Details
| Original language | English |
|---|---|
| Article number | 2201370 |
| Journal | Advanced energy materials |
| Volume | 12 |
| Issue number | 32 |
| Early online date | 21 Jul 2022 |
| Publication status | Published - 25 Aug 2022 |
Abstract
Accurate examination of electricity generation stemming from higher-order deformation (flexoelectricity) in 2D layered materials is a highly challenging task to be investigated with either conventional computational or experimental tools. To address this challenge herein an innovative and computationally efficient approach on the basis of density functional theory (DFT) and machine-learning interatomic potentials (MLIPs) with incorporated long-range interactions to accurately investigate the flexoelectric energy conversion in 2D van der Waals (vdW) bilayers is proposed. In this approach, short-range interactions are accurately defined using the moment tensor potentials trained over computationally inexpensive DFT-based datasets. The long-range electrostatic (charge and dipole) and vdW interaction parameters are calibrated from DFT simulations. Elaborated comparison of mechanical and piezoelectric properties extracted from the herein proposed approach with available data confirms the accuracy of the devised computational strategy. It is shown that the bilayer transition metal dichalcogenides can show a flexoelectric coefficient 2–7 times larger than their monolayer counterparts. Noticeably, this enhancement reaches up to 20 times for Janus diamane and fluorinated boron-nitrogen derivatives of diamane bilayers. The presented results improve the understanding of the flexoelectric effect in vdW heterostructures and moreover the proposed MLIP-based methodology offers a robust tool to improve the design of novel energy harvesting devices.
Keywords
- bending deformation, charge-dipole model, flexoelectricity, machine learning, van der Waals bilayers
ASJC Scopus subject areas
- Energy(all)
- Renewable Energy, Sustainability and the Environment
- Materials Science(all)
- General Materials Science
Sustainable Development Goals
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In: Advanced energy materials, Vol. 12, No. 32, 2201370, 25.08.2022.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Machine-Learning-Based Exploration of Bending Flexoelectricity in Novel 2D Van der Waals Bilayers
AU - Javvaji, Brahmanandam
AU - Zhuang, Xiaoying
AU - Rabczuk, Timon
AU - Mortazavi, Bohayra
N1 - Funding Information: The authors gratefully acknowledge the sponsorship from the ERC Starting Grant COTOFLEXI (Grant No. 802205). The authors also acknowledge the support of the cluster system team at the Leibniz Universität of Hannover, Germany. The authors would like to thank Prof. Alexander Shapeev for his discussions and support to this work.
PY - 2022/8/25
Y1 - 2022/8/25
N2 - Accurate examination of electricity generation stemming from higher-order deformation (flexoelectricity) in 2D layered materials is a highly challenging task to be investigated with either conventional computational or experimental tools. To address this challenge herein an innovative and computationally efficient approach on the basis of density functional theory (DFT) and machine-learning interatomic potentials (MLIPs) with incorporated long-range interactions to accurately investigate the flexoelectric energy conversion in 2D van der Waals (vdW) bilayers is proposed. In this approach, short-range interactions are accurately defined using the moment tensor potentials trained over computationally inexpensive DFT-based datasets. The long-range electrostatic (charge and dipole) and vdW interaction parameters are calibrated from DFT simulations. Elaborated comparison of mechanical and piezoelectric properties extracted from the herein proposed approach with available data confirms the accuracy of the devised computational strategy. It is shown that the bilayer transition metal dichalcogenides can show a flexoelectric coefficient 2–7 times larger than their monolayer counterparts. Noticeably, this enhancement reaches up to 20 times for Janus diamane and fluorinated boron-nitrogen derivatives of diamane bilayers. The presented results improve the understanding of the flexoelectric effect in vdW heterostructures and moreover the proposed MLIP-based methodology offers a robust tool to improve the design of novel energy harvesting devices.
AB - Accurate examination of electricity generation stemming from higher-order deformation (flexoelectricity) in 2D layered materials is a highly challenging task to be investigated with either conventional computational or experimental tools. To address this challenge herein an innovative and computationally efficient approach on the basis of density functional theory (DFT) and machine-learning interatomic potentials (MLIPs) with incorporated long-range interactions to accurately investigate the flexoelectric energy conversion in 2D van der Waals (vdW) bilayers is proposed. In this approach, short-range interactions are accurately defined using the moment tensor potentials trained over computationally inexpensive DFT-based datasets. The long-range electrostatic (charge and dipole) and vdW interaction parameters are calibrated from DFT simulations. Elaborated comparison of mechanical and piezoelectric properties extracted from the herein proposed approach with available data confirms the accuracy of the devised computational strategy. It is shown that the bilayer transition metal dichalcogenides can show a flexoelectric coefficient 2–7 times larger than their monolayer counterparts. Noticeably, this enhancement reaches up to 20 times for Janus diamane and fluorinated boron-nitrogen derivatives of diamane bilayers. The presented results improve the understanding of the flexoelectric effect in vdW heterostructures and moreover the proposed MLIP-based methodology offers a robust tool to improve the design of novel energy harvesting devices.
KW - bending deformation
KW - charge-dipole model
KW - flexoelectricity
KW - machine learning
KW - van der Waals bilayers
UR - http://www.scopus.com/inward/record.url?scp=85134520237&partnerID=8YFLogxK
U2 - 10.1002/aenm.202201370
DO - 10.1002/aenm.202201370
M3 - Article
AN - SCOPUS:85134520237
VL - 12
JO - Advanced energy materials
JF - Advanced energy materials
SN - 1614-6832
IS - 32
M1 - 2201370
ER -