Extended tight-binding methods are attractive for condensed-phase simulations because they retain an explicit electronic Hamiltonian at a cost far below conventional density functional theory. Their transferability to periodic materials, however, cannot be assessed reliably without a genuinely periodic Hamiltonian, Brillouin-zone sampling, and symmetry-aware crystal calculations. We present a periodic implementation of the second-generation geometry, frequency, and noncovalent interaction parametrization of extended tight binding (GFN2-xTB) in CP2K. The central theoretical ingredient is a multipolar Ewald formulation for the self-consistent shell charges, atomic dipoles, and quadrupoles of GFN2-xTB, together with analytical forces and stress contributions for geometry optimization, cell optimization, and molecular dynamics. For the ranking of ice polymorphs in the DMC-ICE13 data set, periodic GFN2-xTB on a converged 33k-point mesh improves the relative polymorph energies over GFN1-xTB, reducing the mean absolute error from 8.01 to 3.46 kJ mol–1. Because GFN1-xTB and GFN2-xTB differ in several coupled, jointly parametrized terms, this whole-model comparison does not isolate the contribution of multipolar electrostatics. For the LC10 set of cubic covalent and ionic solids, GFN2-xTB yields valid equation-of-state minima for all ten systems, with lattice-constant and cohesive-energy mean absolute errors of 0.062 Å and 1.30 eV per atom, respectively. Periodic GFN2-xTB is evaluated for molecular crystal lattice energies and cell parameters using the X23b data set. Using native Bloch 23-mesh cell optimization with full symmetry reduction, followed by a 33-mesh energy evaluation on the optimized cells, periodic GFN2-xTB gives a mean absolute lattice-energy error of 14.09 kJ mol–1 and a mean absolute relaxed-cell volume error of 5.84% for the full X23b relaxed-cell set. The benchmarks position periodic GFN2-xTB as an efficient electronic structure level for screening, pre-optimization, and large exploratory simulations. This work creates the foundation for periodic anisotropic electrostatics in the extended tight-binding framework and their efficient application in CP2K.
Vahideh Alizadeh, Johann V. Pototschnig, L. M. Seidler et al.· Journal of Chemical Theory a...· 0 citations
Machine-learned exchange--correlation (XC) functionals offer a route to improve Kohn--Sham density-functional theory without incurring the cost of explicitly correlated electronic-structure methods. Their use in production simulation codes, however, requires a well-defined mapping between the learned model and the host-code density representation. We formulate and implement a Skala-1.1 interface in CP2K through the external GauXC library. CP2K supplies the geometry, Gaussian basis, spin-resolved atomic-orbital density matrix, and communicator, while GauXC evaluates the XC energy, atomic-orbital potential matrix, and available nuclear derivatives. The interface accepts both all-electron and valence-only density matrices. The latter may arise from separable dual-space pseudopotentials or molecular effective-core potentials. Implementation errors are isolated from functional differences by comparing the Perdew--Burke--Ernzerhof (PBE) functional evaluated through GauXC with native CP2K PBE. The resulting interface gives consistent energies, forces validated against finite-difference total-energy checks, and force-based molecular-virial diagnostics for representative molecular cases. The dietGMTKN55 benchmark suite is evaluated with an all-electron Gaussian augmented plane-wave treatment for elements up to bromine and def2 effective-core potentials for the heavier elements. The resulting aggregate mean absolute deviation of 1.255 kcal/mol is within 0.020 kcal/mol of the corresponding Skala reference value of 1.235 kcal/mol. This work establishes a validated molecular implementation of Skala in CP2K through GauXC.
Franz Pöschel, Johann Pototschnig, Frederick Stein et al.· 0 citations
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
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
