Bulletin of the American Physical Society
APS March Meeting 2020
Volume 65, Number 1
Monday–Friday, March 2–6, 2020; Denver, Colorado
Session F36: Molecular Dynamics Ex Machina: Successes and ChallengesInvited
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Sponsoring Units: DCOMP Chair: Boris Kozinsky, Harvard University Room: 601/603 |
Tuesday, March 3, 2020 8:00AM - 8:36AM |
F36.00001: Smart Sampling for Chemical Property Landscapes with BOSS Invited Speaker: Milica Todorovic Atomistic structure search for organic/inorganic heterostructures is made complex by the many degrees of freedom and the need for accurate but costly density-functional theory (DFT) simulations. To accelerate and simplify structure determination in such heterogenous functional materials, we developed the Bayesian Optimization Structure Search (BOSS) approach [1]. |
Tuesday, March 3, 2020 8:36AM - 9:12AM |
F36.00002: Construction and simulation proofs of reliable high-dimensional neural network atomic potentials Invited Speaker: Satoshi Watanabe High-dimensional neural network atomic potentials (HDNNP) [1] has attracted attention because of their potential to achieve reliability and computational efficiency simultaneously. In this talk, we show our attempts to apply the HDNNP to several different topics, and discuss how to construct reliable HDNNP and verify the reliability through simulation proofs. |
Tuesday, March 3, 2020 9:12AM - 9:48AM |
F36.00003: Embedding physics in machine learning potentials Invited Speaker: Albert Bartok The last decade has seen an expansion in machine learning methods applied to atomistic modelling problems. Utilising the flexible functional form allowed by different flavours of machine learning approaches and thanks to the abundance of electronic structure data, it is now possible to fit highly predictive potential energy surfaces. However, transferability of these models is often limited, and predictive accuracy may only be expected in structural domains where the training data is concentrated. In the case of complex materials, where the accessible configurational space is significantly larger, data requirements could be prohibitive, and there is a need to place constrains on the interaction model. In this talk, I will discuss machine learning potentials in a Bayesian framework, and how physical knowledge and intuition can be embedded in the Bayesian prior of the model. |
Tuesday, March 3, 2020 9:48AM - 10:24AM |
F36.00004: Automated training of machine learned potentials with Bayesian active learning Invited Speaker: Jonathan Vandermause Machine learned interatomic potentials are often manually trained and restricted to single-component and nonreactive systems, severely limiting the practical application of these models. We present an adaptive Bayesian inference method for automating the training of multi-element interatomic potentials using structures drawn “on the fly” from molecular dynamics simulations. Within an online active learning algorithm, the internal uncertainty of a Gaussian process (GP) 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 and reoptimize its hyperparameters. Model uncertainties are derived from the variance of the predictive posterior distribution of the GP, which is shown to correlate with true model error on independent test sets. The GP models are based on low-dimensional, explicitly multi-element two- and three-body kernels that can be mapped onto highly efficient cubic spline models suitable for large scale molecular dynamics simulations. Applications to a range of single- and multi-element systems will be discussed, including vacancy and adatom migration in Aluminum, fast-ion diffusion in AgI, and surface segregation in Pd/Ag alloys. |
Tuesday, March 3, 2020 10:24AM - 11:00AM |
F36.00005: Machine-learning interatomic potentials: a story about how a Big Data approach compensates for our incomplete understanding of interatomic interaction Invited Speaker: Alexander Shapeev Machine learning, an approach to create models based on large amounts of data, is transforming many fields of research. This approach allows us to compensate for our incomplete understanding of a phenomenon by incorporating big data into a model. In my talk I will illustrate how this ideology works in the field of models of interatomic interaction. |
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