Searcharxiv⌕ Search
Home/Archive/arXiv

arXiv · 2003.01943

Machine Learning of Mechanical Properties of Steels

Jie Xiong·Tong-Yi Zhang·San-Qiang Shi
View original source ↗Read source document ↗Export metadata ↓

Abstract

The mechanical properties are essential for structural materials. The analyzed 360 data on four mechanical properties of steels, viz. fatigue strength, tensile strength, fracture strength, and hardness, are selected from the NIMS database, including carbon steels, and low-alloy steels. Five machine learning algorithms were applied on the 360 data to predict the mechanical properties and random forest regression illustrates the best performance. The feature selection was conducted by random forest and symbolic regressions, leading to the four most important features of tempering temperature, and alloying elements of carbon, chromium, and molybdenum to the mechanical properties of steels. Besides, mathematic expressions were generated via symbolic regression, and the expressions explicitly predict how each of the four mechanical properties varies quantitatively with the four most important features. The present work demonstrates the great potential of symbolic regression in the discovery of novel advanced materials.

Explore related subjects

physics.app-phcond-mat.mtrl-sci

Keep this discovery

BibTeXRIS

Jie Xiong, Tong-Yi Zhang, San-Qiang Shi. 2020-03-04. Machine Learning of Mechanical Properties of Steels. https://doi.org/10.1007/s11431-020-1599-5

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Janus Dipoles: Fundamentals, Realizations, and Emerging Applications

The Janus dipole - featuring orthogonally oriented electric and magnetic dipoles with a 90-degree phase difference - has emerged as a powerful paradigm for wave manipulation. Unlike traditional Huygens dipoles used for directional control, this unique configuration exhibits strongly asymmetric, face-selective near-field behavior while maintaining a quasi-isotropic far-field radiation pattern. These remarkable properties make the Janus dipole an essential platform for directional wave shaping, with wide-ranging applications in on-chip photonics, quantum interactions, and wireless power transfer. This review systematically traces the rapid development of the Janus dipole from its foundational theoretical inception to its diverse implementation platforms across optical, microwave, and acoustic frequencies. In this paper, we explore the governing principles, classify realization strategies into passive Janus dipoles, active Janus dipoles, and advanced near-field coupling control, and highlight emerging frontiers. By bridging foundational electrodynamics with advanced device engineering, this paper serves as an essential reference and roadmap for researchers designing next-generation, highly integrated, and compact wave-manipulation systems.

physics.app-ph↗
arXiv2026

Scattering-robust Imaging of Azimuthal Features with Enhanced Resolution

Imaging through scattering media remains a long-standing challenge in numerous real-world applications, ranging from medical imaging to long-distance sensing. Recently, illumination consisting of a single orbital angular momentum (OAM) mode, which is structured in the azimuthal coordinate, has been shown to provide enhanced resolution for imaging objects with azimuthal features, with the resolution becoming maximum at an optimal OAM value. However, in the presence of scattering, single-mode fields, which are spatially fully coherent, cause the imaging resolution to decrease significantly due to speckle formation. In this work, we employ azimuthally partially coherent fields and experimentally demonstrate imaging of azimuthal features with enhanced resolution in the presence of scattering. We show that lower degree of azimuthal coherence in such illumination leads to increased robustness against scattering while the azimuthal structure of the illumination ensures enhanced resolution. We derive the condition for best imaging resolution, and we report increase of imaging contrast in scattering from about 7% to 50% as the illumination is changed from a single-mode fully coherent field to that of an azimuthal partially coherent field.

physics.app-ph↗
arXiv2026

Influence of magnetic fields on the performance of spin-orbit torque magnetic random-access memory

Spin-orbit torque magnetic random-access memory (SOT-MRAM) offers high speed, ultrahigh endurance, and compatibility with advanced semiconductor processes, making it a promising candidate for next-generation nonvolatile memory. However, intrinsic bias fields in magnetic tunnel junctions (MTJs), originating from reference-layer stray fields and interlayer coupling, cause asymmetric critical switching currents and increased energy consumption. Existing compensation approaches usually introduce additional magnetic layers into the MTJ stack, which increases fabrication complexity and limits wafer-scale integration. Here, we propose a bias-compensation strategy without modifying the MTJ stack by engineering local stray magnetic fields through magnetic filling materials in vertical interconnect access (VIA) channels during the back-end-of-line process. Micromagnetic simulations show that the proposed magnetic filling layer can provide the required auxiliary field for deterministic switching and significantly suppress write-current asymmetry. By optimizing the MTJ position relative to the magnetic filling structure, the write-current bias ratio is reduced from 21.6% in the conventional design to 1.3%. The approach is also applicable to in-plane magnetic anisotropy SOT-MTJs, reducing the bias ratio from 19.8% to -0.2%. Scaling analysis further demonstrates that the compensation effect remains effective when the device size is reduced to 20% of the original dimension (MTJ diameter approximately 10 nm), indicating its potential for high-density SOT-MRAM integration.

physics.app-ph↗