JAMIP: an artificial-intelligence aided data-driven infrastructure for computational materials informatics

Zhao, Xian-Geng; Zhou, Kun; Xing, Bangyu; Zhao, Ruoting; Luo, Sheng-Kang; Li, Tianshu; Sun, Yuanhui; Na, Guangren; Xie, Jiahao; Yang, Xiaoyu; Wang, Xinjiang; Wang, Xiaoyu; He, Xin; Lv, Jian; Fu, Yuhao; Zhang, Lijun

doi:10.1016/j.scib.2021.06.011

Cited by 41 publications

(25 citation statements)

References 48 publications

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“…99 Structure constructions, calculations, and data analysis were performed by the automatic high-throughput materials research workflow in our in-house developed high-throughput software, Jilin Artificial-intelligence aided Materials-design Integrated Package (JAMIP). 71,72 More detailed computational methods can be referred to our previous works on multiple layered two-dimensional materials. 74,100–104…”

Section: Methodsmentioning

confidence: 99%

“…We constructed a series of 2D lateral TMD-SLs [A m B n ] (A, B = MX 2 and m + n = 8 or m = n = 1–6) and explored the evolution of their electronic properties in parallel via an automated high-throughput computational method as implemented in JAMIP (Jilin Artificial-intelligence aided Materials-design Integrated Package). 71,72 The structural variants of SLs include the chemical component (A/B) ( e.g. , MoS 2 , WS 2 , MoSe 2 , and WSe 2 ), the interface structure ( e.g.…”

Section: Introductionmentioning

confidence: 99%

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Electronic property modulation in two-dimensional lateral superlattices of monolayer transition metal dichalcogenides

et al. 2022

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Electronic property modulation in two-dimensional lateral superlattices of monolayer transition metal dichalcogenides

et al. 2022

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“…We used our in-house developed code, the Jilin artificial intelligence-aided material design integrated package (JAMIP), , to perform all the DFT-based high-throughput calculations and analyze the calculated results. The VASP package was used to perform all the DFT calculations.…”

Section: Methodsmentioning

confidence: 99%

Inorganic Crystal Structure Prototype Database Based on Unsupervised Learning of Local Atomic Environments

et al. 2022

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Recognition of structure prototypes from tremendous known inorganic crystal structures has been an important subject beneficial for material science research and new materials design. The existing databases of inorganic crystal structure prototypes were mostly constructed by classifying materials in terms of the crystallographic space group information. Herein, we employed a distinct strategy to construct the inorganic crystal structure prototype database, relying on the classification of materials in terms of local atomic environments (LAE) accompanied by unsupervised machine learning method. Specifically, we adopted a hierarchical clustering approach onto all experimentally known inorganic crystal structures data to identify structure prototypes. The criterion for hierarchical clustering is the LAE represented by the state-of-the-art structure fingerprints of the improved bond-orientational order parameters and the smooth overlap of atomic positions. This allows us to build up a LAE-based Inorganic Crystal Structure Prototype Database (LAE-ICSPD) containing 15,613 structure prototypes with defined stoichiometries. In addition, we have developed a Structure Prototype Generator Infrastructure (SPGI) package, which is a useful toolkit for structure prototype generation. Our developed SPGI toolkit and LAE-ICSPD are beneficial for investigating inorganic materials in a global way as well as accelerating materials discovery process in the datadriven mode.

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“…The high-throughput first-principles calculations were performed by using the Jilin Artificial-intelligence aided Materials-design Integrated Package (JAMIP), which is an opensource artificial-intelligence-aided data-driven infrastructure designed purposely for computational materials informatics. 48 Table 1. The valence configurations of the pseudopotentials used in our solid-state DFT calculations.…”

Section: Methodsmentioning

confidence: 99%

Chemical templates that assemble the metal superhydrides

Sun

Miao

2021

Preprint

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The recent incessant discoveries of high-pressure hydrides totally altered our road map toward finding room temperature superconductors. Especially, metal superhydrides consisting of hydrogen covalent networks that resemble metallic hydrogen are favorable candidates for improving Tc and lowering pressure. However, the chemical force that drives the dissociation of H2 and the formation of H covalent network in superhydrides is unknown. Our high-throughput calculations show that, after removing H atoms, the remaining metal lattices exhibit unusual electron occupations at the interstitial regions, which matches excellently to the H lattice like a template. Furthermore, H lattices consist of 3D aromatic building units that are greatly stabilized by chemical templates of metals close to s-d boarder. The theory can help predicting ternary superhydrides that may form at lower pressure.

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JAMIP: an artificial-intelligence aided data-driven infrastructure for computational materials informatics

Cited by 41 publications

References 48 publications

Electronic property modulation in two-dimensional lateral superlattices of monolayer transition metal dichalcogenides

Electronic property modulation in two-dimensional lateral superlattices of monolayer transition metal dichalcogenides

Inorganic Crystal Structure Prototype Database Based on Unsupervised Learning of Local Atomic Environments

Chemical templates that assemble the metal superhydrides

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