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

The need for advanced materials has led to the development of complex,multi-component alloys or solid-solution alloys. These materials have shownexceptional properties like strength, toughness, ductility, electrical andelectronic properties. Current development of such material systems arehindered by expensive experiments and computationally demandingfirst-principles simulations. Atomistic simulations can provide reasonableinsights on properties in such material systems. However, the issue ofdesigning robust potentials still exists. In this paper, we explore a deepconvolutional neural-network based approach to develop the atomistic potentialfor such complex alloys to investigate materials for insights into controllingproperties. In the present work, we propose a voxel representation of theatomic configuration of a cell and design a 3D convolutional neural network tolearn the interaction of the atoms. Our results highlight the performance ofthe 3D convolutional neural network and its efficacy in machine-learning theatomistic potential. We also explore the role of voxel resolution and provideinsights into the two bounding box methodologies implemented for voxelization.

3D Deep Learning with voxelized atomic configurations for modeling atomistic potentials in complex solid-solution alloys