Two‐dimensional materials with active sites are expected to replace platinum as large‐scale hydrogen production catalysts. However, the rapid discovery of excellent two‐dimensional hydrogen evolution reaction catalysts is seriously hindered due to the long experiment cycle and the huge cost of high‐throughput calculations of adsorption energies. Considering that the traditional regression models cannot consider all the potential sites on the surface of catalysts, we use a deep learning method with crystal graph convolutional neural networks to accelerate the discovery of high‐performance two‐dimensional hydrogen evolution reaction catalysts from two‐dimensional materials database, with the prediction accuracy as high as 95.2%. The proposed method considers all active sites, screens out 38 high performance catalysts from 6,531 two‐dimensional materials, predicts their adsorption energies at different active sites, and determines the potential strongest adsorption sites. The prediction accuracy of the two‐dimensional hydrogen evolution reaction catalysts screening strategy proposed in this work is at the density‐functional‐theory level, but the prediction speed is 10.19 years ahead of the high‐throughput screening, demonstrating the capability of crystal graph convolutional neural networks‐deep learning method for efficiently discovering high‐performance new structures over a wide catalytic materials space.
The
shuttle effect has been a major obstacle to the development
of lithium–sulfur batteries. The discovery of new host materials
is essential, but lengthy and complex experimental studies are inefficient
for the identification of potential host materials. We proposed a
machine learning method for the rapid discovery of an AB2-type sulfur host material to suppress the shuttle effect using the 2DMatPedia database, discovering 14 new structures (PdN2, TaS2, PtN2, TaSe2, AgCl2, NbSe2, TaTe2, AgF2, NiN2, AuS2, TmI2, NbTe2, NiBi2, and AuBr2) from 1320 AB2-type compounds.
These structures have strong adsorptions of greater than 1.0 eV for
lithium polysulfides and appreciable electron-transportation capability,
which can serve as the most promising AB2-type host materials
in lithium–sulfur batteries. On the basis of a small data set,
we successfully predicted Li2S6 adsorption at
arbitrary sites on substrate materials using transfer learning, with
a considerably low mean absolute error (below 0.05 eV). The proposed
data-driven method, as accurate as density functional theory calculations,
significantly shortens the research cycle of screening AB2-type sulfur host materials by approximately 8 years. This method
provides high-precision and expeditious solutions for other high-throughput
calculations and material screenings based on adsorption energy predictions.
Lead-free double perovskites are regarded as stable and green optoelectronic alternatives to single perovskites, but may exhibit indirect band gaps and high effective masses, thus limiting their maximum photovoltaic efficiency. Considering that the trialand-error experimental and computational approaches cannot quickly identify ideal candidates, we propose an ensemble learning workflow to screen all suitable double perovskites from the periodic table, with a high predictive accuracy of 92% and a computed speed that is ∼10 8 faster than ab initio calculations. From ∼23 314 unexplored double perovskites, we successfully identify six candidates that exhibit suitable band gaps (1.0−2.0 eV), where two have direct band gaps and low effective masses. They all show good thermal stabilities that are hopefully able to be synthesized. The proposed ML workflow immensely shortens the screening cycle for double perovskites, which will greatly promote the development and application of photovoltaic devices.
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