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The Journal of Physical Chemistry Letters|August 28, 2025
THz Pump Pulse-Driven Temporal Response of Liquid Water Probed by Machine-Learning-Accelerated Non-Equilibrium Molecular DynamicsKit Joll, Philipp SchienbeinThe Journal of Physical Chemistry Letters|January 17, 2025
Mechanism of Fe(II) Chemisorption on Hematite(001) Revealed by Reactive Neural Network Potential Molecular DynamicsKit Joll, Philipp Schienbein, Kevin M Rosso, et al.The Journal of Physical Chemistry. B|March 2, 2026
Transition from Vehicular to Structural Ionic Transport in Electrified Alkali Aqueous SolutionsKit Joll, Philipp Schienbein, Kevin M Rosso, et al.Nature Communications|September 18, 2024
Machine learning the electric field response of condensed phase systems using perturbed neural network potentialsKit Joll, Philipp Schienbein, Kevin M Rosso, et al.Journal of Chemical Theory and Computation|January 25, 2023
Spectroscopy from Machine Learning by Accurately Representing the Atomic Polar TensorPhilipp SchienbeinJournal of Chemical Theory and Computation|April 29, 2026
Mimyria: Machine-Learned Vibrational Spectroscopy for Aqueous Systems Made SimplePhilipp SchienbeinPhysical Review. E|September 27, 2018
Investigation concerning the uniqueness of separatrix lines separating liquidlike from gaslike regimes deep in the supercritical phase of water with a focus on Widom line conceptsPhilipp Schienbein, Dominik MarxChemical Reviews|June 26, 2026
Theoretical Terahertz Spectroscopy of Aqueous Solutions: From Electronic Structure to Molecular UnderstandingPhilipp Schienbein, Dominik MarxPhysical Chemistry Chemical Physics : PCCP|June 15, 2022
Nanosecond solvation dynamics of the hematite/liquid water interface at hybrid DFT accuracy using committee neural network potentialsPhilipp Schienbein, Jochen BlumbergerThe Journal of Physical Chemistry. B|November 8, 2017
Liquid-Vapor Phase Diagram of RPBE-D3 Water: Electronic Properties along the Coexistence Curve and in the Supercritical PhasePhilipp Schienbein, Dominik MarxPageof 3