Authors: Jaeho Lee | Jaehwan Lee | Seokwon Shin | Youngdoo Son | Young‐Kyu Han
Source: International Journal of Energy Research (article, 2023-09-08)
Field: Materials Science — Materials Chemistry

Machine Learning for the Expedited Screening of Hydrogen Evolution Catalysts for Transition Metal-Doped Transition Metal Dichalcogenides Two-dimensional transition metal dichalcogenides (TMDs) have gained attention as potent catalysts for the hydrogen evolution reaction (HER). The traditional trial-and-error methodology for catalyst development has proven inefficient due to its costly and time-intensive nature. To accelerate the catalyst development process, the Gibbs free energy of hydrogen adsorption ( <a:math xmlns:a="http://www.w3.org/1998/Math/MathML" id="M1"> <a:mi>Δ</a:mi> <a:msub> <a:mrow> <a:mi>G</a:mi> </a:mrow> <a:mrow> <a:msup> <a:mrow> <a:mtext>H</a:mtext> </a:mrow> <a:mrow> <a:mo>∗</a:mo> </a:mrow> </a:msup> </a:mrow> </a:msub> </a:math> ), computed using the density functional theory (DFT), is widely used as the paramount descriptor for evaluating and predicting HER catalyst performance. However, DFT calculations for <c:math xmlns:c="http://www.w3.org/1998/Math/MathML" id="M2"> <c:mi>Δ</c:mi> <c:msub> <c:mrow> <c:mi>G</c:mi> </c:mrow> <c:mrow> <c:msup> <c:mrow> <c:mtext>H</c:mtext> </c:mrow> <c:mrow> <c:mo>∗</c:mo> </c:mrow> </c:msup> </c:mrow> </c:msub> </c:math> are time-consuming and thus pose a challenge for high-throughput screening. Herein, we devise a predictive model for <e:math xmlns:e="http://www.w3.org/1998/Math/MathML" id="M3"> <e:mi>Δ</e:mi> <e:msub> <e:mrow> <e:mi>G</e:mi> </e:mrow> <e:mrow> <e:msup> <e:mrow> <e:mtext>H</e:mtext> </e:mrow> <e:mrow> <e:mo>∗</e:mo> </e:mrow> </e:msup> </e:mrow> </e:msub> </e:math> within transition metal-doped TMD systems using a machine learning (ML) framework. We calculate DFT <g:math xmlns:g="http://www.w3.org/1998/Math/MathML" id="M4"> <g:mi>Δ</g:mi> <g:msub> <g:mrow> <g:mi>G</g:mi> </g:mrow> <g:mrow> <g:msup> <g:mrow> <g:mtext>H</g:mtext> </g:mrow> <g:mrow> <g:mo>∗</g:mo> </g:mrow> </g:msup> </g:mrow> </g:msub> </g:math> values for 150 TM-doped MX2 (CrS2, MoS2, WS2, MoSe2, and MoTe2) and apply various ML algorithms. We validate the universality of our model by constructing 15 new external test sets. The prediction results show a high correlation coefficient of <i:math xmlns:i="http://www.w3.org/1998/Math/MathML" id="M5"> <i:msup> <i:mrow> <i:mi>R</i:mi> </i:mrow> <i:mrow> <i:mn>2</i:mn> </i:mrow> </i:msup> <i:mo>=</i:mo> <i:mn>0.92</i:mn> </i:math> . Based on feature analysis, the three most important parameters are the number of valence electrons of the doped transition metal, the distance of the valence electrons of the doped transition metal, and the electronegativity of the doped transition metal. Our DFT-based ML model provides a useful guideline for the material development process through <k:math xmlns:k="http://www.w3.org/1998/Math/MathML" id="M6"> <k:mi>Δ</k:mi> <k:msub> <k:mrow> <k:mi>G</k:mi> </k:mrow> <k:mrow> <k:msup> <k:mrow> <k:mtext>H</k:mtext> </k:mrow> <k:mrow> <k:mo>∗</k:mo> </k:mrow> </k:msup> </k:mrow> </k:msub> </k:math> prediction and facilitates the efficient design of transition metal dichalcogenide catalysts that exhibit superior HER activity. Density functional theory ; Catalysis ; Transition metal ; Doping ; Metal ; Gibbs free energy ; Hydrogen ; Materials science ; Machine learning ; Physical chemistry ; Nanotechnology ; Chemistry ; Computational chemistry ; Physics ; Thermodynamics ; Computer science ; Quantum mechanics ; Metallurgy ; Organic chemistry ; Optoelectronics 2D Materials and Applications ; Advanced Photocatalysis Techniques ; Machine Learning in Materials Science