Accelerating the discovery of low-energy structure configurations: a computational approach that integrates first-principles calculations, Monte Carlo sampling, and Machine Learning

2024-10-08

arXiv.org

AI Summary

The computational cost of identifying the minimum-energy configuration (MEC) in multicomponent alloys escalates dramatically with increasing atomic number, hindering high-throughput alloy design. Method: This work proposes an efficient integrated framework combining first-principles density functional theory (DFT), Monte Carlo sampling, and machine learning (ML). It innovatively couples the cluster expansion (CE) model with local outlier factor (LOF)-based anomaly detection to enhance CE’s physical fidelity and generalizability; further, it introduces a barrier-guided, ML-driven search strategy to jointly optimize accuracy and efficiency. Contribution/Results: Validated on a tungsten-based quaternary high-entropy alloy, the method accelerates MEC discovery by over one order of magnitude. Its modular architecture ensures cross-alloy-system scalability, enabling rational design of high-performance structural materials with reduced computational overhead.

Technology Category

• Machine Learning: Hardware-aware ML
• Search and Optimization: Sampling/Simulation-based Search
• Data Mining & Knowledge Management: Anomaly/Outlier Detection

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Web Mining and Content Analysis: Machine learning and data science for the Web

Abstract

Finding Minimum Energy Configurations (MECs) is essential in fields such as physics, chemistry, and materials science, as they represent the most stable states of the systems. In particular, identifying such MECs in multi-component alloys considered candidate PFMs is key because it determines the most stable arrangement of atoms within the alloy, directly influencing its phase stability, structural integrity, and thermo-mechanical properties. However, since the search space grows exponentially with the number of atoms considered, obtaining such MECs using computationally expensive first-principles DFT calculations often results in a cumbersome task. To escape the above compromise between physical fidelity and computational efficiency, we have developed a novel physics-based data-driven approach that combines Monte Carlo sampling, first-principles DFT calculations, and Machine Learning to accelerate the discovery of MECs in multi-component alloys. More specifically, we have leveraged well-established Cluster Expansion (CE) techniques with Local Outlier Factor models to establish strategies that enhance the reliability of the CE method. In this work, we demonstrated the capabilities of the proposed approach for the particular case of a tungsten-based quaternary high-entropy alloy. However, the method is applicable to other types of alloys and enables a wide range of applications.

Problem

• Minimum Energy Configurations
• Alloy Stability
• Computational Efficiency

Innovation

• High-Entropy Alloys
• Machine Learning Optimization
• Clustering Algorithm