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您的检索式：作者名="Steven B.Torrisi"
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| 1 | On-the-fly active learning of interpretable Bayesian force fields for atomistic rare events显示文摘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. | Jonathan Vandermause Steven B.Torrisi Simon Batzner Yu Xie Lixin Sun Alexie M.Kolpak Boris Kozinsky | 2020 | npj Computational Materials2020，，1： | 17 |
| 2 | Random forest machine learning models for interpretable X-ray absorption near-edge structure spectrum-property relationships显示文摘X-ray absorption spectroscopy(XAS)produces a wealth of information about the local structure of materials,but interpretation of spectra often relies on easily accessible trends and prior assumptions about the structure.Recently,researchers have demonstrated that machine learning models can automate this process to predict the coordinating environments of absorbing atoms from their XAS spectra.However,machine learning models are often difficult to interpret,making it challenging to determine when they are valid and whether they are consistent with physical theories.In this work,we present three main advances to the data-driven analysis of XAS spectra:we demonstrate the efficacy of random forests in solving two new property determination tasks(predicting Bader charge and mean nearest neighbor distance),we address how choices in data representation affect model interpretability and accuracy,and we show that multiscale featurization can elucidate the regions and trends in spectra that encode various local properties. | Steven B.Torrisi Matthew R.Carbone Brian A.Rohr Joseph H.Montoya Yang Ha Junko Yano Santosh K.Suram Linda Hung | 2020 | npj Computational Materials2020，，1： | 3 |
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