Pressure‐Induced Formation of Quaternary Compound and In−N Distribution in InGaAsN Zincblende from Ab Initio Calculation

Pluengphon, Prayoonsak; Wanarattikan, Pornsiri; Bovornratanaraks, Thiti; Inceesungvorn, Burapat

doi:10.1002/open.201900018

Cited by 15 publications

(7 citation statements)

References 39 publications

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“…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][7][8][9] 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%

Transferring predictions of formation energy across lattices of increasing size*

Lupo Pasini,

Karabin,

Eisenbach

2024

Mach. Learn.: Sci. Technol.

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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 the geometry optimization, we first trained our open-source, scalable implementation of GCNN called HydraGNN 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 HydraGNN 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%

Transferring predictions of formation energy across lattices of increasing size*

Lupo Pasini,

Karabin,

Eisenbach

2024

Mach. Learn.: Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…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%

Transferable prediction of formation energy across lattices of increasing size

Lupo Pasini,

Karabin,

Eisenbach

2024

Preprint

View full text Add to dashboard Cite

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.

show abstract

“…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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Pressure‐Induced Formation of Quaternary Compound and In−N Distribution in InGaAsN Zincblende from Ab Initio Calculation

Cited by 15 publications

References 39 publications

Transferring predictions of formation energy across lattices of increasing size*

Transferring predictions of formation energy across lattices of increasing size*

Transferable prediction of formation energy across lattices of increasing size

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

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