English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Nanoporous materials have attracted significant interest as an emergingplatform for adsorption-related applications. The high-throughput computationalscreening became a standard technique to access the performance of thousands ofcandidates, but its accuracy is highly dependent on a partial charge assignmentmethod. In this study, we propose a machine learning model that can reconcilethe benefits of two main approaches-the high accuracy of density-derivedelectrostatic and chemical charge (DDEC) method and the scalability of chargeequilibration (Qeq) method. The mean absolute deviation of predicted partialcharges from the original DDEC counterparts archive an excellent level of0.01e. The model, initially designed for metal-organic frameworks, is alsocapable of assigning charges to another class of nanoporous materials, covalentorganic frameworks, with acceptable accuracy. Adsorption properties of carbondioxide, calculated by means of machine learning derived charges, areconsistent with the reference data obtained with DDEC charges.

Transferable and extensible machine learning derived atomic charges for modeling hybrid nanoporous materials