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

One of the grand challenges in modern theoretical chemistry is designing andimplementing approximations that expedite ab initio methods without loss ofaccuracy. Machine learning (ML), in particular neural networks, are emerging asa powerful approach to constructing various forms of transferable atomisticpotentials. They have been successfully applied in a variety of applications inchemistry, biology, catalysis, and solid-state physics. However, these modelsare heavily dependent on the quality and quantity of data used in theirfitting. Fitting highly flexible ML potentials comes at a cost: a vast amountof reference data is required to properly train these models. We address thisneed by providing access to a large computational DFT database, which consistsof 20M conformations for 57,454 small organic molecules. We believe it willbecome a new standard benchmark for comparison of current and future methods inthe ML potential community.

ANI-1: A data set of 20M off-equilibrium DFT calculations for organic molecules