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.
Abstract
One of the distinguishing aspects of CP2K is its seamless integration of diverse structural and transition-state optimization techniques with advanced sampling approaches including Monte Carlo, molecular dynamics, and metadynamics, enabling the efficient exploration of complex potential- and free-energy landscapes, including rare events. These capabilities are combined with a broad hierarchy of energy and force evaluation methods, ranging from classical and machine-learned interaction potentials and mixed quantum-classical multiscale and semiempirical schemes, to highly accurate quantum-mechanical electronic-structure approaches. At the heart of the latter lies the Gaussian and plane-wave framework, along with its augmented all-electron generalization, which have been described in detail in our previous code review [T. D. K\"uhne et al., J. Chem. Phys. 152, 194103 (2020)]. Building on this foundation, the present work revisits the methods within CP2K that turn electronic structure into dynamics, transport, and spectroscopic response. Particular emphasis is placed on the coupling between static response calculations and nuclear motion: spectra may be evaluated at optimized structures, averaged over thermally sampled configurations, obtained from time-correlation functions along ab-initio or path integral molecular trajectories, or followed in real time together with electronic and nuclear dynamics. The same modular structure also enables equilibrium and biased transport simulations, from Kubo-type linear response to open-boundary approaches under external potentials, highlighting CP2K's unique capability to unify quantum chemistry with quantum and statistical mechanics within a versatile, holistic simulation environment.
Investigating ultrafast electron-nuclear dynamics in molecules requires robust theoretical methods that offer predictive capabilities beyond interpretation purposes. However, the vast number of electronic and nuclear degrees of freedom often prevents an accurate description of chemical systems on classical computers. To address these challenges, hybrid approaches combining quantum and classical computing can leverage the strengths of both to enhance the computational efficiency and fully exploit hardware capabilities. In this work, we perform excited-state molecular dynamics simulations of the formaldimine molecule by combining the surface hopping nonadiabatic molecular dynamics technique with quantum algorithms. We replace conventional electronic energy and overlap calculations with the variational quantum deflation (VQD) algorithm and the Hadamard test, respectively, employing a configuration state function (CSF) spin-restricted ansatz. Finally, we evaluate the accuracy and feasibility of three different gradient calculation approaches integrated with the quantum computing components, demonstrating the advantages of an analytical gradient method over finite difference approaches for hybrid quantum-classical integration.
Silvia Riera-Villapún, Jaime Scharfhausen-Curiel, Juan Luis Sánchez Toural et al.· Journal of Physical Chemistr...· 0 citations
Quantum-centric workflows are a promising route to improving the accuracy of property predictions in computational chemistry and materials science. By integrating quantum sampling algorithms with classical solvers, electronic structure calculations have recently demonstrated their potential even on noisy intermediate-scale quantum devices. In principle, the method of Vibrational Configuration Interaction (VCI) is suitable for integration with quantum sampling algorithms as well. However, demonstrations of computational workflows for quantum-centric, vibrational property predictions are still lacking. Here, we introduce a methodology for performing anharmonic vibrational structure calculations that can be deployed in a hybrid, quantum-classical mode. Starting from a quartic force field, the approach combines a Vibrational Self-Consistent Field (VSCF) with VCI in either Full, Selected (S-VCI), or Symmetry-Adapted (SA-VCI) mode. In S-VCI, an Epstein-Nesbet perturbative screening significantly reduces the configuration space while retaining high predictive accuracy. A state-list input enables the integration of externally generated vibrational configurations as a seed space. As a proof-of-concept, we demonstrate a hybrid, quantum-classical computational workflow, in which a quantum sampling algorithm provides the seed. Our vibrational wave function analysis package ViBra, equipped with a graphical interface, is available at https://github.com/raphafe96/ViBra.
R. F. Ligório, M. A. Barroca, Alan C. Duriez et al.· 0 citations
The rapid development of two-dimensional (2D) materials has reshaped modern nanoscience, offering properties that differ fundamentally from their bulk counterparts. As experimental discovery accelerates, the need for reliable computational techniques has become increasingly important. Within the framework of density functional theory, this review explores the critical role of exchange-correlation (XC) functionals in predicting key material properties such as structural, optoelectronic, magnetic, and thermal properties. We examine the challenges posed by quantum confinement, anisotropic screening, and van der Waals interactions, which conventional functionals often fail to describe. Advanced approaches, including meta-generalized gradient approximation, hybrid functionals, and many-body perturbation theory (e.g. GW and Bethe–Salpeter equation), are assessed for their improved accuracy in capturing electronic structure and excitonic effects. We further discuss the non-universality of functionals across different 2D material families and the emerging role of machine learning to enhance computational efficiency. Finally, the review outlines current limitations and emerging strategies, providing a roadmap for advancing XC functionals and beyond, to enable the practical design and application of 2D materials.
Detailed analysis of thermal conductivity results obtained from molecular dynamics (MD) trajectories conventionally relies on knowledge of the harmonic vibrational modes of the system. Such is the case in methods like Green--Kubo modal analysis (GKMA) and homogeneous nonequilibrium modal analysis (HNEMA). This approach faces several shortcomings that become especially important when dealing with nanostructures. Chief among them is scalability, followed by the ambiguity associated to the degeneracy of the large vibrational subspaces defined by a common translational wave number k. We propose an alternative for quasi-one-dimensional (quasi-1D) systems: we construct the modal basis for the decomposition of the thermal conductivity from line-group projection operators, so that every projected component carries well-defined symmetry labels (including rotational information and parities) and the decomposition is unique at the level of irreducible representations (irreps). Applying the idea to a (10,0)-(20,0) WS2-MoS2 double-walled nanotube (DWNT) described by a neuroevolution potential (NEP), we find that at 300 K both HNEMA and GKMA yield statistically consistent total conductivities and allow the identification of several prominent symmetry-adapted conduction channels. The GKMA pair matrix shows that within-block and same-channel cross-k terms account for 75.6% of the total conductivity, while cross-channel correlations contribute 24.4%.
Yu-Jie Cen, Sandro Wieser, Georg K. H. Madsen et al.· 0 citations
Density functional theory (DFT) has become one of the principal theoretical frameworks for
investigating the electronic structure, energetics and properties of atoms, molecules, solids and interfaces. Its
importance arises from its favorable balance between computational cost and predictive capability compared with many
wavefunction-based electronic-structure methods. Rather than treating the many-electron wavefunction as the primary
variable, DFT expresses ground-state properties through the electron density, while the Kohn–Sham formulation
provides a practical route for solving the resulting equations. DFT is not a single computational method; its predictive
capability depends strongly on the choice of exchange–correlation functional, basis representation, pseudopotential or
core treatment, spin treatment, treatment of dispersion and, for correlated systems, possible Hubbard-type corrections.
This review presents a concise overview of the theoretical foundations of DFT and the major approximations used in
modern calculations. The practical workflow from structural preparation and convergence testing to geometry
optimization, electronic-structure calculations and property analysis is discussed. Applications in molecular chemistry,
materials discovery, defects, catalysis, photocatalysis and energy-storage materials are highlighted. Current limitations,
including delocalization error, band-gap underestimation, strong electron correlation, finite-temperature effects and
model-size limitations, are also examined. Finally, the integration of DFT with high-throughput computation, machine
learning, multiscale modeling and inverse materials design is discussed. These developments are transforming DFT
from an individual calculation technique into a central component of data-driven computational science.
Unknown authors· International Journal of Mul...· 0 citations
Spin-forbidden processes are central to catalysis, photochemistry, and transition-metal reactivity, yet their simulation remains challenging because the relevant transformations proceed between potential energy surfaces of different spin multiplicities and are frequently thermally activated rare events. Existing computational strategies typically provide either static descriptions of crossing regions, such as minimum-energy crossing point searches, or dynamically explicit but computationally demanding nonadiabatic simulations that are difficult to apply to medium- and large-sized systems. Here, we present SURFXsim, a computational framework for simulating spin-forbidden events by combining atomistic molecular dynamics, a configuration-dependent mixed Hamiltonian, and enhanced sampling along a spin-gap collective variable. The method should be understood as a finite-temperature sampling and interpolation strategy, rather than as a direct simulation of coherent spin–orbit-coupled electronic-state dynamics. It enables smooth propagation between end-state basins while using metadynamics to drive the exploration of crossing regions and reconstruct free-energy landscapes. SURFXsim is implemented in Python on top of ASE and interfaced with PLUMED, allowing the use of both machine-learning and conventional electronic-structure calculators. We illustrate the approach with distortion-driven and reactivity-driven spin-crossing examples using machine learning potentials and further demonstrate compatibility with a quantum-mechanical backend through the Gaussian package. Together, these results position SURFXsim as a practical and flexible route for studying spin-forbidden chemistry beyond the scale routinely accessible to fully nonadiabatic dynamics.
Ajmal Rahman Mullukkandy, B. Ensing, Carles Bo et al.· Journal of Chemical Theory a...· 0 citations
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