2D MXene materials have the advantages of versatile chemical composition, tunable layer thickness, and facile functionalization nature, and can be used as catalysts for the hydrogen evolution reaction (HER). However, tuning the thermal stability and activation of in-plane activity remain a challenge. We apply high-throughput density functional theory (DFT) calculations, together with a machine learning framework, to identify 2D MXene ordered binary alloy (OBA) activity trends and guide HER catalyst design. 2D MXenes of M_n+1X_nO₂ (n = 1, 2, 3; X = C, N) and OBA HER catalysts of M₂M′X₂O₂ and M₂M′₂X₃O₂ with 3d, 4d and 5d transition metal electrons were enumerated by screening, followed by catalytic activity, thermal stability, and conductivity computations. Our results indicate that 110 kinds of experimentally unexplored 2D MXene OBAs with thermostability and outstanding HER activity surpassing that of noble metal platinum were selected. Especially, the titanium element is mainly contained in the ideal catalysts of 2D MXene OBAs, which is consistent with the MXenes synthesized by experiments. Further, we show that descriptors developed using the AdaBoost ensemble learning model could accurately predict and uncover the essential geometric and chemical origin of HER activity, which is very consistent with the electronic insights. The advanced research strategy, which combines high-throughput computing with machine learning, shows robust ability for evaluating the activity trends and designing new complicated catalysts.

Inspired by the recent successful growth of Ti₂C and Ti₃C₂ monolayers, here, we investigate the structural, electronic, and mechanical properties of functionalized Ti₂C and Ti₃C₂ monolayers with chalcogenide and halogenide atoms by means of density functional theory calculations.

Amorphous silicon (a-Si) is an important thermal-management material and also serves as an ideal playground for studying heat transport in strongly disordered materials. Theoretical prediction of the thermal conductivity of a-Si in a wide range of temperatures and sample sizes is still a challenge. Herein we present a systematic investigation of the thermal transport properties of a-Si by employing large-scale molecular dynamics (MD) simulations with an accurate and efficient machine learned neuroevolution potential (NEP) trained against abundant reference data calculated at the quantum-mechanical density-functional-theory level. The high efficiency of NEP allows us to study the effects of finite size and quenching rate in the formation of a-Si in great detail. We find that a simulation cell up to $64000$ atoms (a cubic cell with a linear size of 11 nm) and a quenching rate down to ${10}^{11}$ K ${\mathrm{s}}^{-1}$ are required for almost convergent thermal conductivity. Structural properties, including short- and medium-range order as characterized by the pair-correlation function, angular-distribution function, coordination number, ring statistics, and structure factor are studied to demonstrate the accuracy of NEP and to further evaluate the role of quenching rate. Using both the heterogeneous and homogeneous nonequilibrium MD methods and the related spectral decomposition techniques, we calculate the temperature- and thickness-dependent thermal conductivity values of a-Si and show that they agree well with available experimental results from 10 K to room temperature. Our results also highlight the importance of quantum effects in the calculated thermal conductivity and support the quantum-correction method based on the spectral thermal conductivity.

MXene-STM design flow: A physical descriptor ϕ is built to uncover the hydrogen evolution reaction (HER) trends in Ti₂CO₂-STM (single transition metal doping) catalysis.

First principles calculations are performed to predict phonon-limited carrier mobility for a novel graphene-like semiconductor with BC₆N stoichiometry.