Project description:It has long been a norm that researchers extract knowledge from literature to design materials. However, the avalanche of publications makes the norm challenging to follow. Text mining (TM) is efficient in extracting information from corpora. Still, it cannot discover materials not present in the corpora, hindering its broader applications in exploring novel materials, such as high-entropy alloys (HEAs). Here we introduce a concept of "context similarity" for selecting chemical elements for HEAs, based on TM models that analyze the abstracts of 6.4 million papers. The method captures the similarity of chemical elements in the context used by scientists. It overcomes the limitations of TM and identifies the Cantor and Senkov HEAs. We demonstrate its screening capability for six- and seven-component lightweight HEAs by finding nearly 500 promising alloys out of 2.6 million candidates. The method thus brings an approach to the development of ultrahigh-entropy alloys and multicomponent materials.
Project description:Twinning is a fundamental mechanism behind the simultaneous increase of strength and ductility in medium- and high-entropy alloys, but its operation is not yet well understood, which limits their exploitation. Since many high-entropy alloys showing outstanding mechanical properties are actually thermodynamically unstable at ambient and cryogenic conditions, the observed twinning challenges the existing phenomenological and theoretical plasticity models. Here, we adopt a transparent approach based on effective energy barriers in combination with first-principle calculations to shed light on the origin of twinning in high-entropy alloys. We demonstrate that twinning can be the primary deformation mode in metastable face-centered cubic alloys with a fraction that surpasses the previously established upper limit. The present advance in plasticity of metals opens opportunities for tailoring the mechanical response in engineering materials by optimizing metastable twinning in high-entropy alloys.
Project description:Fatigue failure of metallic structures is of great concern to industrial applications. A material will not be practically useful if it is prone to fatigue failures. To take the advantage of lately emerged high-entropy alloys (HEAs) for designing novel fatigue-resistant alloys, we compiled a fatigue database of HEAs from the literature reported until the beginning of 2022. The database is subdivided into three categories, i.e., low-cycle fatigue (LCF), high-cycle fatigue (HCF), and fatigue crack growth rate (FCGR), which contain 15, 23, and 28 distinct data records, respectively. Each data record in any of three categories is characteristic of a summary, which is comprised of alloy compositions, key fatigue properties, and additional information influential to, or interrelated with, fatigue (e.g., material processing history, phase constitution, grain size, uniaxial tensile properties, and fatigue testing conditions), and an individual dataset, which makes up the original fatigue testing curve. Some representative individual datasets in each category are graphically visualized. The dataset is hosted in an open data repository, Materials Cloud.
Project description:Unconventional wire electrical discharge machining technology (WEDM) is a key machining process, especially for machining newly emerging materials, as there are almost no restrictions (only at least minimal electrical conductivity) in terms of demands on the mechanical properties of the workpiece or the need to develop new tool geometry. This study is the first to present an analysis of the machinability of newly developed high entropy alloys (HEAs), namely FeCoCrMnNi and FeCoCrMnNiC0.2, using WEDM. The aim of this study was to find the optimal setting of machine parameters for the efficient production of parts with the required surface quality without defects. For this reason, an extensive design of experiments consisting of 66 rounds was performed, which took into account the influence of five input factors in the form of pulse off time, gap voltage, discharge current, pulse on time, and wire speed on cutting speed and the quality of the machined surface and its subsurface layer. The analysis of topography, morphology, subsurface layers, chemical composition analysis (EDX), and lamella analysis using a transmission electron microscope (TEM) were performed. An optimal setting of the machine parameters was found, which enables machining of FeCoCrMnNi and FeCoCrMnNiC0.2 with the required surface quality without defects.
Project description:High-entropy alloys (HEAs) are new alloys that contain five or more elements in roughly-equal proportion. We present new experiments and theory on the deformation behavior of HEAs under slow stretching (straining), and observe differences, compared to conventional alloys with fewer elements. For a specific range of temperatures and strain-rates, HEAs deform in a jerky way, with sudden slips that make it difficult to precisely control the deformation. An analytic model explains these slips as avalanches of slipping weak spots and predicts the observed slip statistics, stress-strain curves, and their dependence on temperature, strain-rate, and material composition. The ratio of the weak spots' healing rate to the strain-rate is the main tuning parameter, reminiscent of the Portevin-LeChatellier effect and time-temperature superposition in polymers. Our model predictions agree with the experimental results. The proposed widely-applicable deformation mechanism is useful for deformation control and alloy design.
Project description:To develop an adaptive approach to mine frequent semantic tags (FSTs) from heterogeneous clinical research texts.We develop a "plug-n-play" framework that integrates replaceable unsupervised kernel algorithms with formatting, functional, and utility wrappers for FST mining. Temporal information identification and semantic equivalence detection were two example functional wrappers. We first compared this approach's recall and efficiency for mining FSTs from ClinicalTrials.gov to that of a recently published tag-mining algorithm. Then we assessed this approach's adaptability to two other types of clinical research texts: clinical data requests and clinical trial protocols, by comparing the prevalence trends of FSTs across three texts.Our approach increased the average recall and speed by 12.8% and 47.02% respectively upon the baseline when mining FSTs from ClinicalTrials.gov, and maintained an overlap in relevant FSTs with the base- line ranging between 76.9% and 100% for varying FST frequency thresholds. The FSTs saturated when the data size reached 200 documents. Consistent trends in the prevalence of FST were observed across the three texts as the data size or frequency threshold changed.This paper contributes an adaptive tag-mining framework that is scalable and adaptable without sacrificing its recall. This component-based architectural design can be potentially generalizable to improve the adaptability of other clinical text mining methods.
Project description:High entropy alloys and amorphous metallic alloys represent two distinct classes of advanced alloy materials, each with unique structural characteristics. Their emergence has garnered considerable interest across the materials science and engineering communities, driven by their promising properties, including exceptional strength. However, their extensive compositional diversity poses substantial challenges for systematic exploration, as traditional experimental approaches and high-throughput calculations struggle to efficiently navigate this vast space. While the recent development in data-driven materials discovery could potentially help, such efforts are hindered by the scarcity of comprehensive data and the lack of robust predictive tools that can effectively link alloy composition with specific properties. To address these challenges, we have deployed a machine-learning-based workflow for feature selection and statistical analysis to afford predictive models that accelerate the data-driven discovery and optimization of these advanced materials. Our methodology is validated through two case studies: (i) a regression analysis of the bulk modulus, and (ii) a classification analysis based on glass-forming ability. The Bayesian-optimized regression model trained for the prediction of bulk modulus achieved an R2 of 0.969, an mean absolute error (MAE) of 3.958 GPa, and an root mean square error (RMSE) of 5.411 GPa, while our classification model for predicting glass-forming ability achieved an F1-score of 0.91, an area-under-the-curve of the receiver-operating-characteristic curve of 0.98, and an accuracy of 0.91. Furthermore, by leveraging a wide array of chemical data from diverse literature sources, we have successfully predicted a broad range of properties. This success underscores the efficacy of our modeling approach and emphasizes the importance of a comprehensive feature analysis and judicious feature selection strategy over a mere reliance on complex modeling techniques.
Project description:This data article presents the compilation of mechanical properties for 370 high entropy alloys (HEAs) and complex concentrated alloys (CCAs) reported in the period from 2004 to 2016. The data sheet includes alloy composition, type of microstructures, density, hardness, type of tests to measure the room temperature mechanical properties, yield strength, elongation, ultimate strength and Young׳s modulus. For 27 refractory HEAs (RHEAs), the yield stress and elongation are given as a function of the testing temperature. The data are stored in a database provided in Supplementary materials, and for practical use they are tabulated in the present paper. The database was used in recent publications by Miracle and Senkov [1], Gorsse et al. [2] and Senkov et al. [3].