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

The machine learning models developed on a dataset comprising particular class of materials show poor transferability across different classes. The problem can be partially solved by increasing the variability in the dataset at the cost of prediction accuracy. To develop a model on a highly variable database, we propose a localized regression based patchwork kriging approach for capturing most of the complex details in the data. In this approach, the data is partitioned into smaller regions with shared patches of few datapoints across the neighboring boundaries. Local regression functions are developed in each partition with a constrain to give similar performance at the boundary. Out of 17 different properties tried for partitioning the data, the decomposition with respect to target output  κ l gave local models with unprecedented accuracies. The partitioning with respect to  κ l , however, requires its estimate for any unknown compound beforehand. To address this, we developed a global model for the entire database. The global model accurately predicts the order of magnitude of  κ l for the compounds in the dataset and hence, directs them towards a particular partition for more accurate prediction. We define this stepwise approach as guided patchwork kriging, which can be applied to develop highly accurate transferable prediction models.

Guided patchwork kriging to develop highly transferable thermal conductivity prediction models