computational overheads.We develop a grid-centred structural representation,based on Jacobi and Legendre polynomials combined with a linear regression,to accurately learn the converged DFT charge density.This integrates into a ML pipeline that can return any density-dependent observable,including energy and forces,at the quality of a converged DFT calculation,but at a fraction of the computational cost.Fast scanning of energy landscapes and producing starting densities for the DFT self-consistent cycle are among the applications of our scheme. | Bruno Focassio Michelangelo Domina Urvesh Patil Adalberto Fazzio Stefano Sanvito | 2023 | npj Computational Materials2023，，1： | 0 |
| 5 | Generalization of the mixed-space cluster expansion method for arbitrary lattices显示文摘Mixed-space cluster expansion(MSCE),a first-principles method to simultaneously model the configuration-dependent short-ranged chemical and long-ranged strain interactions in alloy thermodynamics,has been successfully applied to binary FCC and BCC alloys.However,the previously reported MSCE method is limited to binary alloys with cubic crystal symmetry on a single sublattice.In the current work,MSCE is generalized to systems with multiple sublattices by formulating compatible reciprocal space interactions and combined with a crystal-symmetry-agnostic algorithm for the calculation of constituent strain energy.This generalized approach is then demonstrated in a hypothetical HCP system and Mg-Zn alloys.The current MSCE can significantly improve the accuracy of the energy parameterization and account for all the fully relaxed structures regardless of lattice distortion.The generalized MSCE method makes it possible to simultaneously analyze the short-and long-ranged configuration-dependent interactions in crystalline materials with arbitrary lattices with the accuracy of typical first-principles methods. | Kang Wang Du Cheng Bi-Cheng Zhou | 2023 | npj Computational Materials2023，，1： | 0 |
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