94 citations · 122 across the 11 of their papers we have counts for
11 papers
Nonadiabatic Forward-Flux Sampling of Rare Molecular Gas-Phase Ammonia Photodissociation
Madlen Maria Reiner, Johannes C. B. Dietschreit, Leticia González +1
Simulating rare events in photochemistry by brute-force surface hopping is computationally prohibitive: most propagated trajectories either remain nonreactive or follow dominant re…
Reweighting free energy profiles between universal machine learning interatomic potentials for fast consensus building
Sauradeep Majumdar, Miguel Steiner, Johannes C. B. Dietschreit +4
Free energy profiles serve as a fundamental bridge between microscopic atomic fluctuations and macroscopic thermodynamic observables. Estimating the free energy profile along a rea…
NATPS: Nonadiabatic Transition Path Sampling Using Time-Reversible MASH Dynamics
Xiran Yang, Madlen Maria Reiner, Brigitta Bachmair +3
Rare nonadiabatic events play a central role in photochemistry but remain difficult to simulate because excited-state dynamics is computationally demanding and often stochastic. He…
The Entropic Barrier around the Conical Intersection Seam
Johannes C. B. Dietschreit, Sebastian Mai, Leticia González
Conical intersections (CIs) are seen as the main mediators of nonadiabatic transitions; yet, mixed quantum-classical (MQC) simulations that treat nuclei as classical point particle…
Incorporating Long-Range Interactions via the Multipole Expansion into Ground and Excited-State Molecular Simulations
Rhyan Barrett, Johannes C. B. Dietschreit, Julia Westermayr
Simulating long-range interactions remains a significant challenge for molecular machine learning potentials due to the need to accurately capture interactions over large spatial r…
Excited-state nonadiabatic dynamics in explicit solvent using machine learned interatomic potentials
Maximilian X. Tiefenbacher, Brigitta Bachmair, Cheng Giuseppe Chen +4
Excited-state nonadiabatic simulations with quantum mechanics/molecular mechanics (QM/MM) are essential to understand photoinduced processes in explicit environments. However, the…
Enhanced sampling of robust molecular datasets with uncertainty-based collective variables
Aik Rui Tan, Johannes C. B. Dietschreit, Rafael Gomez-Bombarelli
Generating a data set that is representative of the accessible configuration space of a molecular system is crucial for the robustness of machine learned interatomic potentials (ML…
Learning Collective Variables with Synthetic Data Augmentation through Physics-Inspired Geodesic Interpolation
Soojung Yang, Juno Nam, Johannes C. B. Dietschreit +1
In molecular dynamics simulations, rare events, such as protein folding, are typically studied using enhanced sampling techniques, most of which are based on the definition of a co…
Single-model uncertainty quantification in neural network potentials does not consistently outperform model ensembles
Aik Rui Tan, Shingo Urata, Samuel Goldman +2
Neural networks (NNs) often assign high confidence to their predictions, even for points far out-of-distribution, making uncertainty quantification (UQ) a challenge. When they are…
Entropy and Energy Profiles of Chemical Reactions
Johannes C. B. Dietschreit, Dennis J. Diestler, Rafael Gómez-Bombarelli
The description of chemical processes at the molecular level is often facilitated by use of reaction coordinates, or collective variables (CVs). The CV measures the progress of the…
Differentiable Simulations for Enhanced Sampling of Rare Events
Martin Šípka, Johannes C. B. Dietschreit, Lukáš Grajciar +1
Simulating rare events, such as the transformation of a reactant into a product in a chemical reaction typically requires enhanced sampling techniques that rely on heuristically ch…