Exploring molecular excited states holds immense significance across organic chemistry, chemical biology, and materials science. Understanding the photophysical properties of molecular chromophores is crucial for designing nature-inspired functional molecules, with applications ranging from photosynthesis to pharmaceuticals. Non-adiabatic molecular dynamics simulations are powerful tools to investigate the photochemistry of molecules and materials, but demand extensive computing resources, especially for complex molecules and environments. To address these challenges, the integration of machine learning has emerged. Machine learning algorithms can be used to analyse vast datasets and accelerate discoveries by identifying relationships between geometrical features and ground as well as excited-state properties. However, challenges persist, including the acquisition of accurate excited-state data and managing the complexity of the data. This article provides an overview of recent and best practices in machine learning for non-adiabatic molecular dynamics, focusing on pre-processing, surface fitting, and post-processing of data.

• Publication type
• Journal Article MeSH
• Review MeSH

The COLUMBUS program system provides the tools for performing high-level multireference (MR) computations, including the multireference configuration interaction (MRCI) method and its multireference averaged quadratic coupled cluster (MR-AQCC) extension, allowing computations on a wide range of fascinating atomic and molecular systems, including the treatment of open-shells and complicated excited state phenomena. The inclusion of spin-orbit coupling (SOC) directly within the MRCI step enables the description of systems containing heavy elements, such as lanthanides and actinides, whose properties are strongly influenced by SOC. Analytic energy gradients and nonadiabatic couplings at the correlated MRCI level provide the foundation for a variety of dynamics studies, giving insight into ultrafast photochemistry. New and ongoing method developments in COLUMBUS include the computation of spin densities, improved descriptions of ionic states, enhancements to the AQCC method, and the porting of COLUMBUS to graphical processing units (GPUs). New external interfaces enable an enhanced description of electronic resonances and molecules in strong laser fields. This work highlights these new developments while providing a detailed account of the diverse applications of COLUMBUS in recent years.

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• Journal Article MeSH

Molecular radicals are efficient electroluminescent emitters due to the spin multiplicity of their electronic states. The excited states often exhibit a complex composition with multiple significant electronic configurations, which are essential for their optoelectronic properties but difficult to probe directly. Here we use light-scanning tunneling microscopy to investigate such an excited state by visualizing the response of a single radical molecule to a laser excitation. We observe characteristic atomic-scale spatial photocurrent patterns that can be tuned by applied bias voltage. We interpret these patterns as resulting from decay of an excited doublet state through sequential electron transfers with the tip and the substrate. The relative contributions of two dominating electronic configurations involved in this excited state are tuned by the applied voltage. This approach thus allows for disentangling the components of multiconfigurational excited states in single molecules.

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• Journal Article MeSH

Variational Quantum Algorithms (VQAs) provide a promising framework for solving electronic structure problems using the computational capabilities of quantum computers to explore high-dimensional Hilbert spaces efficiently. This research investigates the performance of VQAs in electronic structure calculations of gallium arsenide (GaAs), a semiconductor with a zinc-blende structure. Utilizing a tight-binding Hamiltonian and a Jordan-Wigner-like transformation, we map the problem to a 10-qubit Hamiltonian. We analyze the impact of quantum circuit architectures, algorithm hyperparameters, and optimization methods on two VQAs: Variational Quantum Deflation (VQD) and Subspace Search Variational Quantum Eigensolver (SSVQE). We observed that while both algorithms offer promising results, the choice of ansatz and hyperparameter tuning were especially critical in achieving reliable outcomes, particularly for higher energy states. Adjusting the hyperparameters in VQD significantly enhanced the accuracy of higher energy state calculations, reducing the error by an order of magnitude, whereas tuning the hyperparameters in SSVQE had minimal impact. Our findings provide insights into optimizing VQAs for electronic structure problems, paving the way for their application to more complex systems on near-term quantum devices.

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• Journal Article MeSH

Newton-X is an open-source computational platform to perform nonadiabatic molecular dynamics based on surface hopping and spectrum simulations using the nuclear ensemble approach. Both are among the most common methodologies in computational chemistry for photophysical and photochemical investigations. This paper describes the main features of these methods and how they are implemented in Newton-X. It emphasizes the newest developments, including zero-point-energy leakage correction, dynamics on complex-valued potential energy surfaces, dynamics induced by incoherent light, dynamics based on machine-learning potentials, exciton dynamics of multiple chromophores, and supervised and unsupervised machine learning techniques. Newton-X is interfaced with several third-party quantum-chemistry programs, spanning a broad spectrum of electronic structure methods.

• MeSH
• Quantum Theory * MeSH
• Molecular Dynamics Simulation MeSH
• Software * MeSH
• Publication type
• Journal Article MeSH