Machine learning (ML)-assisted first-principles DFT study of mechanical properties, electron–hole recombination, and enhanced light absorption in transition metal trioxides for photocatalytic applications

DFT and Machine Learning Unravel the Mechanism of BC3-Supported Single Atom Catalysts for Efficient N2 Reduction and Hydrogen Evolution

The electrocatalytic nitrogen reduction reaction (NRR) is a valuable green route for ammonia synthesis, yet it is still constrained by the lack of efficient catalysts and the impact of the competing hydrogen evolution reaction (HER). Density functional theory (DFT) is applied to explore the NRR and HER behaviors of m...

Machine learning guided O2 reduction to H2O2 on BN co-doped γ-graphyne: insights from time-domain atomistic dynamics.

Visible-light-driven production of hydrogen peroxide (H2O2) offers a promising and sustainable pathway. Metal-free carbon-based materials are attractive photocatalysts for the two-electron oxygen reduction reaction (2e- ORR), but their performance is often limited by rapid electron-hole recombination and sluggish kinet...

First-principles and machine-learning-guided study of 65 Sc-based quaternary Heusler alloys with high thermoelectric performance and promising optoelectronic properties

In this work, 65 Sc-based quaternary Heusler alloys with 18 valence electrons are systematically investigated by density functional theory in search of new semiconducting materials with multifunctional properties. Structural optimization identifies Y-type (I) and Y-type (II) as the dominant ground-state configurations,...

Machine-Learning-Assisted First-Principles Approach for the Electrochemical Reduction of CO2 on Single-Atom Catalysts

Molecular design of an active electrocatalyst can determine the primary product. Precise selection of the active metal center influences the adsorption of reactants, intermediates, and product selectivity and ultimately affects the efficiency of the process. Here, we predict a potent catalyst, namely, Fe@C6N6, with e...

Machine learning-guided design of redox-active Ru(II)/Os(II) molecular switches for single-molecule electronics

A critical benchmark of universal machine learning force fields and ReaxFF for iron-based oxygen carriers in chemical looping combustion: Defining applicability boundaries from static properties to dynamic CH 4 conversion

Machine learning force fields (MLFFs) offer an attractive balance of accuracy and efficiency for energy conversion processes. However, applications to chemical looping combustion (CLC) are hindered by transferability and reliability of MLFFs to high-temperature, multiphase oxidation reactions over complex oxide inter...