Nested sampling for physical scientists
G Ashton, N Bernstein, J Buchner, X Chen… - Nature Reviews …, 2022 - nature.com
Abstract This Primer examines Skilling's nested sampling algorithm for Bayesian inference
and, more broadly, multidimensional integration. The principles of nested sampling are …
and, more broadly, multidimensional integration. The principles of nested sampling are …
High-entropy high-hardness metal carbides discovered by entropy descriptors
High-entropy materials have attracted considerable interest due to the combination of useful
properties and promising applications. Predicting their formation remains the major …
properties and promising applications. Predicting their formation remains the major …
[HTML][HTML] Phase diagrams—Why they matter and how to predict them
Understanding the thermodynamic stability and metastability of materials can help us to, for
example, gauge whether crystalline polymorphs in pharmaceutical formulations are likely to …
example, gauge whether crystalline polymorphs in pharmaceutical formulations are likely to …
Hyperactive learning for data-driven interatomic potentials
Data-driven interatomic potentials have emerged as a powerful tool for approximating ab
initio potential energy surfaces. The most time-consuming step in creating these interatomic …
initio potential energy surfaces. The most time-consuming step in creating these interatomic …
Nested sampling methods
J Buchner - Statistic Surveys, 2023 - projecteuclid.org
Nested sampling (NS) computes parameter posterior distributions and makes Bayesian
model comparison computationally feasible. Its strengths are the unsupervised navigation of …
model comparison computationally feasible. Its strengths are the unsupervised navigation of …
Machine-learned interatomic potentials for alloys and alloy phase diagrams
We introduce machine-learned potentials for Ag-Pd to describe the energy of alloy
configurations over a wide range of compositions. We compare two different approaches …
configurations over a wide range of compositions. We compare two different approaches …
First-principles atomistic thermodynamics and configurational entropy
In most applications, functional materials operate at finite temperatures and are in contact
with a reservoir of atoms or molecules (gas, liquid, or solid). In order to understand the …
with a reservoir of atoms or molecules (gas, liquid, or solid). In order to understand the …
[HTML][HTML] A general-purpose machine learning Pt interatomic potential for an accurate description of bulk, surfaces, and nanoparticles
A Gaussian approximation machine learning interatomic potential for platinum is presented.
It has been trained on density-functional theory (DFT) data computed for bulk, surfaces, and …
It has been trained on density-functional theory (DFT) data computed for bulk, surfaces, and …
Exploring the configuration space of elemental carbon with empirical and machine learned interatomic potentials
We demonstrate how the many-body potential energy landscape of carbon can be explored
with the nested sampling algorithm, allowing for the calculation of its pressure-temperature …
with the nested sampling algorithm, allowing for the calculation of its pressure-temperature …
Extracting crystal chemistry from amorphous carbon structures
Carbon allotropes have been explored intensively by ab initio crystal structure prediction,
but such methods are limited by the large computational cost of the underlying density …
but such methods are limited by the large computational cost of the underlying density …