4.3 Article

Development of a machine-learning-based ionic-force correction model for quantum molecular dynamic simulations of warm dense matter

Related references

Note: Only part of the references are listed.
Article Materials Science, Multidisciplinary

Deep dive into machine learning density functional theory for materials science and chemistry

L. Fiedler et al.

Summary: With the growth of computational resources, machine learning has gained traction in electronic structure simulations, offering the potential to reduce resource requirements and accelerate materials discovery and chemical reaction pathway research.

PHYSICAL REVIEW MATERIALS (2022)

Review Chemistry, Multidisciplinary

Physics-Inspired Structural Representations for Molecules and Materials

Felix Musil et al.

Summary: This review summarizes the current understanding of the nature and characteristics of the most commonly used structural and chemical descriptions of atomistic structures, emphasizing the deep underlying connections between different frameworks that lead to computationally efficient and universally applicable models. It provides examples of recent applications to a diverse set of chemical and materials science problems, and outlines the open questions and the most promising research directions in the field.

CHEMICAL REVIEWS (2021)

Article Computer Science, Interdisciplinary Applications

mpi4py: Status Update After 12 Years of Development

Lisandro Dalcin et al.

Summary: MPI for Python (mpi4py) has become the most popular Python binding tool for the message passing interface (MPI), featuring support for various specifications and features, such as MPI-3.1 and CUDA-aware MPI implementations.

COMPUTING IN SCIENCE & ENGINEERING (2021)

Review Chemistry, Physical

Machine-learned potentials for next-generation matter simulations

Pascal Friederich et al.

Summary: This paper discusses how machine-learned potentials break the limitations of system-size or accuracy, how active-learning will aid their development, how they are applied, and how they may become a more widely used approach.

NATURE MATERIALS (2021)

Article Materials Science, Multidisciplinary

Improved first-principles equation-of-state table of deuterium for high-energy-density applications

D. Mihaylov et al.

Summary: This study presents a new first-principles equation-of-state (EOS) table of deuterium, iFPEOS, which introduces a universal density functional theory, consistent treatment of exchange-correlation thermal effects, and quantum treatment of ions. The results show improved agreement with experimental data compared to other first-principles EOS models in the challenging warm dense matter (WDM) regime.

PHYSICAL REVIEW B (2021)

Article Chemistry, Physical

Machine Learning for Accurate Force Calculations in Molecular Dynamics Simulations

Punyaslok Pattnaik et al.

JOURNAL OF PHYSICAL CHEMISTRY A (2020)

Article Multidisciplinary Sciences

Evidence for supercritical behaviour of high-pressure liquid hydrogen

Bingqing Cheng et al.

NATURE (2020)

Review Multidisciplinary Sciences

Array programming with NumPy

Charles R. Harris et al.

NATURE (2020)

Article Multidisciplinary Sciences

Signatures of a liquid-liquid transition in an ab initio deep neural network model for water

Thomas E. Gartner et al.

PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2020)

Article Materials Science, Multidisciplinary

Self-learning hybrid Monte Carlo: A first-principles approach

Yuki Nagai et al.

PHYSICAL REVIEW B (2020)

Article Physics, Multidisciplinary

Machine Learning Forces Trained by Gaussian Process in Liquid States: Transferability to Temperature and Pressure

Ryo Tamura et al.

JOURNAL OF THE PHYSICAL SOCIETY OF JAPAN (2019)

Article Chemistry, Multidisciplinary

Machine Learning Interatomic Potentials as Emerging Tools for Materials Science

Volker L. Deringer et al.

ADVANCED MATERIALS (2019)

Article Chemistry, Physical

Automatic selection of atomic fingerprints and reference configurations for machine-learning potentials

Giulio Imbalzano et al.

JOURNAL OF CHEMICAL PHYSICS (2018)

Article Materials Science, Multidisciplinary

Deorbitalized meta-GGA exchange-correlation functionals in solids

Daniel Mejia-Rodriguez et al.

PHYSICAL REVIEW B (2018)

Article Materials Science, Multidisciplinary

Size and temperature transferability of direct and local deep neural networks for atomic forces

Natalia Kuritz et al.

PHYSICAL REVIEW B (2018)

Article Chemistry, Physical

Machine learning for atomic forces in a crystalline solid: Transferability to various temperatures

Teppei Suzuki et al.

INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY (2017)

Article Chemistry, Physical

Machine Learning Force Fields: Construction, Validation, and Outlook

V. Botu et al.

JOURNAL OF PHYSICAL CHEMISTRY C (2017)

Article Physics, Condensed Matter

Advanced capabilities for materials modelling with QUANTUM ESPRESSO

P. Giannozzi et al.

JOURNAL OF PHYSICS-CONDENSED MATTER (2017)

Article Chemistry, Physical

Addressing uncertainty in atomistic machine learning

Andrew A. Peterson et al.

PHYSICAL CHEMISTRY CHEMICAL PHYSICS (2017)

Article Chemistry, Physical

A universal strategy for the creation of machine learning-based atomistic force fields

Tran Doan Huan et al.

NPJ COMPUTATIONAL MATERIALS (2017)

Article Materials Science, Multidisciplinary

Efficient and accurate machine-learning interpolation of atomic energies in compositions with many species

Nongnuch Artrith et al.

PHYSICAL REVIEW B (2017)

Article Computer Science, Interdisciplinary Applications

Introducing PROFESS 3.0: An advanced program for orbital-free density functional theory molecular dynamics simulations

Mohan Chen et al.

COMPUTER PHYSICS COMMUNICATIONS (2015)

Article Chemistry, Physical

Big Data Meets Quantum Chemistry Approximations: The Δ-Machine Learning Approach

Raghunathan Ramakrishnan et al.

JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2015)

Article Physics, Multidisciplinary

Molecular Dynamics with On-the-Fly Machine Learning of Quantum-Mechanical Forces

Zhenwei Li et al.

PHYSICAL REVIEW LETTERS (2015)

Article Computer Science, Interdisciplinary Applications

Finite-temperature orbital-free DFT molecular dynamics: Coupling PROFESS and QUANTUM ESPRESSO

Valentin V. Karasiev et al.

COMPUTER PHYSICS COMMUNICATIONS (2014)

Review Physics, Condensed Matter

Representing potential energy surfaces by high-dimensional neural network potentials

J. Behler

JOURNAL OF PHYSICS-CONDENSED MATTER (2014)

Review Chemistry, Physical

DFT in a nutshell

Kieron Burke et al.

INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY (2013)

Correction Chemistry, Physical

DFT in a nutshell (vol 113, pg 96, 2013)

Kieron Burke et al.

INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY (2013)

Article Materials Science, Multidisciplinary

Nonempirical generalized gradient approximation free-energy functional for orbital-free simulations

Valentin V. Karasiev et al.

PHYSICAL REVIEW B (2013)

Article Materials Science, Multidisciplinary

On representing chemical environments

Albert P. Bartok et al.

PHYSICAL REVIEW B (2013)

Article Materials Science, Multidisciplinary

Generalized-gradient-approximation noninteracting free-energy functionals for orbital-free density functional calculations

Valentin V. Karasiev et al.

PHYSICAL REVIEW B (2012)

Article Water Resources

Parallel distributed computing using Python

Lisandro D. Dalcin et al.

ADVANCES IN WATER RESOURCES (2011)

Review Physics, Condensed Matter

QUANTUM ESPRESSO: a modular and open-source software project for quantum simulations of materials

Paolo Giannozzi et al.

JOURNAL OF PHYSICS-CONDENSED MATTER (2009)

Article Computer Science, Interdisciplinary Applications

Introducing PROFESS: A new program for orbital-free density functional theory calculations

Gregory S. Ho et al.

COMPUTER PHYSICS COMMUNICATIONS (2008)

Article Computer Science, Theory & Methods

MPI for Python:: Performance improvements and MPI-2 extensions

Lisandro Dalcin et al.

JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING (2008)

Article Physics, Multidisciplinary

Generalized neural-network representation of high-dimensional potential-energy surfaces

Joerg Behler et al.

PHYSICAL REVIEW LETTERS (2007)

Article Computer Science, Interdisciplinary Applications

Born-Oppenheimer interatomic forces from simple, local kinetic energy density functionals

V. V. Karasiev et al.

JOURNAL OF COMPUTER-AIDED MATERIALS DESIGN (2006)

Article Chemistry, Physical

Calculation of pressure in case of periodic boundary conditions

MJ Louwerse et al.

CHEMICAL PHYSICS LETTERS (2006)

Article Computer Science, Theory & Methods

MPI for Python

L Dalcín et al.

JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING (2005)