jz9b02420_si_001.pdf (2.63 MB)
Accelerated Discovery of Two-Dimensional Optoelectronic Octahedral Oxyhalides via High-Throughput Ab Initio Calculations and Machine Learning
journal contribution
posted on 2019-10-17, 14:42 authored by Xing-Yu Ma, James P. Lewis, Qing-Bo Yan, Gang SuTraditional trial-and-error methods
are obstacles for large-scale
searching of new optoelectronic materials. Here, we introduce a method
combining high-throughput ab initio calculations
and machine-learning approaches to predict two-dimensional octahedral
oxyhalides with improved optoelectronic properties. We develop an
effective machine-learning model based on an expansive data set generated
from density functional calculations including the geometric and electronic
properties of 300 two-dimensional octahedral oxyhalides. Our model
accelerates the screening of potential optoelectronic materials of
5000 two-dimensional octahedral oxyhalides. The distorted stacked
octahedral factors proposed in our model play essential roles in the
machine-learning prediction. Several potential two-dimensional optoelectronic
octahedral oxyhalides with moderate band gaps, high electron mobilities,
and ultrahigh absorbance coefficients are successfully hypothesized.