arXiv subjects

Johannes C. B. Dietschreit

Publications and source records attributed to Johannes C. B. Dietschreit.

12 recordsLinked to original sources

arXiv2026

Nonadiabatic Forward-Flux Sampling of Rare Molecular Gas-Phase Ammonia Photodissociation

Simulating rare events in photochemistry by brute-force surface hopping is computationally prohibitive: most propagated trajectories either remain nonreactive or follow dominant relaxation channels, while the reaction of interest may occur with very low probability. Path sampling methods have long addressed this problem for ground-state dynamics, but their extension to nonadiabatic processes has so far been limited to low-dimensional analytical models. Here, we demonstrate that our recently developed nonadiabatic forward-flux sampling (NAFFS) method can be applied to efficiently simulate rare-events in full-dimensional molecular systems. As a representative benchmark, we investigate the rare molecular photodissociation channel of gas-phase ammonia, NH$_3$ + h$ν\rightarrow$ NH + H$_2$. NAFFS samples reactive trajectories with the correct statistical weight, reproduces the reaction rate constant obtained in previous brute-force dynamical studies, provides direct access to mechanistic information through an averaged committor analysis, and reduces the simulation time per transition trajectory by up to three orders of magnitude. These results establish NAFFS as an efficient and statistically rigorous framework for investigating rare nonadiabatic processes in realistic molecular systems.

physics.chem-ph↗

arXiv2026

The Entropic Barrier around the Conical Intersection Seam

Conical intersections (CIs) are seen as the main mediators of nonadiabatic transitions; yet, mixed quantum-classical (MQC) simulations that treat nuclei as classical point particles rarely, if ever, sample geometries with exactly degenerate electronic energies. Here we show that this behavior arises from a fundamental statistical-mechanical constraint on classical nuclear motion. Using a linear vibronic coupling model, we derive the free energy along the adiabatic energy gap and demonstrate analytically that as the gap approaches zero, the free energy diverges around the CI seam. Molecular dynamics simulations of the methaniminium cation on the S$_1$ surface confirm this prediction: trajectories can approach regions with small adiabatic gaps, but never reach the CI seam, even if the CI corresponds to a region of lowest potential energy. These results clarify why MQC methods successfully capture nonadiabatic behavior without sampling exact degeneracies and agree with recent findings that classical trajectories can sense the presence of CIs without visiting them.

physics.chem-ph↗

arXiv2026

Reweighting free energy profiles between universal machine learning interatomic potentials for fast consensus building

Free energy profiles serve as a fundamental bridge between microscopic atomic fluctuations and macroscopic thermodynamic observables. Estimating the free energy profile along a reaction coordinate, referred to as the potential of mean force (PMF), with density functional theory (DFT) accuracy is computationally expensive. Universal machine learning interatomic potentials (MLIPs) drastically reduce this cost, but their accuracy is strongly determined by their training data and hence can be uncertain for a given system. In this work, we present a systematic and scalable framework for reweighting PMFs, initially sampled with a single 'source' MLIP, across a representative suite of target MLIPs. Because traditional direct exponential reweighting fails for large system sizes due to low phase-space overlap between potentials, we deploy robust analytical corrections. Applying this to a complex 601-atom system of Li$^+$ transport in a nanoconfined electrolyte, we demonstrate that a mean energy-gap approximation effectively bypasses statistical collapse, producing a highly stable PMF matching the target PMF. Using this approach, we recover high-fidelity target thermodynamics across multiple DFT reference levels (PBE+D3, PBE-sol, r$^2$SCAN,r$^2$SCAN-D4) at a fraction of the computational cost of full simulations. Furthermore, thermodynamic analysis reveals that the studied MLIPs partition into two distinct clusters driven by their training data. Our reweighting framework successfully recovers target thermodynamic properties--specifically, reaction and activation free energies--even when the phase-space overlap between potentials is critically low. Ultimately, this approach establishes a vital diagnostic protocol to achieve affordable cross-model consensus on materials chemistry properties without redundant, resource-intensive simulations.

physics.chem-ph↗

arXiv2026

NATPS: Nonadiabatic Transition Path Sampling Using Time-Reversible MASH Dynamics

Rare nonadiabatic events play a central role in photochemistry but remain difficult to simulate because excited-state dynamics is computationally demanding and often stochastic. Here we introduce a deterministic and time-reversible implementation of nonadiabatic dynamics that enables the application of transition path sampling (TPS) to excited-state processes. Our approach builds on the Mapping Approach to Surface Hopping (MASH) and establishes the conditions required for path ensemble sampling, in particular time reversibility and detailed balance. Combining this dynamics with the TPS framework yields a new method, termed nonadiabatic transition path sampling (NATPS). Using a model system of electronically coupled potential energy surfaces, we demonstrate that NATPS efficiently generates ensembles of reactive trajectories and provides mechanistic insight into nonadiabatic pathways. Compared with brute-force trajectory simulations and forward-flux sampling approaches, NATPS substantially reduces the computational effort required to obtain reactive trajectories.

physics.comp-ph↗

arXiv2025

Incorporating Long-Range Interactions via the Multipole Expansion into Ground and Excited-State Molecular Simulations

Simulating long-range interactions remains a significant challenge for molecular machine learning potentials due to the need to accurately capture interactions over large spatial regions. In this work, we introduce FieldMACE, an extension of the message-passing atomic cluster expansion (MACE) architecture that integrates the multipole expansion to model long-range interactions more efficiently. By incorporating the multipole expansion, FieldMACE effectively captures environmental and long-range effects in both ground and excited states. Benchmark evaluations demonstrate its superior performance in predictions and computational efficiency compared to previous architectures, as well as its ability to accurately simulate nonadiabatic excited-state dynamics. Furthermore, transfer learning from foundational models enhances data efficiency, making FieldMACE a scalable, robust, and transferable framework for large-scale molecular simulations.

physics.comp-ph↗

arXiv2025

Excited-state nonadiabatic dynamics in explicit solvent using machine learned interatomic potentials

Excited-state nonadiabatic simulations with quantum mechanics/molecular mechanics (QM/MM) are essential to understand photoinduced processes in explicit environments. However, the high computational cost of the underlying quantum chemical calculations limits its application in combination with trajectory surface hopping methods. Here, we use FieldSchNet, a machine-learned interatomic potential capable of incorporating electric field effects into the electronic states, to replace traditional QM/MM electrostatic embedding with its ML/MM counterpart for nonadiabatic excited state trajectories. The developed method is applied to furan in water, including five coupled singlet states. Our results demonstrate that with sufficiently curated training data, the ML/MM model reproduces the electronic kinetics and structural rearrangements of QM/MM surface hopping reference simulations. Furthermore, we identify performance metrics that provide robust and interpretable validation of model accuracy.

physics.chem-ph↗

arXiv2024

Learning Collective Variables with Synthetic Data Augmentation through Physics-Inspired Geodesic Interpolation

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 collective variable (CV) along which acceleration occurs. Obtaining an expressive CV is crucial, but often hindered by the lack of information about the particular event, e.g., the transition from unfolded to folded conformation. We propose a simulation-free data augmentation strategy using physics-inspired metrics to generate geodesic interpolations resembling protein folding transitions, thereby improving sampling efficiency without true transition state samples. This new data can be used to improve the accuracy of classifier-based methods. Alternatively, a regression-based learning scheme for CV models can be adopted by leveraging the interpolation progress parameter.

physics.chem-ph↗

arXiv2024

Enhanced sampling of robust molecular datasets with uncertainty-based collective variables

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 (MLIP). However, the complexity of molecular systems, characterized by intricate potential energy surfaces (PESs) with numerous local minima and energy barriers, presents a significant challenge. Traditional methods of data generation, such as random sampling or exhaustive exploration, are either intractable or may not capture rare, but highly informative configurations. In this study, we propose a method that leverages uncertainty as the collective variable (CV) to guide the acquisition of chemically-relevant data points, focusing on regions of the configuration space where ML model predictions are most uncertain. This approach employs a Gaussian Mixture Model-based uncertainty metric from a single model as the CV for biased molecular dynamics simulations. The effectiveness of our approach in overcoming energy barriers and exploring unseen energy minima, thereby enhancing the data set in an active learning framework, is demonstrated on the alanine dipeptide benchmark system.

cs.LG↗

arXiv2023

Single-model uncertainty quantification in neural network potentials does not consistently outperform model ensembles

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 employed to model interatomic potentials in materials systems, this problem leads to unphysical structures that disrupt simulations, or to biased statistics and dynamics that do not reflect the true physics. Differentiable UQ techniques can find new informative data and drive active learning loops for robust potentials. However, a variety of UQ techniques, including newly developed ones, exist for atomistic simulations and there are no clear guidelines for which are most effective or suitable for a given case. In this work, we examine multiple UQ schemes for improving the robustness of NN interatomic potentials (NNIPs) through active learning. In particular, we compare incumbent ensemble-based methods against strategies that use single, deterministic NNs: mean-variance estimation, deep evidential regression, and Gaussian mixture models. We explore three datasets ranging from in-domain interpolative learning to more extrapolative out-of-domain generalization challenges: rMD17, ammonia inversion, and bulk silica glass. Performance is measured across multiple metrics relating model error to uncertainty. Our experiments show that none of the methods consistently outperformed each other across the various metrics. Ensembling remained better at generalization and for NNIP robustness; MVE only proved effective for in-domain interpolation, while GMM was better out-of-domain; and evidential regression, despite its promise, was not the preferable alternative in any of the cases. More broadly, cost-effective, single deterministic models cannot yet consistently match or outperform ensembling for uncertainty quantification in NNIPs.

cs.LG↗

arXiv2023

Entropy and Energy Profiles of Chemical Reactions

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 reaction and allows the construction of profiles that track the evolution of a specific property as the reaction progresses. Whereas CVs are routinely used, especially alongside enhanced sampling techniques, links between profiles and thermodynamic state functions and reaction rate constants are not rigorously exploited. Here, we report a unified treatment of such reaction profiles. Tractable expressions are derived for the free-energy, internal-energy, and entropy profiles as functions of only the CV.We demonstrate the ability of this treatment to extract quantitative insight from the entropy and internal-energy profiles of various real-world physicochemical processes, including intramolecular organic reactions, ionic transport in superionic electrolytes, and molecular transport in nanoporous materials.

physics.chem-ph↗

arXiv2023

From Free-Energy Profiles to Activation Free Energies

Given a chemical reaction going from reactant (R) to the product (P) on a potential energy surface (PES) and a collective variable (CV) that discriminates between R and P, one can define a free-energy profile (FEP) as the logarithm of the marginal Boltzmann distribution of the CV. The FEP is not a true free energy, however, it is common to treat the FEP as the free-energy analog of the minimum energy path on the PES and to take the activation free energy, $ΔF^\ddagger_\mathrm{RP}$, as the difference between the maximum of the FEP at the transition state and the minimum at R. We show that this approximation can result in large errors. Since the FEP depends on the CV, it is therefore not unique, and different, discriminating CVs can yield different activation free energies for the same reaction. We derive an exact expression for the activation free energy that avoids this ambiguity with respect to the choice of CV. We find $ΔF^\ddagger_\mathrm{RP}$ to be a combination of the probability of the system being in the reactant state, the probability density at the transition state surface, and the thermal de~Broglie wavelength associated with the transition from R to P. We then evaluate the activation free energies based on our formalism for simple analytic models and realistic chemical systems. The analytic models show that the widespread FEP-based approximation applies only at low temperatures for CVs for which the effective mass of the associated pseudo-particle is small. Most chemical reactions of practical interest involve polyatomic molecules with complex, high-dimensional PES that cannot be treated analytically and pose the added challenge of choosing a good CV, typically through heuristics. We study the influence of the choice of CV and find that, while the reaction free energy is largely unaffected, $ΔF^\ddagger_\mathrm{RP}$ is quite sensitive.

physics.chem-ph↗

arXiv2023

Differentiable Simulations for Enhanced Sampling of Rare Events

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 chosen collective variables (CVs). We propose using differentiable simulations (DiffSim) for the discovery and enhanced sampling of chemical transformations without a need to resort to preselected CVs, using only a distance metric. Reaction path discovery and estimation of the biasing potential that enhances the sampling are merged into a single end-to-end problem that is solved by path-integral optimization. This is achieved by introducing multiple improvements over standard DiffSim such as partial backpropagation and graph mini-batching making DiffSim training stable and efficient. The potential of DiffSim is demonstrated in the successful discovery of transition paths for the Muller-Brown model potential as well as a benchmark chemical system - alanine dipeptide.

physics.chem-ph↗