Charge optimized many-body (COMB) potential for dynamical simulation of Ni–Al phases

Kumar, Aakash; Chernatynskiy, Aleksandr V.; Liang, Tao; Choudhary, Kamal; Noordhoek, Mark J.; Cheng, Yu; Phillpot, Simon R.; Sinnott, Susan B.

doi:10.1088/0953-8984/27/33/336302

Cited by 10 publications

(8 citation statements)

References 49 publications

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“…Although this outcome might be somewhat discouraging, predicting defect energies can be considered one of the most difficult tests to pass. Still, the CENT potential outperforms other force fields with respect to the accuracy of these values 80,81 . CENT could be further improved by specifically including such defect data points in the training set.…”

Section: Defectsmentioning

confidence: 91%

High accuracy and transferability of a neural network potential through charge equilibration for calcium fluoride

et al. 2017

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We investigate the accuracy and transferability of a recently developed high dimensional neural network (NN) method for calcium fluoride, fitted to a database of ab initio density functional theory (DFT) calculations based on the Perdew-Burke-Ernzerhof (PBE) exchange correlation functional. We call the method charge equilibration via neural network technique (CENT). Although the fitting database contains only clusters (i.e. non-periodic structures), the NN scheme accurately describes a variety of bulk properties. In contrast to other available empirical methods the CENT potential has a much simpler functional form, nevertheless it correctly reproduces the PBE energetics of various crystalline phases both at ambient and high pressure. Surface energies and structures as well as dynamical properties derived from phonon calculations are also in good agreement with PBE results. Overall, the difference between the values obtained by the CENT potential and the PBE reference values is less than or equal to the difference between the values of local density approximation (LDA) and Born-Mayer-Huggins (BMH) with those calculated by the PBE exchange correlation functional.

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Section: Defectsmentioning

confidence: 91%

High accuracy and transferability of a neural network potential through charge equilibration for calcium fluoride

et al. 2017

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“…It is known to work well for metallic, ionic and polymeric systems. Cuprous oxide in COMB 47 , 48 has B v , C 11 and cohesive energy (E c ) as 102 GPa, 114.1 GPa and −3.65 eV/atom while the DFT values are 111.5 GPa,124.16 GPa and −4.79 eV/atom respectively. Again, it is to be noted these values can be sensitive to the initial calculation set-up and the target property/data for which the force-fields were fit.…”

Section: Technical Validationmentioning

confidence: 99%

“…Complex force-fields such as ReaxFF 44 , 45 and COMB potentials 47 , 48 are used for investigating heterogeneous systems. In these cases, it is generally common that the DFT convex hull plot and force-field convex hull plots are different.…”

Section: Technical Validationmentioning

confidence: 99%

Evaluation and comparison of classical interatomic potentials through a user-friendly interactive web-interface

et al. 2017

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Classical empirical potentials/force-fields (FF) provide atomistic insights into material phenomena through molecular dynamics and Monte Carlo simulations. Despite their wide applicability, a systematic evaluation of materials properties using such potentials and, especially, an easy-to-use user-interface for their comparison is still lacking. To address this deficiency, we computed energetics and elastic properties of variety of materials such as metals and ceramics using a wide range of empirical potentials and compared them to density functional theory (DFT) as well as to experimental data, where available. The database currently consists of 3248 entries including energetics and elastic property calculations, and it is still increasing. We also include computational tools for convex-hull plots for DFT and FF calculations. The data covers 1471 materials and 116 force-fields. In addition, both the complete database and the software coding used in the process have been released for public use online (presently at http://www.ctcms.nist.gov/ knc6/periodic.html) in a user-friendly way designed to enable further material design and discovery.

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“…Currently, the vacancy formation energy dataset consists of 508 entries. We compare a subset of this dataset with available data from previous experimental and DFT-studies 14,[39][40][41][42][43] in Fig. 1a.…”

mentioning

confidence: 99%

A Deep-learning Model for Fast Prediction of Vacancy Formation in Diverse Materials

Choudhary¹,

Sumpter²

2022

Preprint

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The presence of point defects such as vacancies plays an important role in material design. Here, we demonstrate that a graph neural network (GNN) model trained only on perfect materials can also be used to predict vacancy formation energies (E vac ) of defect structures without the need for additional training data. Such GNN-based predictions are considerably faster than density functional theory (DFT) calculations with reasonable accuracy and show the potential that GNNs are able to capture a functional form for energy predictions. To test this strategy, we developed a DFT dataset of 508 E vac consisting of 3D elemental solids, alloys, oxides, nitrides, and 2D monolayer materials. We analyzed and discussed the applicability of such direct and fast predictions. We applied the model to predict 192494 E vac for 55723 materials in the JARVIS-DFT database.Defects play an important role in our pursuit to engineer performance of a material. Vacancies are a type of defects which are ubiquitous and their presence can significantly alter 1

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Charge optimized many-body (COMB) potential for dynamical simulation of Ni–Al phases

Cited by 10 publications

References 49 publications

High accuracy and transferability of a neural network potential through charge equilibration for calcium fluoride

High accuracy and transferability of a neural network potential through charge equilibration for calcium fluoride

Evaluation and comparison of classical interatomic potentials through a user-friendly interactive web-interface

A Deep-learning Model for Fast Prediction of Vacancy Formation in Diverse Materials

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