☆ 4.8 Article

Machine Learning Assisted Simulations of Electrochemical Interfaces: Recent Progress and Challenges

JOURNAL OF PHYSICAL CHEMISTRY LETTERS (2023)

Related references

Note: Only part of the references are listed.

Review Chemistry, Multidisciplinary

Ab Initio Simulations of Water/Metal Interfaces

Axel Gross et al.

Summary: Structures and processes at water/metal interfaces are crucial for electrochemical energy conversion and storage, photoconversion, sensors, and corrosion. They also provide a model system for studying solid-liquid interfaces, combining concepts from chemistry and physics. The interactions between water-water and water-metal are similar in strength, resulting in a competition for the structures at water/metal interfaces. Ab initio molecular dynamics simulations are the preferred method for modeling these interfaces, but they require substantial computational resources.

CHEMICAL REVIEWS (2022)

Add to Collection

Article Chemistry, Physical

A deep potential model with long-range electrostatic interactions

Linfeng Zhang et al.

Summary: Researchers have extended the deep potential model to approximate the long-range electrostatic interaction between ions and valence electrons, improving the accuracy and predictive power of the model.

JOURNAL OF CHEMICAL PHYSICS (2022)

Add to Collection

Article Multidisciplinary Sciences

E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials

Simon Batzner et al.

Summary: This paper introduces an E(3)-equivariant deep learning method for accelerating molecular dynamics simulations. The method shows state-of-the-art accuracy and remarkable sample efficiency in faithfully describing the dynamics of complex systems. The Neural Equivariant Interatomic Potentials (NequIP) approach employs E(3)-equivariant convolutions to interact with geometric tensors, resulting in a more information-rich and faithful representation of atomic environments. NequIP outperforms existing models with significantly fewer training data, challenging the commonly held belief about the necessity of massive training sets for deep neural networks.

NATURE COMMUNICATIONS (2022)

Add to Collection

Article Chemistry, Physical

Interpretable machine learning for knowledge generation in heterogeneous catalysis

Jacques A. Esterhuizen et al.

Summary: Most applications of machine learning in heterogeneous catalysis use black-box models, which are not easily interpretable. Interpretable machine learning methods offer an alternative by merging the predictive capacity of black-box models with the interpretability of physics-based models. This Perspective discusses the potential, challenges, and opportunities of interpretable machine learning in catalysis research.

NATURE CATALYSIS (2022)

Add to Collection

Article Chemistry, Multidisciplinary

A Universal Machine Learning Framework for Electrocatalyst Innovation: A Case Study of Discovering Alloys for Hydrogen Evolution Reaction

Letian Chen et al.

Summary: Efforts have been made to develop efficient electrocatalysts for green hydrogen production, and the introduction of machine learning has brought new opportunities. However, current studies show that the efficiency and accuracy of machine learning in electrocatalyst development are hindered by computational cost and errors from simple physical and chemical properties. This study proposes a universal machine learning framework that combines local machine learning potential (MLP) and graph convolutional neural networks to screen and optimize high-performance electrocatalysts.

ADVANCED FUNCTIONAL MATERIALS (2022)

Add to Collection

Article Chemistry, Physical

Machine Learning: A New Paradigm in Computational Electrocatalysis

Xu Zhang et al.

Summary: The recent applications of machine learning in electrocatalysis offer opportunities to discover promising electrocatalysts, investigate dynamic reaction processes, and extract knowledge from vast data, accelerating knowledge generation in this field.

JOURNAL OF PHYSICAL CHEMISTRY LETTERS (2022)

Add to Collection

Review Chemistry, Applied

Targeted design of advanced electrocatalysts by machine learning

Letian Chen et al.

Summary: Exploring the production and application of clean energy has always been the core of sustainable development. As a clean and sustainable technology, electrocatalysis has gained widespread attention. Efficient, stable, and affordable electrocatalysts are crucial, but the traditional trial and error method is time-consuming and costly. With the advancement of computing power, machine learning has played an important role in electrocatalyst design. This article provides an overview of the application of machine learning in electrocatalyst design and discusses its potential and challenges in the future development of electrocatalysis.

CHINESE JOURNAL OF CATALYSIS (2022)

Add to Collection

Article Chemistry, Physical

Nanosecond solvation dynamics of the hematite/liquid water interface at hybrid DFT accuracy using committee neural network potentials

Philipp Schienbein et al.

Summary: Metal oxide/water interfaces play a crucial role in various fields, but their atomistic structure is challenging to characterize experimentally. This study utilizes a neural network potential to simulate the dynamics at the hematite/water interface, revealing solvation dynamics and diffusion of water molecules.

PHYSICAL CHEMISTRY CHEMICAL PHYSICS (2022)

Add to Collection

Article Chemistry, Physical

Structure and energetics of liquid water-hydroxyl layers on Pt(111)

August E. G. Mikkelsen et al.

Summary: In this study, molecular dynamics simulations with an ensemble of neural network potentials were used to investigate the structure and energetics of liquid water and hydroxyl species on Pt(111) surfaces. The results revealed a gradual increase in differential adsorption energy at high hydroxyl coverages, which was explained by the reduction in hydrogen bonds between the water molecules and hydroxyls.

PHYSICAL CHEMISTRY CHEMICAL PHYSICS (2022)

Add to Collection

Article Chemistry, Physical

When do short-range atomistic machine-learning models fall short?

Shuwen Yue et al.

Summary: This study explores the impact of long-range interactions on atomistic machine-learning models, revealing that while short-range models are sufficient for predicting condensed liquid phases, they fall short in representing cluster and vapor phase properties. The findings provide an improved understanding of the role and necessity of long-range interactions in machine learning models.

JOURNAL OF CHEMICAL PHYSICS (2021)

Add to Collection

Article Multidisciplinary Sciences

A fourth-generation high-dimensional neural network potential with accurate electrostatics including non-local charge transfer

Tsz Wai Ko et al.

Summary: Machine learning potentials have limitations in accounting for long-range charge transfer, which can be overcome with the introduction of a fourth-generation high-dimensional neural network potential that includes non-local charge population information. This method provides forces, charges, and energies in excellent agreement with DFT data, significantly extending the applicability of modern machine learning potentials.

NATURE COMMUNICATIONS (2021)

Add to Collection

Article Chemistry, Physical

Insights into lithium manganese oxide-water interfaces using machine learning potentials

Marco Eckhoff et al.

Summary: The atomistic and electronic structure of solid-liquid interfaces are crucial for designing new materials for various applications. Machine learning-driven simulations of water and lithium manganese oxide interfaces provide insights into these structures with high accuracy and efficiency. Through large-scale molecular dynamics simulations, properties such as water molecule dissociation, proton transfer processes, hydrogen bonds, and the geometric and electronic structure of solid surfaces are investigated.

JOURNAL OF CHEMICAL PHYSICS (2021)

Add to Collection

Article Chemistry, Physical

Is the water/Pt(111) interface ordered at room temperature?

August E. G. Mikkelsen et al.

Summary: The study reveals that the water/Pt(111) interface is characterized by a double layer structure involving a primary strongly bound adsorption layer and a secondary weakly bound adsorption layer. There is effective repulsion between the adsorbed water molecules, resulting in a dynamically changing, semi-ordered interfacial structure.

JOURNAL OF CHEMICAL PHYSICS (2021)

Add to Collection

Article Chemistry, Physical

Hydrogen Coupling on Platinum Using Artificial Neural Network Potentials and DFT

Peter S. Rice et al.

Summary: Understanding reactions at solid-liquid interfaces is challenging, and controlling solvent-substrate interactions is crucial for developing catalysts beyond platinum.

JOURNAL OF PHYSICAL CHEMISTRY LETTERS (2021)

Add to Collection

Review Chemistry, Multidisciplinary

Machine Learning Force Fields

Oliver T. Unke et al.

Summary: The use of machine learning in computational chemistry has led to significant advancements, particularly in the development of machine learning-based force fields to bridge the gap between accuracy and efficiency. The key concept is to learn the statistical relations between chemical structure and potential energy, without preconceived notions of fixed bonds. Challenges remain for the next generation of machine learning force fields.

CHEMICAL REVIEWS (2021)

Add to Collection

Article Chemistry, Physical

Establishment of the Potential of Zero Charge of Metals in Aqueous Solutions: Different Faces of Water Revealed by Ab Initio Molecular Dynamics Simulations

Peng Li et al.

Summary: Metal-water interactions were studied through molecular dynamics simulations on Pt(111) and Au(111) surfaces interfaced with water, revealing that first layer water molecules decrease the metal work function while second layer water molecules increase it. Introducing chemisorbed hydrogen with varying coverages modulates the metal-water interactions.

JOURNAL OF PHYSICAL CHEMISTRY C (2021)

Add to Collection

Review Chemistry, Multidisciplinary

Machine Learning for Chemical Reactions

Markus Meuwly

Summary: Machine learning techniques have a long history in the field of chemical reactions, being able to address complex problems involving both computation and experiments. These techniques can develop models consistent with experimental knowledge, handle problems intractable to conventional approaches, and simulate reactive networks in combustion.

CHEMICAL REVIEWS (2021)

Add to Collection

Article Chemistry, Physical

Machine Learning Force Fields: Recent Advances and Remaining Challenges

Igor Poltavsky et al.

Summary: Machine learning methods in chemistry and physics offer transformative impacts by advancing modeling and enhancing understanding of complex molecules and materials. These methods typically consist of mathematically well-defined procedures, with an increasing number of user-friendly ML packages available in recent years.

JOURNAL OF PHYSICAL CHEMISTRY LETTERS (2021)

Add to Collection

Article Chemistry, Physical

Recent Progress toward Ab Initio Modeling of Electrocatalysis

Jia-Bo Le et al.

Summary: This article summarizes the recent progress in method development for modeling electrochemical interfaces, including different methods for describing the electrolytes at the interfaces and different schemes for charging up the electrode surfaces. It also provides an outlook for future development in modeling methods and their applications to electrocatalysis.

JOURNAL OF PHYSICAL CHEMISTRY LETTERS (2021)

Add to Collection

Article Chemistry, Physical

The Rise of Neural Networks for Materials and Chemical Dynamics

Maksim Kulichenko et al.

Summary: Machine learning is rapidly becoming a key tool for modeling chemical processes and materials, especially with the development of ML-based force fields for interatomic potentials. Designing high-quality training data sets is crucial for model accuracy, with strategies like active learning and using high levels of quantum theory to enhance training. Transfer learning allows for training on mixed fidelity data sets, showing significant improvements in model accuracy.

JOURNAL OF PHYSICAL CHEMISTRY LETTERS (2021)

Add to Collection

Article Multidisciplinary Sciences

Machine learning potentials for complex aqueous made

Christoph Schran et al.

Summary: The study introduces a machine learning framework for developing and validating models for complex aqueous systems efficiently through a data-driven active learning protocol. The approach is applied to diverse aqueous systems and evaluated with an automated validation protocol for accuracy and precision of force prediction.

PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2021)

Add to Collection

Article Materials Science, Multidisciplinary

Artificial Intelligence and QM/MM with a Polarizable Reactive Force Field for Next-Generation Electrocatalysts

Saber Naserifar et al.

Summary: In order to develop high-performance electrocatalysts, the combination of the accurate RexPoN reactive force field and QM enables reactive simulations with QM accuracy in the presence of explicit solvent. By incorporating machine learning to predict the performance of surface sites, it provides an accurate and practical way to design high-performance electrocatalysts.

MATTER (2021)

Add to Collection

Article Chemistry, Multidisciplinary

Double Layer of Pt(111)-Aqueous Electrolyte Interface: Potential of Zero Charge and Anomalous Gouy-Chapman Screening

Kasinath Ojha et al.

ANGEWANDTE CHEMIE-INTERNATIONAL EDITION (2020)

Add to Collection

Article Chemistry, Physical

Reconciling Work Functions and Adsorption Enthalpies for Implicit Solvent Models: A Pt (111)/Water Interface Case Study

Gabriel Bramley et al.

JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2020)

Add to Collection

Article Chemistry, Physical

Committee neural network potentials control generalization errors and enable active learning

Christoph Schran et al.

JOURNAL OF CHEMICAL PHYSICS (2020)

Add to Collection

Article Chemistry, Multidisciplinary

Machine Learning in Computational Surface Science and Catalysis: Case Studies on Water and Metal-Oxide Interfaces

Xiaoke Li et al.

FRONTIERS IN CHEMISTRY (2020)

Add to Collection

Article Chemistry, Physical

Improved DFT Adsorption Energies with Semiempirical Dispersion Corrections

David Mahlberg et al.

JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2019)

Add to Collection

Article Chemistry, Physical

Incorporating long-range physics in atomic-scale machine learning

Andrea Grisafi et al.

JOURNAL OF CHEMICAL PHYSICS (2019)

Add to Collection

Article Chemistry, Physical

Structure and Dynamics of the Liquid-Water/Zinc-Oxide Interface from Machine Learning Potential Simulations

Vanessa Quaranta et al.

JOURNAL OF PHYSICAL CHEMISTRY C (2019)

Add to Collection

Article Chemistry, Multidisciplinary

One-dimensional vs. two-dimensional proton transport processes at solid-liquid zinc-oxide-water interfaces

Matti Hellstroem et al.

CHEMICAL SCIENCE (2019)

Add to Collection

Article Chemistry, Physical

Maximally resolved anharmonic OH vibrational spectrum of the water/ZnO(10(1)over-bar0) interface from a high-dimensional neural network potential

Vanessa Quaranta et al.

JOURNAL OF CHEMICAL PHYSICS (2018)

Add to Collection

Article Chemistry, Physical

Machine learning hydrogen adsorption on nanoclusters through structural descriptors

Marc O. J. Jager et al.

NPJ COMPUTATIONAL MATERIALS (2018)

Add to Collection

Article Chemistry, Physical

The electric double layer at metal-water interfaces revisited based on a charge polarization scheme

Sung Sakong et al.

JOURNAL OF CHEMICAL PHYSICS (2018)

Add to Collection

Article Chemistry, Physical

Self-Diffusion of Surface Defects at Copper-Water Interfaces

Suresh Kondati Natarajan et al.

JOURNAL OF PHYSICAL CHEMISTRY C (2017)

Add to Collection

Article Chemistry, Physical

Proton-Transfer Mechanisms at the Water-ZnO Interface: The Role of Presolvation

Vanessa Quaranta et al.

JOURNAL OF PHYSICAL CHEMISTRY LETTERS (2017)

Add to Collection

Article Physics, Multidisciplinary

Determining Potentials of Zero Charge of Metal Electrodes versus the Standard Hydrogen Electrode from Density-Functional-Theory-Based Molecular Dynamics

Jiabo Le et al.

PHYSICAL REVIEW LETTERS (2017)

Add to Collection

Review Chemistry, Multidisciplinary

Nuclear Quantum Effects in Water and Aqueous Systems: Experiment, Theory, and Current Challenges

Michele Ceriotti et al.

CHEMICAL REVIEWS (2016)

Add to Collection

Review Chemistry, Multidisciplinary

Water at Interfaces

E. Bjornehohn et al.

CHEMICAL REVIEWS (2016)

Add to Collection

Article Chemistry, Physical

Solvent-Induced Proton Hopping at a Water-Oxide Interface

Gabriele Tocci et al.

JOURNAL OF PHYSICAL CHEMISTRY LETTERS (2014)

Add to Collection

Article Materials Science, Multidisciplinary

On representing chemical environments

Albert P. Bartok et al.

PHYSICAL REVIEW B (2013)

Add to Collection

Review Chemistry, Physical

A molecular perspective of water at metal interfaces

Javier Carrasco et al.

NATURE MATERIALS (2012)

Add to Collection

Article Chemistry, Physical

Alignment of electronic energy levels at electrochemical interfaces

Jun Cheng et al.

PHYSICAL CHEMISTRY CHEMICAL PHYSICS (2012)

Add to Collection

Article Chemistry, Physical

Atom-centered symmetry functions for constructing high-dimensional neural network potentials

Joerg Behler

JOURNAL OF CHEMICAL PHYSICS (2011)

Add to Collection

Article Chemistry, Physical

Hydroxylation Structure and Proton Transfer Reactivity at the Zinc Oxide-Water Interface

David Raymand et al.

JOURNAL OF PHYSICAL CHEMISTRY C (2011)

Add to Collection

Article Materials Science, Multidisciplinary

Aligning electronic energy levels at the TiO2/H2O interface

Jun Cheng et al.

PHYSICAL REVIEW B (2010)

Add to Collection

Article Physics, Multidisciplinary

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

Albert P. Bartok et al.

PHYSICAL REVIEW LETTERS (2010)

Add to Collection

Article Chemistry, Physical

Insight from first principles into the nature of the bonding between water molecules and 4d metal surfaces

Javier Carrasco et al.

JOURNAL OF CHEMICAL PHYSICS (2009)

Add to Collection

Article Physics, Multidisciplinary

Properties of metal-water interfaces studied from first principles

Sebastian Schnur et al.

NEW JOURNAL OF PHYSICS (2009)

Add to Collection

Review Chemistry, Physical

Water adsorption and the wetting of metal surfaces

A. Hodgson et al.

SURFACE SCIENCE REPORTS (2009)

Add to Collection

Article Chemistry, Physical

On the residence time for water in a solute hydration shell: Application to aqueous halide solutions

Damien Laage et al.

JOURNAL OF PHYSICAL CHEMISTRY B (2008)

Add to Collection

Article Physics, Multidisciplinary

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

Joerg Behler et al.

PHYSICAL REVIEW LETTERS (2007)

Add to Collection

Article Materials Science, Multidisciplinary

Density functional theory simulations of water-metal interfaces: waltzing waters, a novel 2D ice phase, and more

A. Michaelides

APPLIED PHYSICS A-MATERIALS SCIENCE & PROCESSING (2006)

Add to Collection

Article Chemistry, Physical

Measurement of the surface charge density of CO-saturated Pt(111) electrodes as a function of potential: the potential of zero charge of Pt(111)

A Cuesta

SURFACE SCIENCE (2004)

Add to Collection

Article Physics, Multidisciplinary

Structure and bonding of water on Pt(111)

H Ogasawara et al.

PHYSICAL REVIEW LETTERS (2002)

Add to Collection

Article Chemistry, Physical

Growth of thin crystalline ice films on Pt(111)

S Haq et al.

SURFACE SCIENCE (2002)

Add to Collection

Article Chemistry, Physical

Ab initio molecular dynamics simulation of the Ag(111)-water interface

S Izvekov et al.

JOURNAL OF CHEMICAL PHYSICS (2001)

Add to Collection

Article Chemistry, Physical

Ab initio molecular dynamics simulation of the Cu(110)-water interface

S Izvekov et al.

JOURNAL OF CHEMICAL PHYSICS (2001)

Add to Collection

© Peeref 2019-2024. All rights reserved.