How can machine learning be used with molecular dynamics of alloys?
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Machine learning can be used with molecular dynamics of alloys to predict their mechanical properties and analyze their microstructural evolution behavior. Researchers have integrated experimental and computational studies with machine learning algorithms to predict the mechanical properties of high entropy alloys (HEAs) such as FeNiCrCoCu HEAs . Neural network (NN) deep machine learning interatomic potentials have been developed for Fe-Si alloys, which effectively describe the structures and energies of liquid and crystalline phases . Machine learning algorithms, such as multiple linear regression, have been used to predict the optimal composition and tensile strength of FeNiCrCoCu HEAs . Atomic cluster expansion (ACE) combined with first-principles density functional theory (DFT) calculations has been employed for machine learning to obtain force fields for binary alloys like Fe-Co .
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| Papers (5) | Insight |
|---|---|
| Machine learning can be used with molecular dynamics of alloys by employing the method of atomic cluster expansion (ACE) combined with first-principles density functional theory (DFT) calculations to obtain a machine learning force field for the alloy. | |
| Machine learning can be used with molecular dynamics of alloys by fitting force field parameters using the method of atomic cluster expansion combined with first-principles density functional theory. | |
| Machine learning is used with molecular dynamics of alloys to predict the mechanical properties of high-entropy alloys, such as tensile strength, by combining the data processing and analysis capabilities of machine learning with the simulation capabilities of molecular dynamics. | |
| Machine learning can be used with molecular dynamics of alloys by developing a neural network (NN) deep machine learning interatomic potential, which effectively describes the structural and dynamical properties of complex materials at the atomistic level. | |
| Machine learning can be used with molecular dynamics of alloys to predict their mechanical properties, such as Young's modulus, yield strength, and ultimate tensile strength, based on a set of input parameters including atomic concentrations, grain size, operating temperature, and strain rate. |
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