Zobrazeno 1 - 10
of 82
pro vyhledávání: '"Pavlo O, Dral"'
Publikováno v:
Scientific Data, Vol 10, Iss 1, Pp 1-11 (2023)
Abstract Multidimensional surfaces of quantum chemical properties, such as potential energies and dipole moments, are common targets for machine learning, requiring the development of robust and diverse databases extensively exploring molecular confi
Externí odkaz:
https://doaj.org/article/b893e5abcd9644e6b8fe17a30b26266d
Publikováno v:
Frontiers in Physics, Vol 11 (2023)
Externí odkaz:
https://doaj.org/article/d854e45a7e12434e953fbc240d00f6d3
Autor:
Yuming Su, Yiheng Dai, Yifan Zeng, Caiyun Wei, Yangtao Chen, Fuchun Ge, Peikun Zheng, Da Zhou, Pavlo O. Dral, Cheng Wang
Publikováno v:
Advanced Science, Vol 10, Iss 8, Pp n/a-n/a (2023)
Abstract Molecules with strong two‐photon absorption (TPA) are important in many advanced applications such as upconverted laser and photodynamic therapy, but their design is hampered by the high cost of experimental screening and accurate quantum
Externí odkaz:
https://doaj.org/article/1b7d4cb83504495ba9507aee341c0a6f
Autor:
Arif Ullah, Pavlo O. Dral
Publikováno v:
Nature Communications, Vol 13, Iss 1, Pp 1-8 (2022)
Simulations of energy transfer in light-harvesting complexes are computationally very demanding. Here the authors apply an artificial intelligence quantum dissipative algorithm to study the excited state energy transfer dynamics in a light-harvesting
Externí odkaz:
https://doaj.org/article/4f04fbe2ce9a404696a221713473b1f3
Publikováno v:
Scientific Data, Vol 9, Iss 1, Pp 1-10 (2022)
Measurement(s) potential energy surfaces Technology Type(s) quantum chemistry computational methods
Externí odkaz:
https://doaj.org/article/38afbdf9694d4e47b993312c908a8d4c
Publikováno v:
Nature Communications, Vol 12, Iss 1, Pp 1-13 (2021)
Artificial intelligence is combined with quantum mechanics to break the limitations of traditional methods and create a new general-purpose method for computational chemistry simulations with high accuracy, speed and transferability.
Externí odkaz:
https://doaj.org/article/4e8a65d514a545c3b9b9c5a6df51d4b0
Autor:
Miriam Hauschild, Michal Borkowski, Pavlo O. Dral, Tomasz Marszalek, Frank Hampel, Gaozhan Xie, Jan Freudenberg, Uwe H. F. Bunz, Milan Kivala
Publikováno v:
Organic Materials, Vol 02, Iss 03, Pp 204-213 (2020)
Abstract We report the synthesis of 5,7,12,14-tetraphenyl-substituted 6,13-dihydro-6,13-diazapentacene and its fully aromatic 6,13-diazapentacene congener. Both arylated diazapentacenes were characterized by X-ray crystallography to investigate their
Externí odkaz:
https://doaj.org/article/04a55269c82d4e0bbc0d42b62ef5506b
Autor:
Armando de Rezende, Mahdi Malmali, Pavlo O. Dral, Hans Lischka, Daniel Tunega, Adelia J. A. Aquino
Publikováno v:
The Journal of Physical Chemistry C. 126:12184-12196
Autor:
Arif Ullah, Pavlo O. Dral
Machine learning has emerged as a promising paradigm to study the quantum dissipative dynamics of open quantum systems. To facilitate the use of our recently published ML-based approaches for quantum dissipative dynamics, here we present an open-sour
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6e2d805bcd4810bbea8b58743edd5c2e
https://doi.org/10.26434/chemrxiv-2023-0xkv1
https://doi.org/10.26434/chemrxiv-2023-0xkv1
Ultra-fast semi-empirical quantum chemistry for high-throughput computational campaigns with SPARROW
Publikováno v:
The Journal of Chemical Physics, 158 (5)
Semi-empirical quantum chemical approaches are known to compromise accuracy for the feasibility of calculations on huge molecules. However, the need for ultrafast calculations in interactive quantum mechanical studies, high-throughput virtual screeni
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2f40546c9325762f3cbde44c1e3044b3
https://hdl.handle.net/20.500.11850/598917
https://hdl.handle.net/20.500.11850/598917