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 Tools annual

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 monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Machine-learned force fields (ML-FFs) combine the accuracy of ab initiomethods with the efficiency of conventional force fields. However, currentML-FFs typically ignore electronic degrees of freedom, such as the total chargeor spin state, and assume chemical locality, which is problematic whenmolecules have inconsistent electronic states, or when nonlocal effects play asignificant role. This work introduces SpookyNet, a deep neural network forconstructing ML-FFs with explicit treatment of electronic degrees of freedomand quantum nonlocality. Chemically meaningful inductive biases and analyticalcorrections built into the network architecture allow it to properly modelphysical limits. SpookyNet improves upon the current state-of-the-art (orachieves similar performance) on popular quantum chemistry data sets. Notably,it is able to generalize across chemical and conformational space and canleverage the learned chemical insights, e.g. by predicting unknown spin states,thus helping to close a further important remaining gap for today's machinelearning models in quantum chemistry.

SpookyNet: Learning Force Fields with Electronic Degrees of Freedom and Nonlocal Effects