Active learning for accelerated design of layered materials

Hetero-structures made from vertically stacked monolayers of transition metal dichalcogenides hold great potential for optoelectronic and thermoelectric devices.Discovery of the optimal layered material for specific applications necessitates the estimation of key material properties,such as electronic band structure and thermal transport coefficients.However,screening of material properties via brute force ab initio calculations of the entire material structure space exceeds the limits of current computing resources.Moreover,the functional dependence of material properties on the structures is often complicated,making simplistic statistical procedures for prediction difficult to employ without large amounts of data collection.Here,we present a Gaussian process regression model,which predicts material properties of an input hetero-structure,as well as an active learning model based on Bayesian optimization,which can efficiently discover the optimal hetero-structure using a minimal number of ab initio calculations.The electronic band gap,conduction/valence band dispersions,and thermoelectric performance are used as representative material properties for prediction and optimization.The Materials Project platform is used for electronic structure computation,while the BoltzTraP code is used to compute thermoelectric properties.Bayesian optimization is shown to significantly reduce the computational cost of discovering the optimal structure when compared with finding an optimal structure by building a regression model to predict material properties.The models can be used for predictions with respect to any material property and our software,including data preparation code based on the Python Materials Genomics(PyMatGen)library as well as python-based machine learning code,is available open source.

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.