Revealing the crucial role of rough energy landscape on self-diffusion in high-entropy alloys based on machine learning and kinetic Monte Carlo

Xu, Biao; Zhang, Jun; Ma, Shunchao; Xiong, Yaoxu; Huang, Shasha; Kai, Ji‐Jung; Zhao, Shijun

doi:10.1016/j.actamat.2022.118051

Cited by 29 publications

(2 citation statements)

References 46 publications

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“…Now, we focus on ML approaches for exploring the energy and enthalpy landscape of materials, which has gained increased popularity in the recent times [7,103–105] . ML models generally rely on different structural descriptors to act as the features for the model.…”

Section: Modeling Approachesmentioning

confidence: 99%

“…Specifically, we explore three families of algorithms that enable the exploration of energy and enthalpy landscapes, namely classical, metaheuristic, and machine learning (ML) approaches. The first includes the classical optimization techniques, such as simulated annealing, [3,4] basin‐hoping, [5,6] and activation‐relaxation technique (ART) [7–9] . The second group is the evolutionary approaches, which include genetic algorithm (GA), [10–13] particle swarm optimization (PSO), [14–16] Bayesian optimization (BO), and ant colony optimization (ACO).…”

Section: Introductionmentioning

confidence: 99%

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Navigating energy landscapes for materials discovery: Integrating modeling, simulation, and machine learning

Mannan,

Bihani,

Krishnan

et al. 2024

Materials Genome Engineering Advances

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The energy landscape represents a high‐dimensional mapping of the configurational states of an atomic system with their respective energies. Under isobaric conditions, enthalpy landscapes can be used to account for volumetric changes of the system. Understanding the energy or enthalpy landscape holds the key for discovering materials with targeted properties, since the landscape encapsulates the complete thermodynamic and kinetic behavior of a system, including relaxation, metastable phases, and reactivity. However, the curse of dimensionality prohibits one from enumerating and visualizing the energy landscape—the energy landscape of an N‐atom system has 3N dimensions. Here, we outline the emerging computational techniques that allow the exploration of complex energy landscapes of materials in three distinct categories: the classical, metaheuristic, and machine learning approaches. We discuss the advantages and disadvantages associated with each of these methods, with a focus on the nature of problems where they can provide excellent solutions (and vice versa). Altogether, in addition to giving an overview of existing approaches, we hope the review provides an impetus to develop novel methods to explore the energy landscapes that can, in turn, provide both a fundamental understanding of the physics of materials and accelerate the discovery of novel materials.

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Section: Modeling Approachesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Navigating energy landscapes for materials discovery: Integrating modeling, simulation, and machine learning

Mannan,

Bihani,

Krishnan

et al. 2024

Materials Genome Engineering Advances

View full text Add to dashboard Cite

show abstract

Machine Learning Paves the Way for High Entropy Compounds Exploration: Challenges, Progress, and Outlook

Wan,

Li,

et al. 2023

Advanced Materials

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Machine learning (ML) has emerged as a powerful tool in the research field of high entropy compounds (HEC), which have gained worldwide attention due to their vast compositional space and abundant regulatability. However, the complex structure space of HEC poses challenges to traditional experimental and computational approaches, necessitating the adoption of machine learning. Microscopically, machine learning can model the Hamiltonian of the HEC system, enabling atomic‐level property investigations, while macroscopically, it can analyze macroscopic material characteristics such as hardness, melting point, and ductility. Various machine learning algorithms, both traditional methods and deep neural networks can be employed in HEC research. Comprehensive and accurate data collection, feature engineering, and model training and selection through cross‐validation are crucial for establishing excellent ML models. ML also holds promise in analyzing phase structures and stability, constructing potentials in simulations, and facilitating the design of functional materials. Although some domains, such as magnetic and device materials, still require further exploration, machine learning's potential in HEC research is substantial. Consequently, machine learning has become an indispensable tool in understanding and exploiting the capabilities of HEC, serving as the foundation for the new paradigm of artificial intelligence‐assisted material exploration.This article is protected by copyright. All rights reserved

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Accelerating the Exploration of High‐Entropy Alloys: Synergistic Effects of Integrating Computational Simulation and Experiments

Jiang,

Li,

Wang

et al. 2024

Small Structures

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High‐entropy alloys (HEAs) are novel materials composed of multiple elements with nearly equal concentrations and they exhibit exceptional properties such as high strength, ductility, thermal stability, and corrosion resistance. However, the intricate and diverse structures of HEAs pose significant challenges to understanding and predicting their behavior at different length scales. This review summarizes recent advances in computational simulations and experiments of structure‐property relationships in HEAs at the nano/micro scales. Various methods such as first‐principles calculations, molecular dynamics simulations, phase diagram calculations, and finite element simulations are discussed for revealing atomic/chemical and crystal structures, defect formation and migration, diffusion and phase transition, phase formation and stability, stress‐strain distribution, deformation behavior, and thermodynamic properties of HEAs. Emphasis is placed on the synergistic effects of computational simulations and experiments in terms of validation and complementarity to provide insights into the underlying mechanisms and evolutionary rules of HEAs. Additionally, current challenges and future directions for computational and experimental studies of HEAs are identified, including accuracy, efficiency, and scalability of methods, integration of multiscale and multiphysics models, and exploration of practical applications of HEAs.

show abstract

Revealing the crucial role of rough energy landscape on self-diffusion in high-entropy alloys based on machine learning and kinetic Monte Carlo

Cited by 29 publications

References 46 publications

Navigating energy landscapes for materials discovery: Integrating modeling, simulation, and machine learning

Navigating energy landscapes for materials discovery: Integrating modeling, simulation, and machine learning

Machine Learning Paves the Way for High Entropy Compounds Exploration: Challenges, Progress, and Outlook

Accelerating the Exploration of High‐Entropy Alloys: Synergistic Effects of Integrating Computational Simulation and Experiments

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