64 citations · 88 across the 8 of their papers we have counts for
10 papers
Unconstrained learning of equivariant atomistic properties
Joseph W. Abbott, Paolo Pegolo, Pol Febrer +6
Machine-learned interatomic potentials based on graph neural networks (GNNs) have made first-principles accuracy routine for the potential energy surface (PES). Their backbones, of…
Electrolyte density, diffusivity and conductivity in graphene nanoconfinement predicted by separating interfacial from genuine confinement effects
Haoyuan Quan, Hanne S. Antila, Maximilian R. Becker +2
Confined aqueous electrolytes exhibit strong deviations from bulk behaviour, but it remains unclear which changes arise from genuine confinement-induced modification and which mere…
Combined effects of evaporation, sedimentation and solute crystallization on the dynamics of aerosol size distributions on multiple length and time scales
Sina Zendehroud, Ole Kleinjung, Philip Loche +5
We investigate three aspects of aerosol-mediated air-borne viral infection mechanisms on different length and time scales. First, we address the evolution of the size distribution…
Static Dielectric Permittivity Profiles and Coarse-graining Approaches for Water in Graphene Slit Pores
Philipp Stärk, Henrik Stooß, Philip Loche +3
The dielectric response of nano-confined fluids is crucial across technologies and biological systems, yet its calculation and interpretation from molecular simulations are often m…
Water cavitation results from the kinetic competition of bulk, surface and surface-defect nucleation events
Philip Loche, Matej Kanduč, Emanuel Schneck +1
Water at negative pressures can remain in a metastable state for a surprisingly long time before it reaches equilibrium by cavitation, i.e. by the formation of vapor bubbles. The w…
Prediction rigidities for data-driven chemistry
Sanggyu Chong, Filippo Bigi, Federico Grasselli +3
The widespread application of machine learning (ML) to the chemical sciences is making it very important to understand how the ML models learn to correlate chemical structures with…
Physics-inspired Equivariant Descriptors of Non-bonded Interactions
Kevin K. Huguenin-Dumittan, Philip Loche, Ni Haoran +1
One essential ingredient in many machine learning (ML) based methods for atomistic modeling of materials and molecules is the use of locality. While allowing better system-size sca…
Dielectric properties of aqueous electrolytes at the nanoscale
Maximilian R. Becker, Philip Loche, Douwe Jan Bonthuis +3
Despite the ubiquity of aqueous electrolytes, the effect of salt on water organization remains controversial. We introduce a nonlocal and nonlinear field theory for the nanoscale p…
Energy Transfer within the Hydrogen Bonding Network of Water Following Resonant Terahertz Excitation
Hossam Elgabarty, Tobias Kampfrath, Douwe Jan Bonthuis +7
Energy dissipation in water is very fast and more efficient than in many other liquids. This behavior is commonly attributed to the intermolecular interactions associated with hydr…
Exploring the Absorption Spectrum of Simulated Water from MHz to the Infrared
Shane Carlson, Florian N. Brünig, Philip Loche +2
Absorption spectra of liquid water at 300 K are calculated from both classical and density functional theory molecular dynamics simulation data, which together span from 1 MHz to h…