AMP2: A fully automated program for ab initio calculations of crystalline materials

Youn, Yong; Lee, Miso; Hong, Chang-Ho; Kim, Doyeon; Kim, Sangtae; Jung, Jisu; Yim, Kanghoon; Han, Seungwu

doi:10.1016/j.cpc.2020.107450

Cited by 12 publications

(10 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…About pseudopotentials, we mostly employ those without any tags in the VASP database, which tends to reduce the number of valence electrons. For further details, we refer to the original publication 15 .…”

Section: Methodsmentioning

confidence: 99%

“…Addressing limitations in the existing material databases, we herein report a theoretical dataset of fundamental and optical E g computed by employing a hybrid functional and identifying the stable magnetic ordering, thus providing more accurate E g than the existing databases. For the high-throughput computational workflow, we employ ‘Automated Ab initio Modeling of Materials Property Package’ (AMP 2 ) 15 , which is a fully automated package for density functional theory (DFT) calculations of crystalline properties. AMP 2 addresses the band-gap underestimation in semilocal functionals with the help of a hybrid functional, thereby producing a more accurate E g , even if the material is incorrectly metallic within the semilocal functional.…”

Section: Background and Summarymentioning

confidence: 99%

“…For small-gap materials such as Cu 3 AsSe 4 , Ge, and IrSb 3 , E g is sensitive to the lattice parameters that are slightly overestimated by PBE. Employing experimental lattice parameters or those relaxed within HSE significantly improves the results 15 . For Cu-bearing materials, it is known that HSE often exhibits substantial errors in E g due to nonlocal screening effects in Cu, which requires GW calculations 33 , 34 .…”

Section: Technical Validationmentioning

confidence: 99%

See 2 more Smart Citations

A band-gap database for semiconducting inorganic materials calculated with hybrid functional

et al. 2020

Self Cite

View full text Add to dashboard Cite

Semiconducting inorganic materials with band gaps ranging between 0 and 5 eV constitute major components in electronic, optoelectronic and photovoltaic devices. Since the band gap is a primary material property that affects the device performance, large band-gap databases are useful in selecting optimal materials in each application. While there exist several band-gap databases that are theoretically compiled by density-functional-theory calculations, they suffer from computational limitations such as band-gap underestimation and metastable magnetism. In this data descriptor, we present a computational database of band gaps for 10,481 materials compiled by applying a hybrid functional and considering the stable magnetic ordering. For benchmark materials, the root-mean-square error in reference to experimental data is 0.36 eV, significantly smaller than 0.75–1.05 eV in the existing databases. Furthermore, we identify many small-gap materials that are misclassified as metals in other databases. By providing accurate band gaps, the present database will be useful in screening materials in diverse applications.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Background and Summarymentioning

confidence: 99%

Section: Technical Validationmentioning

confidence: 99%

See 1 more Smart Citation

A band-gap database for semiconducting inorganic materials calculated with hybrid functional

et al. 2020

Self Cite

View full text Add to dashboard Cite

show abstract

“…Vast amounts of data of different origin may be utilized to understand and design catalytic processes [67,68]. A substantial number of publicly accessible databases has become available along with software packages encoding general workflows to interact with these databases [93][94][95][96][97][98][99][100][101][102][103][104]. Screening these data can produce valuable property predictions [105][106][107][108][109].…”

Section: Computational Catalysis and Mechanism Explorationmentioning

confidence: 99%

Autonomous Reaction Network Exploration in Homogeneous and Heterogeneous Catalysis

Steiner

Reiher

2022

Top Catal

View full text Add to dashboard Cite

Autonomous computations that rely on automated reaction network elucidation algorithms may pave the way to make computational catalysis on a par with experimental research in the field. Several advantages of this approach are key to catalysis: (i) automation allows one to consider orders of magnitude more structures in a systematic and open-ended fashion than what would be accessible by manual inspection. Eventually, full resolution in terms of structural varieties and conformations as well as with respect to the type and number of potentially important elementary reaction steps (including decomposition reactions that determine turnover numbers) may be achieved. (ii) Fast electronic structure methods with uncertainty quantification warrant high efficiency and reliability in order to not only deliver results quickly, but also to allow for predictive work. (iii) A high degree of autonomy reduces the amount of manual human work, processing errors, and human bias. Although being inherently unbiased, it is still steerable with respect to specific regions of an emerging network and with respect to the addition of new reactant species. This allows for a high fidelity of the formalization of some catalytic process and for surprising in silico discoveries. In this work, we first review the state of the art in computational catalysis to embed autonomous explorations into the general field from which it draws its ingredients. We then elaborate on the specific conceptual issues that arise in the context of autonomous computational procedures, some of which we discuss at an example catalytic system. Graphical Abstract

show abstract

“…After 120 generations, USPEX suggests 10-20 candidate structures with lowest energies. By utilizing AMP 2 [69], an automation script for operating VASP, we relax these structures until all the atomic forces and stress components are less than 0.02 eV/ Å and 4 kbar, thereby obtaining accurate DFT energies.…”

Section: B Comparison Of Nnp and Dft Energies For Metastable Structuresmentioning

confidence: 99%

Training machine-learning potentials for crystal structure prediction using disordered structures

Hong,

Choi,

Jeong

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

Prediction of the stable crystal structure for multinary (ternary or higher) compounds demands fast and accurate evaluation of free energies in exploring the vast configurational space. The machine-learning potential such as the neural network potential (NNP) is poised to meet this requirement but a dearth of information on the crystal structure poses a challenge in choosing training sets. Herein we propose constructing the training set from density-functional-theory (DFT) based dynamical trajectories of liquid and quenched amorphous phases, which does not require any preceding information on material structures except for the chemical composition. To demonstrate suitability of the trained NNP in the crystal structure prediction, we compare NNP and DFT energies for Ba2AgSi3, Mg2SiO4, LiAlCl4, and InTe2O5F over experimental phases as well as low-energy crystal structures that are generated theoretically. For every material, we find strong correlations between DFT and NNP energies, ensuring that the NNPs can properly rank energies among lowenergy crystalline structures. We also find that the evolutionary search using the NNPs can identify low-energy metastable phases more efficiently than the DFT-based approach. By proposing a way to developing reliable machine-learning potentials for the crystal structure prediction, this work will pave the way to identifying unexplored multinary phases efficiently.

show abstract

AMP2: A fully automated program for ab initio calculations of crystalline materials

Cited by 12 publications

References 45 publications

A band-gap database for semiconducting inorganic materials calculated with hybrid functional

A band-gap database for semiconducting inorganic materials calculated with hybrid functional

Autonomous Reaction Network Exploration in Homogeneous and Heterogeneous Catalysis

Training machine-learning potentials for crystal structure prediction using disordered structures

Contact Info

Product

Resources

About