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Main Authors:	Kocer, Emir, Haouari, Redouan El, Dellago, Christoph, Behler, Jörg
Format:	Preprint
Published:	2024
Subjects:	Chemical Physics
Online Access:	https://arxiv.org/abs/2410.03299
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Perspective: Atomistic Simulations of Water and Aqueous Systems with Machine Learning Potentials
by: Omranpour, Amir, et al.
Published: (2024)

Ab initio thermodynamics of liquid and solid water
by: Cheng, Bingqing, et al.
Published: (2018)

Random sampling versus active learning algorithms for machine learning potentials of quantum liquid water
by: Stolte, Nore, et al.
Published: (2024)

Molecular Hessian matrices from a machine learning random forest regression algorithm
by: Domenichini, Giorgio, et al.
Published: (2023)

Free energy profiles for chemical reactions in solution from high-dimensional neural network potentials: The case of the Strecker synthesis
by: Tokita, Alea Miako, et al.
Published: (2025)

Molecular Dynamics Simulations of $γ$-Belite(010)-Water Interfaces with High-Dimensional Neural Network Potentials
by: Prus, Bernadeta, et al.
Published: (2025)

Insights into the Structure and Dynamics of Water at Co$_3$O$_4$(001) Using a High-Dimensional Neural Network Potential
by: Omranpour, Amir, et al.
Published: (2025)

Atomistic Simulations of Oxide-Water Interfaces using Machine Learning Potentials
by: Elsner, Jan, et al.
Published: (2025)

Unsupervised identification of crystal defects from atomistic potential descriptors
by: Kývala, Lukáš, et al.
Published: (2024)

Machine Learning Potentials for Heterogeneous Catalysis
by: Omranpour, Amir, et al.
Published: (2024)

Iterative charge equilibration for fourth-generation high-dimensional neural network potentials
by: Kocer, Emir, et al.
Published: (2025)

Benchmarking foundation potentials against quantum chemistry methods for predicting molecular redox potentials
by: Chen, Yicheng, et al.
Published: (2025)

Computation of the heat capacity of water from first principles
by: Shiga, Motoyuki, et al.
Published: (2025)

Accuracy of Charge Densities in Electronic Structure Calculations
by: Gubler, Moritz, et al.
Published: (2024)

Hydrogen Bond Topology Reveals Layering of LDL-like and HDL-like Water at its Liquid/Vapor Interface
by: Jedlovszky, Pal, et al.
Published: (2025)

Nuclear Quantum Effects in Liquid Water Are Negligible for Structure but Significant for Dynamics
by: Stolte, Nore, et al.
Published: (2024)

Absolute standard hydrogen electrode potential and redox potentials of atoms and molecules: machine learning aided first principles calculations
by: Jinnouchi, Ryosuke, et al.
Published: (2024)

Structure and dynamics of the magnetite(001)/water interface from molecular dynamics simulations based on a neural network potential
by: Romano, Salvatore, et al.
Published: (2024)

Electrochemical CO2 capture with pH-independent redox chemistry
by: Kim, Sang Cheol, et al.
Published: (2025)

Comparing the Mechanical and Thermodynamic Definitions of Pressure in Ice Nucleation
by: de Hijes, Pablo Montero, et al.
Published: (2025)

NATPS: Nonadiabatic Transition Path Sampling Using Time-Reversible MASH Dynamics
by: Yang, Xiran, et al.
Published: (2026)

Introduction to machine learning potentials for atomistic simulations
by: Thiemann, Fabian L., et al.
Published: (2024)

Machine learning electronic structure and atomistic properties from the external potential
by: Nigam, Jigyasa, et al.
Published: (2026)

Machine learned potential for defected single layer hexagonal boron nitride
by: Janisch, John, et al.
Published: (2025)

Structure of the water/magnetite interface from sum frequency generation experiments and neural network based molecular dynamics simulations
by: Romano, Salvatore, et al.
Published: (2024)

Machine learning interatomic potential can infer electrical response
by: Zhong, Peichen, et al.
Published: (2025)

Oxygen redox in battery cathodes: A brief overview
by: Assadi, M. Hussein N., et al.
Published: (2024)

Surface potentials of conductors in electrolyte solutions
by: Vinogradova, Olga I., et al.
Published: (2024)

Mapping electrostatic potential in electrolyte solution
by: Huang, Bo, et al.
Published: (2023)

Leveraging active learning-enhanced machine-learned interatomic potential for efficient infrared spectra prediction
by: Bhatia, Nitik, et al.
Published: (2025)

Looping metal-support interaction in heterogeneous catalysts during redox reactions
by: Pan, Yue, et al.
Published: (2025)

Thawed Gaussian wavepacket dynamics with $Δ$-machine learned potentials
by: Gherib, Rami, et al.
Published: (2024)

Benchmarking of machine learning interatomic potentials for reactive hydrogen dynamics at metal surfaces
by: Stark, Wojciech G., et al.
Published: (2024)

Monomeric machine learning potential for general covalent molecules: linear alkanes as an example
by: Li, Xinze, et al.
Published: (2026)

Low-rank matrix and tensor approximations for compression of machine-learning interatomic potentials
by: Vorotnikov, Igor, et al.
Published: (2025)

Accurate nuclear quantum statistics on machine-learned classical effective potentials
by: Zaporozhets, Iryna, et al.
Published: (2024)

Unsymmetrical synthesis of benzimidazole-fused naphthalene imides with panchromatic absorption and redox activity
by: Lin, Guan-Ru, et al.
Published: (2025)

Machine learning surrogate models for particle insertions and element substitutions
by: Jinnouchi, Ryosuke
Published: (2024)

Exploring the design space of machine-learning models for quantum chemistry with a fully differentiable framework
by: Suman, Divya, et al.
Published: (2025)

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