High-throughput screening and ML models for discovering novel insulators

Achieving zero-resistance current flow is one of the most challenging tasks in materials science and condensed matter physics research to reduce energy losses. The novel valley-polarized quantum anomalous Hall (VP-QAH) insulators have the potential to show dissipation-less current flow. This is due to the simultaneous existence of valley Hall and quantum anomalous Hall effect in a single material, which leads to a highly robust valley-depended edge current.

VP-QAH materials are exceptionally rare because of stringent requirements and the complex interplay between the valley and topological physics. These constraints for searching new VP-QAH materials can be easily controlled by high-throughput screening (HTS) and newly developed machine-learning (ML) based predictions.

A team at the Materials Research Centre led by Abhishek Kumar Singh has developed an efficient procedural algorithm for identifying VP-QAH insulators by utilising HTS coupled ML models, which could accelerate the design of next-generation electronic devices. By employing physically meaningful conditions, high-throughput screening is carried on the first principles MXene database “aNANt” on 13000 randomly selected structures. The screening resulted in 14 MXenes, having highly robust valley-dependent edge states making them ideal candidates to possess dissipation-less valleytronics. The HTS approach is extremely robust; however, it requires expensive first principles calculations to generate the database. Therefore, we have employed ML-based models to better predict the VP-QAH materials.

The models developed are highly accurate and transferable with excellent metric scores. The ML models can capture most of the variability in the data. This study, therefore, provides completely new insights for developing VP-QAH materials.

Accelerated Discovery of Valley Polarized Quantum Anomalous Hall Effect in MXenes Chem. Mater., DOI: https://doi.org/10.1021/acs.chemmater.1c00798

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