Influence of atomic order on the enthalpy of formation and bulk modulus of the sigma phase

Liu, Wei; Lu, Xiao-Gang; Boulet, Pascal; Record, Marie‐Christine; Hu, Qing‐Miao

doi:10.1016/j.fluid.2017.10.006

Cited by 14 publications

(11 citation statements)

References 24 publications

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“…Density functional theory (DFT) [1,2] is an important computational method for studying solid solution alloys [3,4,5] that compensates for the limitations and impracticality of experimental approaches, as it allows researchers to study the properties of solid solution alloys at the electronic level, including how the composition affects the properties of the resulting alloy [6,7]. Studying solid solution alloys at the atomic and electronic levels requires considering crystal structures sufficiently large in size (i.e., containing thousands of atoms) to accurately capture the effect of the configurational entropy on the material properties.…”

Section: Introductionmentioning

confidence: 99%

“…Studying solid solution alloys at the atomic and electronic levels requires considering crystal structures sufficiently large in size (i.e., containing thousands of atoms) to accurately capture the effect of the configurational entropy on the material properties. The influence of atomic disorder on thermodynamic and mechanical properties has already been explored computationally [6,8,9,7] for various families of crystalline materials including solid solution alloys. Unfortunately, DFT calculations can fast become computationally expensive or infeasible, especially for systems with thousands of atoms.…”

Section: Introductionmentioning

confidence: 99%

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Transferable prediction of formation energy across lattices of increasing size

Lupo Pasini,

Karabin,

Eisenbach

2024

Preprint

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In this study, we show the transferability of graph convolutional neural network (GCNN) predictions of the formation energy of the nickel-platinum (NiPt) solid solution alloy across atomic structures of increasing sizes. The original dataset was generated with the Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS) using the second nearest-neighbor modified embedded-atom method (2NN MEAM) empirical interatomic potential. Geometry optimization was performed on the initially randomly generated face centered cubic (FCC) crystal structures and the formation energy has been calculated at each step of the geometry optimization, with configurations spanning the whole compositional range. Using data from various steps of geometry optimization, we first trained the GCNN on a lattice of 256 atoms, which accounts well for the short-range interactions. Using this data, we predicted the formation energy for lattices of 864 atoms and 2,048 atoms, which resulted in lower-than-expected accuracy due to the long-range interactions present in these larger lattices. We accounted for the long-range interactions by including a small amount of training data representative for those two larger sizes, whereupon the predictions of the GCNN scaled linearly with the size of the lattice. Therefore, our strategy ensured scalability while reducing significantly the computational cost of training on larger lattice sizes.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Transferable prediction of formation energy across lattices of increasing size

Lupo Pasini,

Karabin,

Eisenbach

2024

Preprint

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“…The design of solid solution alloys with desired mechanical macroscopic properties is a combinatorically complex problem because it requires exploring a high dimensional material space defined by multiple chemical compositions and disordered atomic configurations for each composition. The influence of atomic disorder on thermodynamic and mechanical properties has already been explored computationally [1,2,3,4] for various families of crystalline materials including solid solution alloys. Given N lattice sites and p possible pure element types at each site, the dimensionality of the material space characterizing solid solution alloys scales like p N , which explodes for increasing values of N and/or p. A thorough and time-efficient exploration of such a high-dimensional space requires fast (but still accurate) evaluations of the target property for every possible atomic arrangement.…”

Section: Introductionmentioning

confidence: 99%

Graph neural networks predict energetic and mechanical properties for models of solid solution metal alloy phases

Pasini

Jung

Irle

2022

Preprint

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We developed a graph convolutional neural network (GCNN) to predict the formation energy and the bulk modulus for models of solid solution alloys for various atomic crystal structures and relaxed volumes. We trained the GCNN model on a dataset for nickel-niobium (NiNb) that was generated for simplicity with the embedded atom model (EAM) empirical interatomic potential. The dataset has been generated by calculating the formation energy and the bulk modulus as a prototypical elastic property for optimized geometries starting from initial body-centered cubic (BCC), face-centered cubic (FCC), and hexagonal compact packed (HCP) crystal structures, with configurations spanning the possible compositional range for each of the three types of initial crystal structure. Numerical results show that the GCNN model effectively predicts both the formation energy and the bulk modulus as functions of the optimized crystal structure, relaxed volume, and configurational entropy of the model structures for solid solution alloys.

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“…On the microscale, atomistic thermodynamic modeling can provide energy information and often is utilized to gain information missing in CALPHAD [7,9]. The influence of atomic disorder on thermodynamic and mechanical properties has already been explored computationally [10,11,12,13] for various families of crystalline materials including solid solution alloys. Given N lattice sites and p possible pure element types at each site, the dimensionality of the material space characterizing solid solution alloys scales like p N , which explodes for increasing values of N and/or p. A thorough and time-efficient exploration of such a highdimensional space requires fast (but still accurate) evaluations of the target property for every possible atomic arrangement.…”

Section: Introductionmentioning

confidence: 99%