Global structure optimization in computational chemistry is often limited not by leaving the current local minimum, which can be achieved by sufficiently large random moves, but by proposing productive moves that exploit local funnel structure without losing diversity. Minima hopping addresses this problem through short molecular-dynamics escape trajectories, local relaxation, and history-dependent feedback, but its efficiency depends strongly on the initial escape direction. We benchmark a curvature-assisted variant in which inverse-Hessian information accumulated by Broyden–Fletcher–Goldfarb–Shanno (BFGS) and limited-memory BFGS (L-BFGS) relaxation is recycled as an escape model. This requires no explicit second derivatives and no additional force evaluations before proposing low-curvature directions. Lennard–Jones (LJ) clusters with 60–74 particles provide controlled landscapes for comparing random and softened random directions, single Hessian modes, multi-mode Hessian combinations, and mixed Hessian-random directions. Dense BFGS curvature information identifies physically meaningful escape subspaces and can reduce repeated local exploration. Single deterministic modes, however, oversample local funnels, and L-BFGS curvature information is not reliable enough for direct mode selection. Combining several BFGS modes improves robustness, but softened random directions with L-BFGS remain the lowest-cost baseline. Curvature reuse is therefore most useful when it provides an inexpensive soft-mode subspace while preserving stochastic diversity, especially when conventional softening or trial-move optimization is expensive.
Daniel Schärf, T. Kühne· Theoretical Chemistry accoun...· 0 citations
Understanding and controlling chemical reactivity in biological systems require atomic-level insight into processes that are often inaccessible to experiments. Hybrid quantum mechanics/molecular mechanics (QM/MM) simulations provide a powerful framework for describing chemical reactions in complex environments, but their practical application remains limited by fragmented software ecosystems, restricted accessibility, and methodological approximations that can compromise accuracy and reproducibility. In particular, many existing QM/MM implementations rely on ad hoc couplings, proprietary software, or truncated treatments of long-range electrostatic interactions. Here, we present a robust and fully periodic QM/MM interface between the open-source molecular dynamics engine GROMACS and the electronic structure theory code CP2K. The implementation enables efficient and reproducible QM/MM molecular dynamics and enhanced sampling simulations with a consistent treatment of long-range electrostatics under periodic boundary conditions. By combining the strengths of two widely used community codes, this interface provides a general and scalable platform for studying chemical reactivity in biological systems and establishes a transparent reference implementation for QM/MM simulations.
D. Morozov, C. Blau, Ole Schütt et al.· Journal of Chemical Informat...· 0 citations
The present work revisits the methods within CP2K that turn electronic structure into dynamics, transport, and spectroscopic response, highlighting CP2K's unique capability to unify quantum chemistry with quantum and statistical mechanics within a versatile, holistic simulation environment.
Jan Wilhelm, Anna-Sophia Hehn, Hossam Elgabarty et al.· 1 citation· ⚡1
Mandala is a modular software framework for learning block-sparse electronic-structure matrices with E(3)-equivariant graph neural networks that connects electronic-structure learning and observable-guided modeling while retaining a representation tied to quantum-mechanical operators rather than only scalar or vector targets as in MLIPs.
B. Brzoza, Wiktoria Szopa, Z. Elabid et al.· 0 citations
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