output
20172025
most citedQuantum Package 2.0: An Open-Source Determinant-Driven Suite of Programs

171 citations

15 papers

physics.comp-ph2025★ 10 cited

A Descriptor Is All You Need: Accurate Machine Learning of Nonadiabatic Coupling Vectors

Jakub Martinka, Lina Zhang, Yi-Fan Hou +4

Nonadiabatic couplings (NACs) play a crucial role in modeling photochemical and photophysical processes with methods such as the widely used fewest-switches surface hopping (FSSH).…

quant-ph2025★ 4 cited

The Quantum Measurement Problem: A Review of Recent Trends

Anderson A. Tomaz, Rafael S. Mattos, Mario Barbatti

Left on its own, a quantum state evolves deterministically under the Schrödinger Equation, forming superpositions. Upon measurement, however, a stochastic process governed by the B…

physics.chem-ph2025★ 39 cited

Roadmap for Molecular Benchmarks in Nonadiabatic Dynamics

Léon E. Cigrang, Basile F. E. Curchod, Rebecca A. Ingle +42

Simulating the coupled electronic and nuclear response of a molecule to light excitation requires the application of nonadiabatic molecular dynamics. However, when faced with a spe…

cond-mat.mes-hall2024★ 1 cited

Dynamic switching of ferrocene and plasmonic interactions in Au/self-assembled monolayer/single Ag nanocube molecular junctions

Mariem Ben Youssef, Peeranuch Poungsripong, Hugo Bidotti +7

We report the redox switching of ferrocene moieties embedded in a double tunnel barrier plasmonic cavity fabricated from a click-chemistry self-assembled monolayers of ferrocenyl-a…

physics.chem-ph2024★ 5 cited

Extending non-adiabatic rate theory to strong electronic couplings in the Marcus inverted regime

Thomas P Fay

Electron transfer reactions play an essential role in many chemical and biological processes. Fermi's Golden rule, which assumes that the coupling between electronic states is smal…

physics.chem-ph2024★ 5 cited

A simple approach to rotationally invariant machine learning of avector quantity

Jakub Martinka, Marek Pederzoli, Mario Barbatti +2

Unlike with the energy, which is a scalar property, machine learning (ML) predictions of vector or tensor properties poses the additional challenge of achieving proper invariance (…

physics.chem-ph2024★ 31 cited

MLatom software ecosystem for surface hopping dynamics in Python with quantum mechanical and machine learning methods

Lina Zhang, Sebastian V. Pios, Mikołaj Martyka +7

We present an open-source MLatom@XACS software ecosystem for on-the-fly surface hopping nonadiabatic dynamics based on the Landau-Zener-Belyaev-Lebedev (LZBL) algorithm. The dynami…

physics.chem-ph2023★ 70 cited

MLatom 3: Platform for machine learning-enhanced computational chemistry simulations and workflows

Pavlo O. Dral, Fuchun Ge, Yi-Fan Hou +15

Machine learning (ML) is increasingly becoming a common tool in computational chemistry. At the same time, the rapid development of ML methods requires a flexible software framewor…

physics.chem-ph2022★ 11 cited

Probing the electronic structure and photophysics of thiophene-diketopyrrolopyrrole derivatives in solution

Daniel W Polak, Mariana T do Casal, Josene M Toldo +7

Diketopyrrolopyrroles are a popular class of electron-withdrawing unit in optoelectronic materials. When combined with electron donating side-chain functional groups such as thioph…

cond-mat.other2022★ 21 cited

The role of electron polarization on nuclear spin diffusion

Alessandro Chessari, Samuel F. Cousin, Sami Jannin +1

Dynamic nuclear polarization (DNP) is capable of boosting signals in nuclear magnetic resonance by orders of magnitude by creating out-of-equilibrium nuclear spin polarization. The…

physics.chem-ph2020★ 30 cited

Analytic energy, gradient, and hessian of electrostatic embedding QM/MM based on electrostatic potential fitted atomic charges scaling linearly with the MM subsystem size

Miquel Huix-Rotllant, Nicolas Ferré

Electrostatic potential fitting method (ESPF) is a powerful way of defining atomic charges derived from quantum density matrices fitted to reproduce a quantum mechanical charge dis…

physics.chem-ph2019★ 24 cited

Efficient analytic second derivative of electrostatic embedding QM/MM energy: normal mode analysis of plant cryptochrome

Karno Schwinn, Nicolas Ferré, Miquel Huix-Rotllant

Analytic second derivatives of electrostatic embedding (EE) quantum mechanics/molecular mechanics (QM/MM) energy are important for performing vibrational analysis and simulating vi…

cond-mat.str-el2019★ 11 cited

Frustration, ring exchange, and the absence of long-range order in EtMeSb[Pd(dmit)]: from first principles to many-body theory

E. P. Kenny, G. David, N. Ferré +2

We parameterize Hubbard and spin models for EtMeSb[Pd(dmit)] from broken symmetry density functional calculations. This gives a scalene triangular model where the large…

physics.comp-ph2019★ 171 cited

Quantum Package 2.0: An Open-Source Determinant-Driven Suite of Programs

Yann Garniron, Thomas Applencourt, Kevin Gasperich +16

\textsc{Quantum Package} is an open-source programming environment for quantum chemistry specially designed for wave function methods. Its main goal is the development of determina…

physics.chem-ph2017★ 67 cited

Ultrafast carbon monoxide photolysis and heme spin-crossover in myoglobin via nonadiabatic quantum dynamics

K. Falahati, H. Tamura, H. Burghardt +1

Light absorption of myoglobin triggers diatomic ligand photolysis and spin crossover transition of iron(II) that initiate protein conformational change. The photolysis and spin cro…