Discovery of highly radon-selective metal-organic frameworks through high-throughput computational screening and experimental validation

Park, Wanje; Oh, Kwang Hyun; Kim, Seo-Yul; Bae, Youn‐Sang

doi:10.1016/j.cej.2022.139189

Cited by 6 publications

(1 citation statement)

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“…As computations play an increasingly significant role in the development of new materials, their contribution to material data will be further enlarged. On the other hand, HTE serves to validate the computational models and results by refining the models and reliability in HTC [36–39] . The integration of material data and computations is the premise for efficient material computations and designs.…”

Section: Introductionmentioning

confidence: 99%

Advances in data‐assisted high‐throughput computations for material design

Xu,

Zhang,

Huo

et al. 2023

Materials Genome Engineering Advances

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Extensive trial and error in the variable space is the main cause of low efficiency and high cost in material development. The experimental tasks can be reduced significantly in the case that the variable space is narrowed down by reliable computer simulations. Because of their numerous variables in material design, however, the variable space is still too large to be accessed thoroughly even with a computational approach. High‐throughput computations (HTC) make it possible to complete a material screening in a large space by replacing the conventionally manual and sequential operations with automatic, robust, and concurrent streamlines. The efficiency of HTC, which is one of the pillars of materials genome engineering, has been verified in many studies, but its applications are still limited by demanding computational costs. Introduction of data mining and artificial intelligence into HTC has become an effective approach to solve the problem. In the past years, many studies have focused on the development and application of HTC and data combined approaches, which is considered as a new paradigm in computational materials science. This review focuses on the main advances in the field of data‐assisted HTC for material research and development and provides our outlook on its future development.

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Section: Introductionmentioning

confidence: 99%