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 Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Constructing more expressive ansatz has been a primary focus for quantumMonte Carlo, aimed at more accurate \textit{ab initio} calculations. However,with more powerful ansatz, e.g. various recent developed models based onneural-network architectures, the training becomes more difficult andexpensive, which may have a counterproductive effect on the accuracy ofcalculation. In this work, we propose to make use of the training data toperform variance extrapolation when using neural-network ansatz in variationalMonte Carlo. We show that this approach can speed up the convergence andsurpass the ansatz limitation to obtain an improved estimation of the energy.Moreover, variance extrapolation greatly enhances the error cancellationcapability, resulting in significantly improved relative energy outcomes, whichare the keys to chemistry and physics problems.

Variance extrapolation method for neural-network variational Monte Carlo