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 …

High-entropy high-hardness metal carbides discovered by entropy descriptors

P Sarker, T Harrington, C Toher, C Oses… - Nature …, 2018 - nature.com
High-entropy materials have attracted considerable interest due to the combination of useful
properties and promising applications. Predicting their formation remains the major …

[HTML][HTML] Phase diagrams—Why they matter and how to predict them

PY Chew, A Reinhardt - The Journal of Chemical Physics, 2023 - pubs.aip.org
Understanding the thermodynamic stability and metastability of materials can help us to, for
example, gauge whether crystalline polymorphs in pharmaceutical formulations are likely to …

Hyperactive learning for data-driven interatomic potentials

C van der Oord, M Sachs, DP Kovács… - npj Computational …, 2023 - nature.com
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 …

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 …

Machine-learned interatomic potentials for alloys and alloy phase diagrams

CW Rosenbrock, K Gubaev, AV Shapeev… - npj Computational …, 2021 - nature.com
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 …

First-principles atomistic thermodynamics and configurational entropy

C Sutton, SV Levchenko - Frontiers in Chemistry, 2020 - frontiersin.org
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 …

[HTML][HTML] A general-purpose machine learning Pt interatomic potential for an accurate description of bulk, surfaces, and nanoparticles

J Kloppenburg, LB Pártay, H Jónsson… - The Journal of chemical …, 2023 - pubs.aip.org
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 …

Exploring the configuration space of elemental carbon with empirical and machine learned interatomic potentials

GA Marchant, MA Caro, B Karasulu… - npj Computational …, 2023 - nature.com
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 …

Extracting crystal chemistry from amorphous carbon structures

VL Deringer, G Csányi, DM Proserpio - ChemPhysChem, 2017 - Wiley Online Library
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 …