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.

Autonomous reinforcement learning agent for stretchable kirigami design of 2D materials

Mechanical behavior of 2D materials such as MoS_(2) can be tuned by the ancient art of kirigami.Experiments and atomistic simulations show that 2D materials can be stretched more than 50%by strategic insertion of cuts.However,designing kirigami structures with desired mechanical properties is highly sensitive to the pattern and location of kirigami cuts.We use reinforcement learning(RL)to generate a wide range of highly stretchable MoS_(2) kirigami structures.The RL agent is trained by a small fraction(1.45%)of molecular dynamics simulation data,randomly sampled from a search space of over 4 million candidates for MoS_(2)kirigami structures with 6 cuts.After training,the RL agent not only proposes 6-cut kirigami structures that have stretchability above 45%,but also gains mechanistic insight to propose highly stretchable(above 40%)kirigami structures consisting of 8 and 10 cuts from a search space of billion candidates as zero-shot predictions.