274 citations · 1.2k across the 26 of their papers we have counts for
23 papers · 1 filter
CP2K: An electronic structure and molecular dynamics software package - Dynamics, Transport, and Spectroscopic Response
Jan Wilhelm, Anna-Sophia Hehn, Hossam Elgabarty +39
One of the distinguishing aspects of CP2K is its seamless integration of diverse structural and transition-state optimization techniques with advanced sampling approaches including…
Multi-head committees enable direct uncertainty prediction for atomistic foundation models
Hubert Beck, Pavol Simko, Lars L. Schaaf +2
Machine learning potentials have become a standard tool for atomistic materials modelling. While models continue to become more generalisable, an open challenge relates to efficien…
Introduction to machine learning potentials for atomistic simulations
Fabian L. Thiemann, Niamh O'Neill, Venkat Kapil +2
Machine learning potentials have revolutionised the field of atomistic simulations in recent years and are becoming a mainstay in the toolbox of computational scientists. This pape…
Protons accumulate at the graphene-water interface
Xavier R. Advincula, Kara D. Fong, Angelos Michaelides +1
Water's ability to autoionize into hydroxide and hydronium ions profoundly influences surface properties, rendering interfaces either basic or acidic. While it is well-established…
A foundation model for atomistic materials chemistry
Ilyes Batatia, Philipp Benner, Yuan Chiang +85
Atomistic simulations of matter, especially those that leverage first-principles (ab initio) electronic structure theory, provide a microscopic view of the world, underpinning much…
To Pair or not to Pair? Machine-Learned Explicitly-Correlated Electronic Structure for NaCl in Water
Niamh O'Neill, Benjamin X. Shi, Kara Fong +2
The extent of ion pairing in solution is an important phenomenon to rationalise transport and thermodynamic properties of electrolytes. A fundamental measure of this pairing is the…
Understanding the anomalously low dielectric constant of confined water: an ab initio study
Thomas Dufils, Christoph Schran, Ji Chen +3
Recent experiments have shown that the out-of-plane dielectric constant of water confined in nanoslits of graphite and hexagonal boron nitride (hBN) is vanishingly small. Despite e…
Crumbling Crystals: On the Dissolution Mechanism of NaCl in Water
Niamh O'Neill, Christoph Schran, Stephen J. Cox +1
Life on Earth depends upon the dissolution of ionic salts in water, particularly NaCl. However, an atomistic scale understanding of the process remains elusive. Simulations lend th…
Neural Network Interaction Potentials for para-Hydrogen with Flexible Molecules
Laura Durán Caballero, Christoph Schran, Fabien Brieuc +1
The study of molecular impurities in -hydrogen (H) clusters is key to push forward our understanding of intra- and intermolecular interactions including their impac…
State-resolved infrared spectrum of the protonated water dimer: Revisiting the characteristic proton transfer doublet peak
Henrik R. Larsson, Markus Schröder, Richard Beckmann +4
The infrared (IR) spectra of protonated water clusters encode precise information on the dynamics and structure of the hydrated proton. However, the strong anharmonic coupling and…
Infrared spectra at coupled cluster accuracy from neural network representations
Richard Beckmann, Fabien Brieuc, Christoph Schran +1
Infrared spectroscopy is key to elucidate molecular structures, monitor reactions and observe conformational changes, while providing information on both structural and dynamical p…
Machine learning potentials for complex aqueous systems made simple
Christoph Schran, Fabian L. Thiemann, Patrick Rowe +3
Simulation techniques based on accurate and efficient representations of potential energy surfaces are urgently needed for the understanding of complex aqueous systems such as soli…
Correlated Particle Motion and THz Spectral Response of Supercritical Water
Maciej Śmiechowsk, Christoph Schran, Harald Forbert +1
Molecular dynamics simulations of supercritical water reveal distinctly different distance-dependent modulations of dipolar response and correlations in particle motion compared to…
Force-induced Catastrophes on Energy Landscapes: Mechanochemical Manipulation of Downhill and Uphill Bifurcations Explains Ring-opening Selectivity of Cyclopropanes
Miriam Wollenhaupt, Christoph Schran, Martin Krupička +1
The mechanochemistry of ring-opening reactions of cyclopropane derivatives turns out to be unexpectedly rich and puzzling. After showing that a rare so-called uphill bifurcation in…
High-dimensional neural network potentials for solvation: The case of protonated water clusters in helium
Christoph Schran, Felix Uhl, Jörg Behler +1
The design of accurate helium-solute interaction potentials for the simulation of chemically complex molecules solvated in superfluid helium has long been a cumbersome task due to…
Converged Colored Noise Path Integral Molecular Dynamics Study of the Zundel Cation down to Ultra-low Temperatures at Coupled Cluster Accuracy
Christoph Schran, Fabien Brieuc, Dominik Marx
For a long time, performing converged path integral simulations at ultra-low, but finite temperatures of a few Kelvin has been a nearly impossible task. However, recent development…
Manifestations of Local Supersolidity of He around a Charged Molecular Impurity
Fabien Brieuc, Christoph Schran, Dominik Marx
A frozen, solid helium core, dubbed snowball, is typically observed around cations in liquid helium. Here we discover, using path integral simulations, that around a cationic molec…
Transferability of machine learning potentials: Protonated water neural network potential applied to the protonated water hexamer
Christoph Schran, Fabien Brieuc, Dominik Marx
A previously published neural network potential for the description of protonated water clusters up to the protonated water tetramer, H(HO), at essentially converged co…
Deciphering High-order Structural Correlations within Fluxional Molecules from Classical and Quantum Configurational Entropy
Rafal Topolnicki, Fabien Brieuc, Christoph Schran +1
We employ the k-th nearest-neighbor estimator of configurational entropy in order to decode within a parameter-free numerical approach the complex high-order structural correlation…
Committee neural network potentials control generalization errors and enable active learning
Christoph Schran, Krystof Brezina, Ondrej Marsalek
It is well known in the field of machine learning that committee models improve accuracy, provide generalization error estimates, and enable active learning strategies. In this wor…
Quantum Nature of the Hydrogen Bond from Ambient Conditions down to Ultra-low Temperatures
Christoph Schran, Dominik Marx
Many experimental techniques such as tagging photodissociation and helium nanodroplet isolation spectroscopy operate at very low temperatures in order to investigate hydrogen bondi…
Automated Fitting of Neural Network Potentials at Coupled Cluster Accuracy: Protonated Water Clusters as Testing Ground
Christoph Schran, Jörg Behler, Dominik Marx
Highly accurate potential energy surfaces are of key interest for the detailed understanding and predictive modeling of chemical systems. In recent years, several new types of forc…
Unravelling the influence of quantum proton delocalization on electronic charge transfer through the hydrogen bond
Christoph Schran, Ondrej Marsalek, Thomas E. Markland
Upon hydrogen bond formation, electronic charge density is transferred between the donor and acceptor, impacting processes ranging from hydration to spectroscopy. Here we use ab in…