Joanna Jankowska

Dr. Joanna Jankowska

orcid

Research Focus & Metrics

Academic Career Length

16Years (2011-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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1 May 2018 in Journal of Energy Chemistry
Verified
Journal Article
Closed access
XZXin Zhou
Xin Zhou
JJJoanna Jankowska
Joanna Jankowska
HDHao Dong
Hao Dong
OPOleg V. Prezhdo
Oleg V. Prezhdo

25 August 2021 in Molecules
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Journal Article
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JJJoanna Jankowska
Joanna Jankowska
ASAndrzej L. Sobolewski
Andrzej L. Sobolewski
The excited-state intramolecular proton transfer (ESIPT) phenomenon is nowadays widely acknowledged to play a crucial role in many photobiological and photochemical processes. It is an extremely fast transformation, often taking place at sub-100 fs timescales. While its experimental characterization can be highly challenging, a rich manifold of theoretical approaches at different levels is nowadays available to support and guide experimental investigations. In this perspective, we summarize the state-of-the-art quantum-chemical methods, as well as molecular- and quantum-dynamics tools successfully applied in ESIPT process studies, focusing on a critical comparison of their specific properties.

28 May 2019 in The Journal of Chemical Physics
Verified
Journal Article
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PNParmeet Nijjar
Parmeet Nijjar
JJJoanna Jankowska
Joanna Jankowska
OPOleg Prezhdo
Oleg Prezhdo
We present a semiclassical approach for nonadiabatic molecular dynamics based on the Ehrenfest method with corrections for decoherence and detailed balance. Decoherence is described via a coherence penalty functional that drives dynamics away from regions in Hilbert space characterized by large values of coherences. Detailed balance is incorporated by modification of the off-diagonal matrix elements with a quantum correction factor used in semiclassical approximations to quantum time-correlation functions. Both decoherence and detailed balance corrections introduce nonlinear terms to the Schrödinger equation. At the same time, the simplicity of fully deterministic dynamics and a single trajectory for each initial condition is preserved. In contrast, surface hopping is stochastic and requires averaging over multiple realization of the stochastic process for each initial condition. The Ehrenfest-decoherence-detailed-balance (Ehrenfest-DDB) method is adapted to the classical path approximation and ab initio time-dependent density functional theory and applied to an experimentally studied nanoscale system consisting of a fluorophore molecule and an scanning tunneling microscopy tip and undergoing current-induced charge injection, cooling, and recombination. Ehrenfest-DDB produces time scales that are similar to those obtained with decoherence induced surface hopping, which is a popular nonadiabatic molecular dynamics technique applied to condensed matter. At long times, Ehrenfest-DDB dynamics slows down considerably because the detailed balance correction makes off-diagonal elements go to zero on approach to Boltzmann equilibrium. The Ehrenfest-DDB technique provides efficient means to study quantum dynamics in large systems.

5 June 2024 in Journal of Chemical Theory and Computation
Verified
Journal Article
Closed access
LZLina Zhang
Lina Zhang
SPSebastian V. Pios
Sebastian V. Pios
MMMikołaj Martyka
Mikołaj Martyka
FGFuchun Ge
Fuchun Ge
YHYi-Fan Hou
Yi-Fan Hou
PDPavlo O. Dral
Pavlo O. Dral

13 May 2025 in npj Computational Materials
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Journal Article
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MMMikołaj Martyka
Mikołaj Martyka
LZLina Zhang
Lina Zhang
FGFuchun Ge
Fuchun Ge
YHYi-Fan Hou
Yi-Fan Hou
JJJoanna Jankowska
Joanna Jankowska
PDPavlo O. Dral
Pavlo O. Dral
We present a robust protocol for affordable learning of electronic states to accelerate photophysical and photochemical molecular simulations. The protocol solves several issues precluding the widespread use of machine learning (ML) in excited-state simulations. We introduce a novel physics-informed multi-state ML model that can learn an arbitrary number of excited states across molecules, with accuracy better or similar to the accuracy of learning ground-state energies, where information on excited-state energies improves the quality of ground-state predictions. We also present gap-driven dynamics for accelerated sampling of the small-gap regions, which proves crucial for stable surface-hopping dynamics. Together, multi-state learning and gap-driven dynamics enable efficient active learning, furnishing robust models for surface-hopping simulations and helping to uncover long-time-scale oscillations in cis-azobenzene photoisomerization. Our active-learning protocol includes sampling based on physics-informed uncertainty quantification, ensuring the quality of each adiabatic surface, low error in energy gaps, and precise calculation of the hopping probability.

3 February 2017 in Physical Chemistry Chemical Physics
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Journal Article
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JJJoanna Jankowska
Joanna Jankowska
MBMario Barbatti
Mario Barbatti
JSJoanna Sadlej
Joanna Sadlej
ASAndrzej L. Sobolewski
Andrzej L. Sobolewski
Small molecular systems exhibiting Excited State Intramolecular Proton Transfer (ESIPT) attract considerable attention due to their possible role as ultrafast, efficient, and photostable molecular photoswitches. Here, by means of static potential energy profile scan and on-the-fly non-adiabatic dynamics simulations we study the photodeactivation process of a minimal-chromophore aromatic Schiff base, salicylidene methylamine (SMA), and its two derivatives 6-cyano-salicylidene methylamine (6-CN-SMA) and 3-hydroxy-salicylidene methylamine (3-OH-SMA). We show that the dominant character of the lowest excited singlet state – ππ* vs. nπ* – plays a crucial role in the system's photophysics and controls the ESIPT efficiency. We also show that the relative alignment of the ππ* and nπ* states may be controlled through chemical substitutions made to the aromatic ring of the Schiff-base molecule. We believe that our findings will improve the rational-design strategies employed for the ESIPT systems, especially in the context of their possible photoswitching.