We analyze Curie temperatures of rare-earth transition metal binary alloys with machine learning method. In order to select important descriptors and descriptor groups, we introduce newly developed subgroup relevance analysis and adopt the hierarchical clustering in the representation. We execute the exhaustive search and successfully illustrate the importance of descriptors and descriptor groups. We execute the exhaustive search and illustrate that our approach indeed leads to the successful selection of important descriptors and descriptor groups. It helps us to choose the combination of the descriptors and to understand the meaning of the selected combination of descriptors.
Robust spin Hall effects (SHE) have recently been observed in non-magnetic heavy metal systems with strong spin-orbit interactions. These SHE are either attributed to an intrinsic band-structure effect or to extrinsic spin-dependent scattering from impurities, namely side-jump or skew scattering. Here we report on an extraordinarily strong spin Hall effect, attributable to spin fluctuations, in ferromagnetic FexPt1-x alloys near their Curie point, tunable with x. This results in a damping-like spin-orbit torque being exerted on an adjacent ferromagnetic layer that is strongly temperature depen…
Stacking fault energy (SFE) is an essential parameter for characterizing mechanical properties. However, in high entropy alloys (HEAs), the local chemical environment varies significantly across different stacking fault planes, resulting in a substantial fluctuation of SFE values rather than a unique value, which prohibits the prediction of the local SFE. Herein, we proposed an effective descriptor based on the local concentration ratio near stacking fault to quantitatively predict the local SFE of refractory HEAs. We find that the role of a given element in determining SFE strongly depends on…
Exchange interactions and Curie temperatures in Cr-based alloys in Zinc Blende structure: volume- and composition-dependence from first-principles calculations
本文计算了Cr基磷属元素化物和硫属元素化物CrX（X=As，Sb，S，Se和Te）以及混合合金CrAs $_{50}$ X $_{50}$（X=Sb，S，Se和Te）的交换作用和居里温度。计算了Zn Blende（ZB）结构的12个晶格参数值（5.44 ~ 6.62 μ m），适用于一些典型的II-VI族和III-V族半导体衬底。电子结构是通过原子球近似（ASA）中的线性松饼-锡-轨道（LMTO）方法计算的，使用空球来优化ASA相关误差。必要时，使用该方法的全电位版本FP-LMTO对结果进行验证。采用相干势近似（CPA）方法研究了CrAs $_{50}$ X $_{50}$（X=Sb，S，Se，Te）合金中As亚晶格的无序效应.交换相互作用的合金的铁磁（FM）的参考状态，以及无序的本地矩（DLM）状态使用线性响应方法计算。然后，这些结果被用来估计居里温度从低和高温侧的铁磁/顺磁转变。居里温度的估计提供，平均场和更准确的随机相位近似的基础上。占主导地位的反铁磁交换相互作用的一些低的值的晶格参数的FM参考状态CrS，CrSe和CrTe促使我们寻找这些系统的反铁磁（AFM）配置的能量低于相应的FM和DLM值。有限数量的AFM计算的结果进行了讨论，确定AFM[111]状态作为这些情况下的基态的可能候选者。
Predicting the Curie Temperature of Magnetic Materials with Machine Learning: Descriptor Engineering, Graph Neural Networks, and the Role of Curated Data
Isfahan University of Technology（伊斯法罕理工大学）；Skolkovo Institute of Science and Technology（斯科尔科沃科学技术研究所）
We study magnon properties in terms of spin stiffness, Curie temperatures and magnon spectrum of Fe-Ni, Co-Ni and Fe-Co random alloys using a combination of electronic structure calculations and atomistic spin dynamics simulations. Influence of the disorder are studied in detail by use of large supercells with random atomic arrangement. It is found that disorder affects the magnon spectrum in vastly different ways depending on the system. Specifically, it is more pronounced in Fe-Ni alloys compared to Fe-Co alloys. In particular, the magnon spectrum at room temperature in Permalloy (Fe$_{20}$N…
The dynamics of magnetic moments in iron-chromium alloys with different levels of Cr clustering show unusual features resulting from the fact that even in a perfect body-centred cubic structure, magnetic moments experience geometric magnetic frustration resembling that of a spin glass. Due to the long range exchange coupling and configuration randomness, magnetic moments of Cr solutes remain non-collinear at all temperatures. To characterise magnetic properties of Fe-Cr alloys, we explore the temperature dependence of magnetisation, susceptibility, Curie temperature and spin-spin correlations …
Magnetic materials have a plethora of applications ranging from informatics to energy harvesting and conversion. However, such functionalities are limited by the magnetic ordering temperature. In this work, we performed machine learning on the magnetic ground state and the Curie temperature (TC), using generic chemical and crystal structural descriptors. Based on a database of 2805 known intermetallic compounds, a random forest model is trained to classify ferromagnetic and antiferromagnetic compounds and to do regression on the TC for the ferromagnets. The resulting accuracy is about 86% for …
We investigate the exchange coupling constant $J_{ij}$ in Nd$_x$Fe$_{1-x}$ amorphous alloys with different compositions within the framework of first-principles calculation. We observed a strong atomic-dependence of $J_{ij}$ and its fluctuations. We show that the composition strongly affects the distance dependence of $J_{ij}$. Composition dependence of calculated Curie temperatures is modest for $x<0.5$. To examine the effect of the local environment on the exchange couplings, we demonstrate combined analyses of the coordination structure and exchange coupling constants using the Gabriel grap…
Future spintronics requires the realization of thin film of half-metallic ferromagnets having high Curie temperature and 100\% spin polarization at the Fermi level for potential spintronics applications. In this paper, we report the epitaxial thin films growth of half-metallic CoFeMnGe Heusler alloy on MgO (001) substrate using pulsed laser deposition system, along with the study of structural, magnetic and transport properties. The magnetic property measurements of the thin film suggest a soft ferromagnetic state at room temperature with an in-plane magnetic anisotropy and a Curie temperature…
We study the exchange interactions in half-metallic Heusler alloys using first-principles calculations in conjunction with the frozen-magnon approximation. The Curie temperature is estimated within both mean-field (MF) and random-phase-approximation (RPA) approaches. For the half-Heusler alloys NiMnSb and CoMnSb the dominant interaction is between the nearest Mn atoms. In this case the MF and RPA estimations differ strongly. The RPA approach provides better agreement with experiment. The exchange interactions are more complex in the case of full-Heusler alloys Co$_2$MnSi and Co$_2$CrAl where t…
Developing data-driven machine-learning interatomic potentials for materials containing many elements becomes increasingly challenging due to the vast configuration space that must be sampled by the training data. We study the learning rates and achievable accuracy of machine-learning interatomic potentials for many-element alloys with different combinations of descriptors for the local atomic environments. We show that for a five-element alloy system, potentials using simple low-dimensional descriptors can reach meV/atom-accuracy with modestly sized training datasets, significantly outperform…
An accurate prediction of atomic diffusion in Fe alloys is challenging due to thermal magnetic excitations and magnetic transitions. We propose an effcient approach to address these properties via Monte Carlo simulation, using ab-initio based effective interaction models. The temperature evolution of self- and Cu diffusion coeffcients in alpha-iron are successfully predicted, particularly the diffusion acceleration around the Curie point, which requires a quantum treatment of spins. We point out a dominance of magnetic disorder over chemical effects on diffusion in the very dilute systems.
We combined descriptor-based analytical models for stiffness-matrix and elastic-moduli with mean-field methods to accelerate assessment of technologically useful properties of high-entropy alloys, such as strength and ductility. Model training for elastic properties uses Sure-Independence Screening (SIS) and Sparsifying Operator (SO) method yielding an optimal analytical model, constructed with meaningful atomic features to predict target properties. Computationally inexpensive analytical descriptors were trained using a database of the elastic properties determined from density functional the…
We formulate the maximum driving force (MDF) parameter as a descriptor to capture the thermodynamic stability of aqueous surface scale creation over a range of environmental conditions. We use formation free energies, $Δ_f G$s, sourced from high-throughput density functional theory (DFT) calculations and experimental databases to compute the maximum driving force for a wide variety of materials, including simple oxides, intermetallics, and alloys of varying compositions. We show how to use the MDF to describe trends in aqueous corrosion of nickel thin films determined from experimental linear-…
We report a combined theoretical and experimental investigation of half-metallic ferromagnetism in equiatomic quaternary Heusler alloy CoRuMnSi. Room temperature XRD analysis reveals that the alloy crystallizes in L21 disorder instead of pristine Y-type structure due to 50% swap disorder between the tetrahedral sites, i.e., Co and Ru atoms. Magnetization measurements reveal a net magnetization of 4 $μ_B$ with Curie temperature of ~780 K. Resistivity measurement reveals the presence of localization effect below 35 K while above 100 K, a linear dependence is observed. Resistivity behavior indica…
Exchange splitting as a descriptor for giant anomalous Hall and Nernst effects in ferromagnets
National Institute for Materials Science (NIMS)（物质材料研究机构）；Zhejiang Key Laboratory of Magnetic Materials and Applications, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences（中国科学院宁波材料技术与工程研究所磁材料与应用浙江省重点实验室）；Kyoto Institute of Technology（京都工艺纤维大学）
Lowering the Exponential Wall: Accelerating High-Entropy Alloy Catalysts Screening using Local Surface Energy Descriptors from Neural Network Potentials
Japan Advanced Institute of Science and Technology（日本先进科学技术大学）；ESICMM, NIMS（日本国立材料科学研究所ESICMM）；HPC Systems Inc.（HPC系统公司）；Center for Materials Research by Information Integration, National Institute for Materials Science（日本国立材料科学研究所信息集成材料研究中心）；CD-FMat, AIST（日本产业技术综合研究所CD-FMat）；JST, PRESTO（日本科学技术振兴机构PRESTO）
We propose a data-driven method to extract dissimilarity between materials, with respect to a given target physical property. The technique is based on an ensemble method with Kernel ridge regression as the predicting model; multiple random subset sampling of the materials is done to generate prediction models and the corresponding contributions of the reference training materials in detail. The distribution of the predicted values for each material can be approximated by a Gaussian mixture model. The reference training materials contributed to the prediction model that accurately predicts the…
Oxidation and tarnishing are the two most common initial steps in the corrosive process of metals at ambient conditions. These are always initiated with O and S binding to a metallic surface, so that one can use the binding energy as a rough proxy for the metal reactivity. With this in mind, we present a systematic study of the binding energy of O and S across the entire transition-metals composition space, namely we explore the binding energy of {\bf 88} single-phase transition metals and of {\bf 646} transition-metal binary alloys. The analysis is performed by defining a suitable descriptor …
