Revisiting the structure, interaction, and dynamical property of ionic liquid from the deep learning force field

Dissolving salt is not equivalent to applying a pressure on water

(2022) Chunyi Zhang et al.   Nature Communications

Applying Classical, Ab Initio, and Machine-Learning Molecular Dynamics Simulations to the Liquid Electrolyte for Rechargeable Batteries

(2022) Nan Yao et al.   CHEMICAL REVIEWS

Machine Learning Force Fields

(2021) Oliver T. Unke et al.   CHEMICAL REVIEWS

In Situ Active Site for CO Activation in Fe-Catalyzed Fischer–Tropsch Synthesis from Machine Learning

(2021) Qian-Yu Liu et al.   JOURNAL OF THE AMERICAN CHEMICAL SOCIETY

Machine-learned potentials for next-generation matter simulations

(2021) Pascal Friederich et al.   NATURE MATERIALS

A Differentiable Neural-Network Force Field for Ionic Liquids

(2021) Hadrián Montes-Campos et al.   Journal of Chemical Information and Modeling

DP-GEN: A concurrent learning platform for the generation of reliable deep learning based potential energy models

(2020) Yuzhi Zhang et al.   COMPUTER PHYSICS COMMUNICATIONS

Active learning of uniformly accurate interatomic potentials for materials simulation

(2019) Linfeng Zhang et al.   PHYSICAL REVIEW MATERIALS

Comparison of ionic liquid electrolyte to aqueous electrolytes on carbon nanofibres supercapacitor electrode derived from oxygen-functionalized graphene

(2019) Kabir O. Oyedotun et al.   CHEMICAL ENGINEERING JOURNAL

Thermal rectification of graphene on substrates with inhomogeneous stiffness

(2019) Ning Wei et al.   CARBON

DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics

(2018) Han Wang et al.   COMPUTER PHYSICS COMMUNICATIONS

The confined [Bmim][BF4] ionic liquid flow through graphene oxide nanochannels: a molecular dynamics study

(2018) Yanlei Wang et al.   PHYSICAL CHEMISTRY CHEMICAL PHYSICS

Machine learning molecular dynamics for the simulation of infrared spectra

(2017) Michael Gastegger et al.   Chemical Science

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

(2017) Tran Doan Huan et al.   npj Computational Materials

Perspective: Machine learning potentials for atomistic simulations

(2016) Jörg Behler   JOURNAL OF CHEMICAL PHYSICS

Pore chemistry and size control in hybrid porous materials for acetylene capture from ethylene

(2016) X. Cui et al.   SCIENCE

Long-range electrostatic screening in ionic liquids

(2015) Matthew A. Gebbie et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

A new class of ion-ion interaction: Z-bond

(2014) Kun Dong et al.   Science China-Chemistry

The critical power to maintain thermally stable molecular junctions

(2014) Yanlei Wang et al.   Nature Communications

Classical atomistic simulations of surfaces and heterogeneous interfaces with the charge-optimized many body (COMB) potentials

(2013) Tao Liang et al.   MATERIALS SCIENCE & ENGINEERING R-REPORTS

Lithium-ion batteries. A look into the future

(2011) Bruno Scrosati et al.   Energy & Environmental Science

High-Performance Supercapacitors Based on Poly(ionic liquid)-Modified Graphene Electrodes

(2010) Tae Young Kim et al.   ACS Nano

Gaussian Approximation Potentials: The Accuracy of Quantum Mechanics, without the Electrons

(2010) Albert P. Bartók et al.   PHYSICAL REVIEW LETTERS

Ionic liquids as electrolytes for Li-ion batteries—An overview of electrochemical studies

(2009) Andrzej Lewandowski et al.   JOURNAL OF POWER SOURCES