On-the-fly active learning of interpretable Bayesian force fields for atomistic rare events

Machine learned force fields typically require manual construction of training sets consisting of thousands of first principles calculations,which can result in low training efficiency and unpredictable errors when applied to structures not represented in the training set of the model.This severely limits the practical application of these models in systems with dynamics governed by important rare events,such as chemical reactions and diffusion.We present an adaptive Bayesian inference method for automating the training of interpretable,low-dimensional,and multi-element interatomic force fields using structures drawn on the fly from molecular dynamics simulations.Within an active learning framework,the internal uncertainty of a Gaussian process regression model is used to decide whether to accept the model prediction or to perform a first principles calculation to augment the training set of the model.The method is applied to a range of single-and multi-element systems and shown to achieve a favorable balance of accuracy and computational efficiency,while requiring a minimal amount of ab initio training data.We provide a fully opensource implementation of our method,as well as a procedure to map trained models to computationally efficient tabulated force fields.

Accurate and scalable graph neural network force field and molecular dynamics with direct force architecture

Recently,machine learning(ML)has been used to address the computational cost that has been limiting ab initio molecular dynamics(AIMD).Here,we present GNNFF,a graph neural network framework to directly predict atomic forces from automatically extracted features of the local atomic environment that are translationally-invariant,but rotationally-covariant to the coordinate of the atoms.We demonstrate that GNNFF not only achieves high performance in terms of force prediction accuracy and computational speed on various materials systems,but also accurately predicts the forces of a large MD system after being trained on forces obtained from a smaller system.Finally,we use our framework to perform an MD simulation of Li7P3S11,a superionic conductor,and show that resulting Li diffusion coefficient is within 14%of that obtained directly from AIMD.The high performance exhibited by GNNFF can be easily generalized to study atomistic level dynamics of other material systems.

Endwall aerodynamic losses from turbine components within gas turbine engines

A survey of research on aerodynamic loss investigations for turbine components of gas tuibine engines is presented.Experimental and numerically predicted results are presented from investigations undertaken over the past 65 plus years.Of particular interest are losses from the development of secondary flows from airfoil/endwall interactions.The most important of the airfoilAmdwall secondary flows are passage vortices,counter voitices,and corner vortices.The structure and development of these secondaiy flows are described as they affect aerodynamic perfonnance within and downstream of turbine passage flows in compressible,high speed flows with either subsonic or transonic Mach number distributions,as well as within low-speed,incompressible flows.Also discussed are methods of endwall contouring,and its consequences in regard to airfoil/endwall secondary flows.