Predictive Modeling of High-Entropy Alloys and Amorphous Metallic Alloys Using Machine Learning

Journal of Chemical Information and Modeling, Journal Year: 2024, Volume and Issue: unknown

Published: Oct. 1, 2024

High entropy alloys and amorphous metallic represent two distinct classes of advanced alloy materials, each with unique structural characteristics. Their emergence has garnered considerable interest across the materials science engineering communities, driven by their promising properties, including exceptional strength. However, extensive compositional diversity poses substantial challenges for systematic exploration, as traditional experimental approaches high-throughput calculations struggle to efficiently navigate this vast space. While recent development in data-driven discovery could potentially help, such efforts are hindered scarcity comprehensive data lack robust predictive tools that can effectively link composition specific properties. To address these challenges, we have deployed a machine-learning-based workflow feature selection statistical analysis afford models accelerate optimization materials. Our methodology is validated through case studies: (i) regression bulk modulus, (ii) classification based on glass-forming ability. The Bayesian-optimized model trained prediction modulus achieved an

Language: Английский

Automatic Prediction of Molecular Properties Using Substructure Vector Embeddings within a Feature Selection Workflow

Journal of Chemical Information and Modeling, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 23, 2024

Machine learning (ML) methods provide a pathway to accurately predict molecular properties, leveraging patterns derived from structure–property relationships within materials databases. This approach holds significant importance in drug discovery and design, where the rapid, efficient screening of molecules can accelerate development new pharmaceuticals chemical for highly specialized target application. Unsupervised self-supervised applied graph-based or geometric models have garnered considerable traction. More recently, transformer-based language emerged as powerful tools. Nevertheless, their application entails computational resources, owing need an extensive pretraining process on vast corpus unlabeled data sets. To this end, we present semisupervised strategy that harnesses substructure vector embeddings conjunction with ML-based feature selection workflow various properties. We evaluate efficacy our modeling methodology across diverse range sets, encompassing both regression classification tasks. Our findings demonstrate superior performance compared most existing state-of-the-art algorithms, while offering advantages terms balancing model accuracy requirements. Moreover, provides deeper insights into interactions are essential interpretability. A case study is conducted lipophilicity molecules, exemplifying robustness strategy. The result underscores meticulous analysis over mere reliance predictive high degree algorithmic complexity.

Language: Английский

Integrating machine learning with advanced processing and characterization for polycrystalline materials: a methodology review and application to Iron-based Supercon.Ductors

Science and Technology of Advanced Materials, Journal Year: 2024, Volume and Issue: 26(1)

Published: Dec. 16, 2024

In this review, we present a new set of machine learning-based materials research methodologies for polycrystalline developed through the Core Research Evolutionary Science and Technology project Japan Agency. We focus on constituents (i.e. grains, grain boundaries [GBs], microstructures) summarize their various aspects (experimental synthesis, artificial single GBs, multiscale experimental data acquisition via electron microscopy, formation process modeling, property description 3D reconstruction, data-driven design methods). Specifically, discuss mechanochemical involving high-energy milling, in situ observation microstructural using scanning transmission phase-field modeling coupled with Bayesian assimilation, nano-orientation analysis precession diffraction, semantic segmentation neural network models, Bayesian-optimization-based BOXVIA software. As proof concept, researcher- methodology is applied to iron-based superconductor evaluate its bulk magnet properties. Finally, future challenges prospects development superconductors are discussed.

Language: Английский