Enhancing the Faradaic efficiency of solid oxide electrolysis cells: progress and perspective

To reduce global warming,many countries are shifting to sustainable energy production systems.Solid oxide electrolysis cells(SOECs)are being considered due to their high hydrogen generation efficiency.However,low faradaic efficiency in scaling SOEC technology affects costs and limits large-scale adoption of hydrogen as fuel.This review covers SOECs’critical aspects:current state-of-the-art anode,cathode,and electrolyte materials,operational and materials parameters affecting faradaic efficiency,and computational modeling techniques to resolve bottlenecks affecting SOEC faradaic efficiency.

A computational framework for guiding the MOCVD-growth of wafer-scale 2D materials

Reproducible wafer-scale growth of two-dimensional(2D)materials using the Chemical Vapor Deposition(CVD)process with precise control over their properties is challenging due to a lack of understanding of the growth mechanisms spanning over several length scales and sensitivity of the synthesis to subtle changes in growth conditions.A multiscale computational framework coupling Computational Fluid Dynamics(CFD),Phase-Field(PF),and reactive Molecular Dynamics(MD)was developed–called the CPM model–and experimentally verified.Correlation between theoretical predictions and thorough experimental measurements for a Metal-Organic CVD(MOCVD)-grown WSe_(2)model material revealed the full power of this computational approach.Large-area uniform 2D materials are synthesized via MOCVD,guided by computational analyses.The developed computational framework provides the foundation for guiding the synthesis of wafer-scale 2D materials with precise control over the coverage,morphology,and properties,a critical capability for fabricating electronic,optoelectronic,and quantum computing devices.

INDEEDopt:a deep learning-based ReaxFF parameterization framework

Empirical interatomic potentials require optimization of force field parameters to tune interatomic interactions to mimic ones obtained by quantum chemistry-based methods.The optimization of the parameters is complex and requires the development of new techniques.Here,we propose an INitial-DEsign Enhanced Deep learning-based OPTimization(INDEEDopt)framework to accelerate and improve the quality of the ReaxFF parameterization.The procedure starts with a Latin Hypercube Design(LHD)algorithm that is used to explore the parameter landscape extensively.The LHD passes the information about explored regions to a deep learning model,which finds the minimum discrepancy regions and eliminates unfeasible regions,and constructs a more comprehensive understanding of physically meaningful parameter space.We demonstrate the procedure here for the parameterization of a nickel–chromium binary force field and a tungsten–sulfide–carbon–oxygen–hydrogen quinary force field.We show that INDEEDopt produces improved accuracies in shorter development time compared to the conventional optimization method.