Biased screening for multi-component materials with Structures of Alloy Generation And Recognition (SAGAR)
Source abstract
Abstract
Abstract Exploring new materials has attracted more and more attention, due to the improvement of technology and industry. Theoretically, the first-principles calculations combined with global optimization have been applied to predicting the properties of materials accurately and accelerate the discovery of new materials. For certain multi-component materials, the atoms are nearly at the lattice sites and the material exploration can be enhanced with biased screening. Based on the space symmetry, the calculations of duplicate structures can be avoided, and the specified constraints are applied to filter the energetically unstable candidates out. To provide the reasonable candidates for the first-principles calculations, we have released a program of Structures of Alloy Generation And Recognition (SAGAR), which can be conveniently used through the website. Herein, we will present several examples to show that the biased screening is practical and efficient, determining structures with high stabilities and novel properties. We have studied the distribution of up to 50% Cl vacancy concentration in NaCl and revealed semiconductor–metal transition in the boron/nitrogen co-doped diamond. The ground state anti-ferrormagnetic configuration of VCl2 is determined, as well as the low-lying hydrogenated-C60 structures with high symmetry. Our results indicate that the biased screening with proper constraint will effectively enhance the material exploration.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Machine Learning in Materials Science · Diamond and Carbon-based Materials Research · Electronic and Structural Properties of Oxides
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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