Machine Learning-Based Charge Transport Computation for Pentacene

被引:41
|
作者
Lederer, Jonas [1 ]
Kaiser, Waldemar [1 ]
Mattoni, Alessandro [2 ]
Gagliardi, Alessio [1 ]
机构
[1] Tech Univ Munich, Dept Elect & Comp Engn, Karlstr 45, D-80333 Munich, Germany
[2] CNR IOM SLACS Cagliari, Ist Officina Mat, I-09042 Monserrato, Italy
关键词
charge transport; machine learning; multiscale approach; organic semiconductors; pentacene; FIELD-EFFECT MOBILITY; MM3; FORCE-FIELD; ORGANIC SEMICONDUCTORS; MOLECULAR-MECHANICS; REORGANIZATION ENERGY; CARRIER TRANSPORT; MONTE-CARLO; DYNAMICS; CRYSTALS; POLARIZATION;
D O I
10.1002/adts.201800136
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Insight into the relation between morphology and transport properties of organic semiconductors can be gained using multiscale simulations. Since computing electronic properties, such as the intermolecular transfer integral, using quantum chemical (QC) methods requires a high computational cost, existing models assume several approximations. A machine learning (ML)-based multiscale approach is presented that allows to simulate charge transport in organic semiconductors considering the static disorder within disordered crystals. By mapping fingerprints of dimers to their respective transfer integral, a kernel ridge regression ML algorithm for the prediction of charge transfer integrals is trained and evaluated. Since QC calculations of the electronic structure must be performed only once, the use of ML reduces the computation time radically, while maintaining the prediction error small. Transfer integrals predicted by ML are utilized for the computation of charge carrier mobilities using off-lattice kinetic Monte Carlo (kMC) simulations. Benefiting from the rapid performance of ML, microscopic processes can be described accurately without the need for phenomenological approximations. The multiscale system is tested with the well-known molecular semiconductor pentacene. The presented methodology allows reproducing the experimentally observed anisotropy of the mobility and enables a fast estimation of the impact of disorder.
引用
收藏
页数:11
相关论文
共 50 条
  • [41] Machine learning-based phishing attack detection
    Hossain S.
    Sarma D.
    Chakma R.J.
    International Journal of Advanced Computer Science and Applications, 2020, 11 (09): : 378 - 388
  • [42] Machine learning-based scheduling: a bibliometric perspective
    Li, Shiyun
    Yu, Tianzong
    Cao, Xu
    Pei, Zhi
    Yi, Wenchao
    Chen, Yong
    Lv, Ruifeng
    IET COLLABORATIVE INTELLIGENT MANUFACTURING, 2021, 3 (02) : 131 - 146
  • [43] Machine learning-based infant crying interpretation
    Hammoud, Mohammed
    Getahun, Melaku N.
    Baldycheva, Anna
    Somov, Andrey
    FRONTIERS IN ARTIFICIAL INTELLIGENCE, 2024, 7
  • [44] Machine Learning-Based Colorectal Cancer Detection
    Blanes-Vidal, Victoria
    Baatrup, Gunnar
    Nadimi, Esmaeil S.
    PROCEEDINGS OF THE 2018 CONFERENCE ON RESEARCH IN ADAPTIVE AND CONVERGENT SYSTEMS (RACS 2018), 2018, : 43 - 46
  • [45] Machine Learning-Based Design Concept Evaluation
    Camburn, Bradley
    He, Yuejun
    Raviselvam, Sujithra
    Luo, Jianxi
    Wood, Kristin
    JOURNAL OF MECHANICAL DESIGN, 2020, 142 (03)
  • [46] Machine learning-based test smell detection
    Valeria Pontillo
    Dario Amoroso d’Aragona
    Fabiano Pecorelli
    Dario Di Nucci
    Filomena Ferrucci
    Fabio Palomba
    Empirical Software Engineering, 2024, 29
  • [47] Supervised Machine Learning-based Fall Detection
    Caya, Meo Vincent C.
    Magwili, Glenn V.
    Agulto, Denver L.
    John Laranang, Russell
    Palomo, Louisse Kayle G.
    2018 IEEE 10TH INTERNATIONAL CONFERENCE ON HUMANOID, NANOTECHNOLOGY, INFORMATION TECHNOLOGY, COMMUNICATION AND CONTROL, ENVIRONMENT AND MANAGEMENT (HNICEM), 2018,
  • [48] Battery safety: Machine learning-based prognostics
    Zhao, Jingyuan
    Feng, Xuning
    Pang, Quanquan
    Fowler, Michael
    Lian, Yubo
    Ouyang, Minggao
    Burke, Andrew F.
    PROGRESS IN ENERGY AND COMBUSTION SCIENCE, 2024, 102
  • [49] Machine learning-based prediction models in neurosurgery
    Habashy, Karl J.
    Arrieta, Victor A.
    Feghali, James
    NEUROSURGICAL FOCUS, 2023, 55 (03)
  • [50] An Analysis of Machine Learning-Based Semantic Matchmaking
    Karabulut, Erkan
    Sofia, Rute C. C.
    IEEE ACCESS, 2023, 11 : 27829 - 27842