We review the materials science applications of the nested sampling (NS) method, which was originally conceived for calculating the evidence in Bayesian inference. We describe how NS can be adapted to sample the potential energy surface (PES) of atomistic systems, providing a straightforward approximation for the partition function and allowing the evaluation of thermodynamic variables at arbitrary temperatures. After an overview of the basic method, we describe a number of extensions, including using variable cells for constant pressure sampling, the semi-grand-canonical approach for multicomponent systems, parallelizing the algorithm, and visualizing the results. We cover the range of materials applications of NS from the past decade, fro...
AbstractNested sampling is a Bayesian sampling technique developed to explore probability distributi...
The theoretical analysis of many problems in physics, astronomy, and applied mathematics requires an...
In this thesis, we extend the scope of atomistic simulations through a combination of machine learni...
Abstract: We review the materials science applications of the nested sampling (NS) method, which was...
The nested sampling (NS) method was originally proposed by John Skilling to calculate the evidence i...
We describe a method to explore the configurational phase space of chemical systems. It is based on ...
We extend the nested sampling algorithm to simulate materials under periodic boundary and constant p...
We describe a method to explore the configurational phase space of chemical systems. It is based on ...
The recently introduced nested sampling algorithm allows the direct and efficient calculation of the...
The recently introduced nested sampling algorithm allows the direct and efficient calculation of the...
The nested sampling algorithm has been shown to be a general method for calculating the pressure-tem...
© 2016 Elsevier B.V. All rights reserved. Nested Sampling (NS) is a parameter space sampling algorit...
Nested sampling is a Bayesian sampling technique developed to explore probability distributions loca...
We report an embarrassingly parallel method for the evaluation of thermodynaproperties over an energ...
AbstractCalculating thermodynamic potentials and observables efficiently and accurately is key for t...
AbstractNested sampling is a Bayesian sampling technique developed to explore probability distributi...
The theoretical analysis of many problems in physics, astronomy, and applied mathematics requires an...
In this thesis, we extend the scope of atomistic simulations through a combination of machine learni...
Abstract: We review the materials science applications of the nested sampling (NS) method, which was...
The nested sampling (NS) method was originally proposed by John Skilling to calculate the evidence i...
We describe a method to explore the configurational phase space of chemical systems. It is based on ...
We extend the nested sampling algorithm to simulate materials under periodic boundary and constant p...
We describe a method to explore the configurational phase space of chemical systems. It is based on ...
The recently introduced nested sampling algorithm allows the direct and efficient calculation of the...
The recently introduced nested sampling algorithm allows the direct and efficient calculation of the...
The nested sampling algorithm has been shown to be a general method for calculating the pressure-tem...
© 2016 Elsevier B.V. All rights reserved. Nested Sampling (NS) is a parameter space sampling algorit...
Nested sampling is a Bayesian sampling technique developed to explore probability distributions loca...
We report an embarrassingly parallel method for the evaluation of thermodynaproperties over an energ...
AbstractCalculating thermodynamic potentials and observables efficiently and accurately is key for t...
AbstractNested sampling is a Bayesian sampling technique developed to explore probability distributi...
The theoretical analysis of many problems in physics, astronomy, and applied mathematics requires an...
In this thesis, we extend the scope of atomistic simulations through a combination of machine learni...