Author: Arif Ullah

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The work “Physics-Informed Neural Networks and Beyond: Enforcing Physical Constraints in Quantum Dissipative Dynamics” performed in collaboration of Professor Pavlo O. Dral (Xiamen University) and Assistant Professor Arif Ullah (Anhui University) was published in Digital Discovery. In this blog, Arif Ullah highlights this …

Physically-consistent quantum dissipative dynamics simulations with neural networks Read more »

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In a recent article published in Frontiers in Physics, we introduce QD3SET-1, a database consisting of 8 data sets that provide the time-evolved population and coherence dynamics for two widely studied systems: the so-called spin-boson model and FMO complex. The …

QD3SET-1: A Database with Quantum Dissipative Dynamics Data Sets Read more »

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In the work published in the Journal of Physical Chemistry Letters, we have proposed a one-shot trajectory learning (OSTL) approach that allows an ultrafast prediction of 10-ps-long quantum dynamics of an open quantum system just in 70 milliseconds. OSTL approach takes …

One-Shot Trajectory Learning of Open Quantum Systems Dynamics Read more »

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In the work published in Nature Communications, we have developed a blazingly fast artificial intelligence (AI)-based quantum dynamics (QD) approach with applications to excitation energy transfer in the well-known Fenna–Matthews–Olson (FMO) complex found in green sulfur bacteria.

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In the work published in New Journal of Physics, we combine machine learning (ML) with the numerically exact hierarchical equations of motion (HEOM) approach, propagating quantum dynamics of a two-state system (spin-boson model) with only ca. 10% of the HEOM …

Speeding up quantum dissipative dynamics of open systems with kernel methods Read more »