An evolutionary de novo design method is presented to fine-tune the excitation energies of molecules calculated using time-dependent density functional theory (TD-DFT). The approach is applied to a pi-conjugated molecular system, azobenzene. The excitation energies for all the molecules generated by the evolutionary design scheme were computed at TD-DFT level on multiple supercomputing clusters. A software developed in-house was used to automatically set up the TD-DFT calculations and exploit the advantages of parallelization and thereby speed up the process of obtaining results for the evolutionary de novo program. Our proposed optimisation scheme is able to propose new azobenzene structures with significant decrease in excitation energies.
机构:Shenzhen University,Big Data Institute, College of Computer Science and Software Engineering, Guangdong Key Laboratory of Intelligent Information Processing
Shuyue Chen
Hongjie Wang
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机构:Shenzhen University,Big Data Institute, College of Computer Science and Software Engineering, Guangdong Key Laboratory of Intelligent Information Processing
Hongjie Wang
Qin Wang
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机构:Shenzhen University,Big Data Institute, College of Computer Science and Software Engineering, Guangdong Key Laboratory of Intelligent Information Processing
Qin Wang
Guohua Zhang
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机构:Shenzhen University,Big Data Institute, College of Computer Science and Software Engineering, Guangdong Key Laboratory of Intelligent Information Processing
Guohua Zhang
International Journal of Machine Learning and Cybernetics,
2020,
11
: 1631
-
1641
机构:
Shenzhen Univ, Big Data Inst, Coll Comp Sci & Software Engn, Guangdong Key Lab Intelligent Informat Proc, Shenzhen 518060, Guangdong, Peoples R ChinaShenzhen Univ, Big Data Inst, Coll Comp Sci & Software Engn, Guangdong Key Lab Intelligent Informat Proc, Shenzhen 518060, Guangdong, Peoples R China
Chen, Shuyue
Wang, Hongjie
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机构:
China Construct Rail Electrificat Engn Co Ltd, Beijing, Peoples R ChinaShenzhen Univ, Big Data Inst, Coll Comp Sci & Software Engn, Guangdong Key Lab Intelligent Informat Proc, Shenzhen 518060, Guangdong, Peoples R China
Wang, Hongjie
Wang, Qin
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机构:
Shenzhen Univ, Big Data Inst, Coll Comp Sci & Software Engn, Guangdong Key Lab Intelligent Informat Proc, Shenzhen 518060, Guangdong, Peoples R ChinaShenzhen Univ, Big Data Inst, Coll Comp Sci & Software Engn, Guangdong Key Lab Intelligent Informat Proc, Shenzhen 518060, Guangdong, Peoples R China
Wang, Qin
Zhang, Guohua
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机构:
China Construct Rail Electrificat Engn Co Ltd, Beijing, Peoples R ChinaShenzhen Univ, Big Data Inst, Coll Comp Sci & Software Engn, Guangdong Key Lab Intelligent Informat Proc, Shenzhen 518060, Guangdong, Peoples R China
机构:
Univ Madras, Madras Christian Coll Autonomous, Dept Chem, Chennai 600059, Tamil Nadu, India
Univ Madras, Govt Arts Coll Autonomous, Dept Chem, Chennai 600035, Tamil Nadu, IndiaUniv Madras, Madras Christian Coll Autonomous, Dept Chem, Chennai 600059, Tamil Nadu, India
Sridhar, Serangolam Krishnasami
Solomon, Rajadurai Vijay
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Univ Madras, Madras Christian Coll Autonomous, Dept Chem, Chennai 600059, Tamil Nadu, IndiaUniv Madras, Madras Christian Coll Autonomous, Dept Chem, Chennai 600059, Tamil Nadu, India