Combining Machine Learning and Molecular Dynamics to Predict Mechanical Properties and Microstructural Evolution of FeNiCrCoCu High-Entropy Alloys

Yu, Jingui; Yu, Faping; Fu, Qiang; Zhao, Gang; Gong, Caiyun; Wang, Mingchao; Zhang, Qiaoxin

doi:10.3390/nano13060968

Cited by 4 publications

(1 citation statement)

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“…These approaches are also applied in various aspects of MD simulations, 170) such as neural network potentials [54][55][56] , estimation of local atomic configuration, [171][172][173][174] and prediction of materials properties. [175][176][177][178][179][180] In particular, generative models are gaining attention in all fields as artificial intelligence tools for a new era. While there are not many examples of applying generative models to MD simulations yet, some pioneering studies have been reported.…”

Section: Application Of Machine Learning Approaches To MD Simulationmentioning

confidence: 99%

Molecular Dynamics of Solidification

Shibuta

2024

ISIJ Int.

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Over many years, mesoscale analysis such as the phase-field method has been the mainstream for numerical simulation of solidification. In contrast, our group has taken the initiative in applying molecular dynamics (MD) simulation to various problems in solidification. In this review, recent advances and contributions of MD simulations for solidification are presented. The primary contribution of MD simulation is the derivation of solid-liquid interfacial properties since it is not easy to measure these properties experimentally with high precision. In addition, recent significant progress in computational environments has dramatically expanded the possibilities of MD simulations for solidification. Now, MD simulations with a scale of billion atoms at the micrometerscale have become a reality, enabling the exploration of analyses previously dominated by mesoscale methods, such as grain growth and dendrite growth. In particular, the dendrite growth at the micrometer-scale presented here represents the first achievement of directly simulating a typical four-fold symmetrical dendrite structure solely through atomic-scale simulations, to the best of the author's knowledge. Moreover, new attempts at the fusion of data-driven methods and MD simulations are presented in this review, aiming to contribute to the rapid development of the field of solidification in the future.

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Section: Application Of Machine Learning Approaches To MD Simulationmentioning

confidence: 99%

Molecular Dynamics of Solidification

Shibuta

2024

ISIJ Int.

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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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