Browse
Submit Data
Databases
API
Help

Dataset Information

31 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Machine learning assisted prediction of the Young's modulus of compositionally complex alloys.

ABSTRACT: We identify compositionally complex alloys (CCAs) that offer exceptional mechanical properties for elevated temperature applications by employing machine learning (ML) in conjunction with rapid synthesis and testing of alloys for validation to accelerate alloy design. The advantages of this approach are scalability, rapidity, and reasonably accurate predictions. ML tools were implemented to predict Young's modulus of refractory-based CCAs by employing different ML models. Our results, in conjunction with experimental validation, suggest that average valence electron concentration, the difference in atomic radius, a geometrical parameter λ and melting temperature of the alloys are the key features that determine the Young's modulus of CCAs and refractory-based CCAs. The Gradient Boosting model provided the best predictive capabilities (mean absolute error of 6.15 GPa) among the models studied. Our approach integrates high-quality validation data from experiments, literature data for training machine-learning models, and feature selection based on physical insights. It opens a new avenue to optimize the desired materials property for different engineering applications.

SUBMITTER: Khakurel H

PROVIDER: S-EPMC8387451 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

Predicting the Young's Modulus of Silicate Glasses using High-Throughput Molecular Dynamics Simulations and Machine Learning.

Project description:The application of machine learning to predict materials' properties usually requires a large number of consistent data for training. However, experimental datasets of high quality are not always available or self-consistent. Here, as an alternative route, we combine machine learning with high-throughput molecular dynamics simulations to predict the Young's modulus of silicate glasses. We demonstrate that this combined approach offers good and reliable predictions over the entire compositional domain. By comparing the performances of select machine learning algorithms, we discuss the nature of the balance between accuracy, simplicity, and interpretability in machine learning.

| S-EPMC6584533 | biostudies-literature

Effect of Zr Content on Phase Stability, Deformation Behavior, and Young's Modulus in Ti-Nb-Zr Alloys.

Project description:Ti alloys have attracted continuing research attention as promising biomaterials due to their superior corrosion resistance and biocompatibility and excellent mechanical properties. Metastable ?-type Ti alloys also provide several unique properties such as low Young's modulus, shape memory effect, and superelasticity. Such unique properties are predominantly attributed to the phase stability and reversible martensitic transformation. In this study, the effects of the Nb and Zr contents on phase constitution, transformation temperature, deformation behavior, and Young's modulus were investigated. Ti-Nb and Ti-Nb-Zr alloys over a wide composition range, i.e., Ti-(18-40)Nb, Ti-(15-40)Nb-4Zr, Ti-(16-40)Nb-8Zr, Ti-(15-40)Nb-12Zr, Ti-(12-17)Nb-18Zr, were fabricated and their properties were characterized. The phase boundary between the ? phase and the ?'' martensite phase was clarified. The lower limit content of Nb to suppress the martensitic transformation and to obtain a single ? phase at room temperature decreased with increasing Zr content. The Ti-25Nb, Ti-22Nb-4Zr, Ti-19Nb-8Zr, Ti-17Nb-12Zr and Ti-14Nb-18Zr alloys exhibit the lowest Young's modulus among Ti-Nb-Zr alloys with Zr content of 0, 4, 8, 12, and 18 at.%, respectively. Particularly, the Ti-14Nb-18Zr alloy exhibits a very low Young's modulus less than 40 GPa. Correlation among alloy composition, phase stability, and Young's modulus was discussed.

| S-EPMC7014103 | biostudies-literature

Prediction of Breast Cancer Estrogen Receptor Status using Machine Learning

Project description:Gene expression profiles were generated from 199 primary breast cancer patients. Samples 1-176 were used in another study, GEO Series GSE22820, and form the training data set in this study. Sample numbers 200-222 form a validation set. This data is used to model a machine learning classifier for Estrogen Receptor Status. RNA was isolated from 199 primary breast cancer patients. A machine learning classifier was built to predict ER status using only three gene features.

2013-01-01 | E-GEOD-29210 | biostudies-arrayexpress

Machine learning-based prediction of phases in high-entropy alloys: A data article.

Project description:A systematic framework for choosing the most determinant combination of predictor features and solving the multiclass phase classification problem associated with high-entropy alloy (HEA) was recently proposed [1]. The data associated with that research paper, titled "Machine learning-based prediction of phases in high-entropy alloys", is presented in this data article. This dataset is a systematic documentation and comprehensive survey of experimentally reported HEA microstructures. It contains microstructural phase experimental observations and metallurgy-specific features as introduced and reported in peer-reviewed research articles. The dataset is provided with this article as a supplementary file. Since the dataset was collected from experimental peer-reviewed articles, these data can provide insights into the microstructural characteristics of HEAs, can be used to improve the optimization HEA phases, and have an important role in machine learning, material informatics, as well as in other fields.

| S-EPMC8477136 | biostudies-literature

Unusually high ratio of shear modulus to Young's modulus in a nano-structured gyroid metamaterial.

Project description:Unlike the constant nature of elastic coefficients of isotropic bulk materials, the Young's (E) and shear (μ) moduli of nano-structured (NS) gyroid metamaterials change with relative density (ρ), but at different rates depending on the cell size of the structure. These elastic behaviors displayed by E and μ cause crossover/inversion of these two moduli, such that μ of the NS gyroid metamaterials is greater than E for the structures with ρ < 0.23. This peculiar elastic behavior causes NS gyroid metamaterials to display high μ/E values (~1.0), which are more than 250% larger than the typical values of the bulk material (~0.38), indicating that the NS gyroid metamaterial, even if it is light, is resistant to shear deformation. Here, we report the results of molecular dynamics simulations performed to elucidate the reason for unusually high μ/E values in NS gyroid metamaterials.

| S-EPMC5585398 | biostudies-literature

Machine-Learning-Assisted Accurate Prediction of Molecular Optical Properties upon Aggregation.

Project description:For practical applications, molecules often exist in an aggregate state. Therefore, it is of great value if one can predict the performance of molecules when forming aggregates, for example, aggregation-induced emission (AIE) or aggregation-caused quenching (ACQ). Herein, a database containing AIE/ACQ molecules reported in the literature is first established. Through training, these machine learning (ML) models can build up the structure-property relationship and thus implement fast prediction of AIE/ACQ properties. To this end, a multi-modal approach is proposed, multiple prediction methods are compared and designed, and thus an ensemble strategy is developed. First, multiple molecular descriptors are considered at the same time, major features are extracted by dimensionality reduction, and multi-modal features are synthesized. Then, several state-of-the-art methods are designed and compared to analyze the advantages of the different methods. Finally, the ensemble strategy combines the advantages of the multiple methods to obtain the final prediction result. The reliability of this approach in an unknown molecular space is further verified by three newly designed molecules. Reasonable consistency between model predictions and experimental outcomes is obtained. The result indicates that ML can be a powerful tool to predict molecular properties in the aggregated state, thus accelerating the development of solid-state optical materials.

| S-EPMC8760175 | biostudies-literature

Unsupervised machine learning discovers classes in aluminium alloys.

Project description:Aluminium (Al) alloys are critical to many applications. Although Al alloys have been commercially widespread for over a century, their development has predominantly taken a trial-and-error approach. Furthermore, many discrete studies regarding Al alloys, often application specific, have precluded a broader consolidation of Al alloy classification. Iterative label spreading (ILS), an unsupervised machine learning approach, was used to identify the different classes of Al alloys, drawing from a specifically curated dataset of 1154 Al alloys (including alloy composition and processing conditions). Using ILS, eight classes of Al alloys were identified based on a comprehensive feature set under two descriptors. Further, a decision tree classifier was used to validate the separation of classes.

| S-EPMC9890099 | biostudies-literature

Prediction of Breast Cancer Estrogen Receptor Status using Machine Learning

2013-01-01 | GSE29210 | GEO

Simulation-assisted machine learning.

Project description:MotivationIn a predictive modeling setting, if sufficient details of the system behavior are known, one can build and use a simulation for making predictions. When sufficient system details are not known, one typically turns to machine learning, which builds a black-box model of the system using a large dataset of input sample features and outputs. We consider a setting which is between these two extremes: some details of the system mechanics are known but not enough for creating simulations that can be used to make high quality predictions. In this context we propose using approximate simulations to build a kernel for use in kernelized machine learning methods, such as support vector machines. The results of multiple simulations (under various uncertainty scenarios) are used to compute similarity measures between every pair of samples: sample pairs are given a high similarity score if they behave similarly under a wide range of simulation parameters. These similarity values, rather than the original high dimensional feature data, are used to build the kernel.ResultsWe demonstrate and explore the simulation-based kernel (SimKern) concept using four synthetic complex systems-three biologically inspired models and one network flow optimization model. We show that, when the number of training samples is small compared to the number of features, the SimKern approach dominates over no-prior-knowledge methods. This approach should be applicable in all disciplines where predictive models are sought and informative yet approximate simulations are available.Availability and implementationThe Python SimKern software, the demonstration models (in MATLAB, R), and the datasets are available at https://github.com/davidcraft/SimKern.Supplementary informationSupplementary data are available at Bioinformatics online.

| S-EPMC6792064 | biostudies-literature

Net-Net Auto Machine Learning (AutoML) Prediction of Complex Ecosystems.

Project description:Biological Ecosystem Networks (BENs) are webs of biological species (nodes) establishing trophic relationships (links). Experimental confirmation of all possible links is difficult and generates a huge volume of information. Consequently, computational prediction becomes an important goal. Artificial Neural Networks (ANNs) are Machine Learning (ML) algorithms that may be used to predict BENs, using as input Shannon entropy information measures (Shk) of known ecosystems to train them. However, it is difficult to select a priori which ANN topology will have a higher accuracy. Interestingly, Auto Machine Learning (AutoML) methods focus on the automatic selection of the more efficient ML algorithms for specific problems. In this work, a preliminary study of a new approach to AutoML selection of ANNs is proposed for the prediction of BENs. We call it the Net-Net AutoML approach, because it uses for the first time Shk values of both networks involving BENs (networks to be predicted) and ANN topologies (networks to be tested). Twelve types of classifiers have been tested for the Net-Net model including linear, Bayesian, trees-based methods, multilayer perceptrons and deep neuronal networks. The best Net-Net AutoML model for 338,050 outputs of 10 ANN topologies for links of 69 BENs was obtained with a deep fully connected neuronal network, characterized by a test accuracy of 0.866 and a test AUROC of 0.935. This work paves the way for the application of Net-Net AutoML to other systems or ML algorithms.

| S-EPMC6098100 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data