The ReaxFF reactive force-field: development, applications and future directions

The reactive force-field(ReaxFF)interatomic potential is a powerful computational tool for exploring,developing and optimizing material properties.Methods based on the principles of quantum mechanics(QM),while offering valuable theoretical guidance at the electronic level,are often too computationally intense for simulations that consider the full dynamic evolution of a system.Alternatively,empirical interatomic potentials that are based on classical principles require significantly fewer computational resources,which enables simulations to better describe dynamic processes over longer timeframes and on larger scales.Such methods,however,typically require a predefined connectivity between atoms,precluding simulations that involve reactive events.The ReaxFF method was developed to help bridge this gap.Approaching the gap from the classical side,ReaxFF casts the empirical interatomic potential within a bond-order formalism,thus implicitly describing chemical bonding without expensive QM calculations.This article provides an overview of the development,application,and future directions of the ReaxFF method.

Multiobjective genetic training and uncertainty quantification of reactive force fields

The ReaxFF reactive force-field approach has significantly extended the applicability of reactive molecular dynamics simulations to a wide range of material properties and processes.ReaxFF parameters are commonly trained to fit a predefined set of quantummechanical data,but it remains uncertain how accurately the quantities of interest are described when applied to complex chemical reactions.Here,we present a dynamic approach based on multiobjective genetic algorithm for the training of ReaxFF parameters and uncertainty quantification of simulated quantities of interest.ReaxFF parameters are trained by directly fitting reactive molecular dynamics trajectories against quantum molecular dynamics trajectories on the fly,where the Pareto optimal front for the multiple quantities of interest provides an ensemble of ReaxFF models for uncertainty quantification.Our in situ multiobjective genetic algorithm workflow achieves scalability by eliminating the file I/O bottleneck using interprocess communications.The in situ multiobjective genetic algorithm workflow has been applied to high-temperature sulfidation of MoO_(3) by H_(2)S precursor,which is an essential reaction step for chemical vapor deposition synthesis of MoS_(2) layers.Our work suggests a new reactive molecular dynamics simulation approach for far-from-equilibrium chemical processes,which quantitatively reproduces quantum molecular dynamics simulations while providing error bars.

NOAA/AVHRR sea surface temperature accuracy in the East/Japan Sea

Sea surface temperature(SST)retrieved from Advanced Very High Resolution Radiometer(AVHRR)onboard National Oceanic and Atmospheric Administration(NOAA)polar orbiting environmental satellites were validated in the East/Japan Sea(EJS)using surface drifter measurements as ground truths from 2005 to 2010.Overall,the root-mean-square(rms)errors of multichannel SSTs(MCSSTs)and non-linear SSTs(NLSSTs)using global SST coefficients were approximately 0.85℃ and 0.80℃,respectively.An analysis of the SST errors(satellite-drifter)revealed a dependence on the amount of atmospheric moisture.In addition,satellite-derived SSTs tended to be related to wind speeds,particularly during the night.The SST errors also demonstrated diurnal variations with relatively higher rms from 0.80℃ to 1.00℃ during the night than the day,with a small rms of about 0.50℃.Bias also exhibited reasonable diurnal differences,showing small biases during the daytime.Although a satellite zenith angle has been considered in the global SST coefficients,its effect on the SST errors still remained in case of the EJS.Given the diverse use of SST data,the continuous validation and understanding of the characteristic errors of satellite SSTs should be conducted based on extensive in-situ temperature measurements in the global ocean as well as local seas.