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Wu X, Zhou W, Dong H, Ying P, Wang Y, Song B, Fan Z, Xiong S. Correcting force error-induced underestimation of lattice thermal conductivity in machine learning molecular dynamics. J Chem Phys 2024; 161:014103. [PMID: 38949595 DOI: 10.1063/5.0213811] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2024] [Accepted: 06/11/2024] [Indexed: 07/02/2024] Open
Abstract
Machine learned potentials (MLPs) have been widely employed in molecular dynamics simulations to study thermal transport. However, the literature results indicate that MLPs generally underestimate the lattice thermal conductivity (LTC) of typical solids. Here, we quantitatively analyze this underestimation in the context of the neuroevolution potential (NEP), which is a representative MLP that balances efficiency and accuracy. Taking crystalline silicon, gallium arsenide, graphene, and lead telluride as examples, we reveal that the fitting errors in the machine-learned forces against the reference ones are responsible for the underestimated LTC as they constitute external perturbations to the interatomic forces. Since the force errors of a NEP model and the random forces in the Langevin thermostat both follow a Gaussian distribution, we propose an approach to correcting the LTC by intentionally introducing different levels of force noises via the Langevin thermostat and then extrapolating to the limit of zero force error. Excellent agreement with experiments is obtained by using this correction for all the prototypical materials over a wide range of temperatures. Based on spectral analyses, we find that the LTC underestimation mainly arises from increased phonon scatterings in the low-frequency region caused by the random force errors.
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Affiliation(s)
- Xiguang Wu
- Guangzhou Key Laboratory of Low-Dimensional Materials and Energy Storage Devices, School of Materials and Energy, Guangdong University of Technology, Guangzhou 510006, China
| | - Wenjiang Zhou
- Department of Energy and Resources Engineering, Peking University, Beijing 100871, China
- School of Advanced Engineering, Great Bay University, Dongguan 523000, China
| | - Haikuan Dong
- College of Physical Science and Technology, Bohai University, Jinzhou 121013, China
| | - Penghua Ying
- Department of Physical Chemistry, School of Chemistry, Tel Aviv University, Tel Aviv 6997801, Israel
| | - Yanzhou Wang
- MSP Group, QTF Centre of Excellence, Department of Applied Physics, Aalto University, FI-00076 Aalto Espoo, Finland
| | - Bai Song
- Department of Energy and Resources Engineering, Peking University, Beijing 100871, China
- Department of Advanced Manufacturing and Robotics, Peking University, Beijing 100871, China
- National Key Laboratory of Advanced MicroNanoManufacture Technology, Beijing 100871, China
| | - Zheyong Fan
- College of Physical Science and Technology, Bohai University, Jinzhou 121013, China
| | - Shiyun Xiong
- Guangzhou Key Laboratory of Low-Dimensional Materials and Energy Storage Devices, School of Materials and Energy, Guangdong University of Technology, Guangzhou 510006, China
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Zeng J, Liang T, Zhang J, Liu D, Li S, Lu X, Han M, Yao Y, Xu JB, Sun R, Li L. Correlating Young's Modulus with High Thermal Conductivity in Organic Conjugated Small Molecules. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2024; 20:e2309338. [PMID: 38102097 DOI: 10.1002/smll.202309338] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Revised: 11/26/2023] [Indexed: 12/17/2023]
Abstract
Attaining elevated thermal conductivity in organic materials stands as a coveted objective, particularly within electronic packaging, thermal interface materials, and organic matrix heat exchangers. These applications have reignited interest in researching thermally conductive organic materials. The understanding of thermal transport mechanisms in these organic materials is currently constrained. This study concentrates on N, N'-dioctyl-3,4,9,10-perylenedicarboximide (PTCDI-C8), an organic conjugated crystal. A correlation between elevated thermal conductivity and augmented Young's modulus is substantiated through meticulous experimentation. Achievement via employing the physical vapor transport method, capitalizing on the robust C═C covalent linkages running through the organic matrix chain, bolstered by π-π stacking and noncovalent affiliations that intertwine the chains. The coexistence of these dynamic interactions, alongside the perpendicular alignment of PTCDI-C8 molecules, is confirmed through structural analysis. PTCDI-C8 thin film exhibits an out-of-plane thermal conductivity of 3.1 ± 0.1 W m-1 K-1, as determined by time-domain thermoreflectance. This outpaces conventional organic materials by an order of magnitude. Nanoindentation tests and molecular dynamics simulations elucidate how molecular orientation and intermolecular forces within PTCDI-C8 molecules drive the film's high Young's modulus, contributing to its elevated thermal conductivity. This study's progress offers theoretical guidance for designing high thermal conductivity organic materials, expanding their applications and performance potential.
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Affiliation(s)
- Jianhui Zeng
- Guangdong Key Laboratory for Processing and Forming of Advanced Metallic Materials, School of Mechanical & Automotive Engineering, South China University of Technology, 381 Wushan, Guangzhou, 510640, China
- National Key Laboratory of Materials for Integrated Circuits, Shenzhen Institute of Advanced Electronic Materials, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
| | - Ting Liang
- Department of Electronics Engineering, The Chinese University of Hong Kong, Hong Kong, China
| | - Jingjing Zhang
- National Key Laboratory of Materials for Integrated Circuits, Shenzhen Institute of Advanced Electronic Materials, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
- Nano Science and Technology Institute, University of Science and Technology of China, No. 166 Renai Road, Suzhou, 215000, China
| | - Daoqing Liu
- National Key Laboratory of Materials for Integrated Circuits, Shenzhen Institute of Advanced Electronic Materials, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
| | - Shiang Li
- Department of Physics, The Chinese University of Hong Kong, Hong Kong, 999077, China
| | - Xinhui Lu
- Department of Physics, The Chinese University of Hong Kong, Hong Kong, 999077, China
| | - Meng Han
- National Key Laboratory of Materials for Integrated Circuits, Shenzhen Institute of Advanced Electronic Materials, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
| | - Yimin Yao
- National Key Laboratory of Materials for Integrated Circuits, Shenzhen Institute of Advanced Electronic Materials, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
| | - Jian-Bin Xu
- Department of Electronics Engineering, The Chinese University of Hong Kong, Hong Kong, China
| | - Rong Sun
- National Key Laboratory of Materials for Integrated Circuits, Shenzhen Institute of Advanced Electronic Materials, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
| | - Liejun Li
- Guangdong Key Laboratory for Processing and Forming of Advanced Metallic Materials, School of Mechanical & Automotive Engineering, South China University of Technology, 381 Wushan, Guangzhou, 510640, China
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Liu S, Dupuis R, Fan D, Benzaria S, Bonneau M, Bhatt P, Eddaoudi M, Maurin G. Machine learning potential for modelling H 2 adsorption/diffusion in MOFs with open metal sites. Chem Sci 2024; 15:5294-5302. [PMID: 38577379 PMCID: PMC10988610 DOI: 10.1039/d3sc05612k] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Accepted: 03/05/2024] [Indexed: 04/06/2024] Open
Abstract
Metal-organic frameworks (MOFs) incorporating open metal sites (OMS) have been identified as promising sorbents for many societally relevant-adsorption applications including CO2 capture, natural gas purification and H2 storage. This has been ascribed to strong specific interactions between OMS and the guest molecules that enable the MOF to achieve an effective capture even under low gas pressure conditions. In particular, the presence of OMS in MOFs was demonstrated to substantially boost the H2 binding energy for achieving high adsorbed hydrogen densities and large usable hydrogen capacities. So far, there is a critical bottleneck to computationally attain a full understanding of the thermodynamics and dynamics of H2 in this sub-class of MOFs since the generic classical force fields (FFs) are known to fail to accurately describe the interactions between OMS and any guest molecules, in particular H2. This clearly hampers the computational-assisted identification of MOFs containing OMS for a target adsorption-related application since the standard high-throughput screening approach based on these generic FFs is not applicable. Therefore, there is a need to derive novel FFs to achieve accurate and effective evaluation of MOFs for H2 adsorption. On this path, as a proof-of-concept, the soc-MOF-1d containing OMS, previously envisaged as a potential platform for H2 adsorption, was selected as a benchmark material and a machine learning potential (MLP) was derived for the Al-soc-MOF-1d from a dataset initially generated by ab initio molecular dynamics (AIMD) simulations. This MLP was further implemented in MD simulations to explore the H2 binding modes as well as the temperature dependence distribution of H2 in the MOF pores from 10 K to 80 K. MLP-Grand Canonical Monte Carlo (GCMC) simulations were then performed to predict the H2 sorption isotherm of Al-soc-MOF-1d at 77 K that was further confirmed using sorption data we collected on this sample. As a further step, MLP-based molecular dynamics (MD) simulations were conducted to anticipate the kinetics of H2 in this MOF. This work delivers the first MLP able to describe accurately the interactions between the challenging H2 guest molecule and MOFs containing OMS. This innovative strategy applied to one of the most complex molecules owing to its highly polarizable nature, paves the way towards a more systematic accurate and efficient in silico assessment of MOFs containing OMS for H2 adsorption and beyond to the low-pressure capture of diverse molecules.
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Affiliation(s)
- Shanping Liu
- UMR 5253, CNRS, ENSCM, Institute Charles Gerhardt Montpellier, University of Montpellier Montpellier 34293 France
| | - Romain Dupuis
- UMR 5253, CNRS, ENSCM, Institute Charles Gerhardt Montpellier, University of Montpellier Montpellier 34293 France
- LMGC, Univ. Montpellier, CNRS Montpellier France
| | - Dong Fan
- UMR 5253, CNRS, ENSCM, Institute Charles Gerhardt Montpellier, University of Montpellier Montpellier 34293 France
| | - Salma Benzaria
- Division of Physical Science and Engineering, Advanced Membrane and Porous Materials Center, King Abdullah, University of Science and Technology (KAUST) Thuwal 23955-6900 Kingdom of Saudi Arabia
| | - Mickaele Bonneau
- Division of Physical Science and Engineering, Advanced Membrane and Porous Materials Center, King Abdullah, University of Science and Technology (KAUST) Thuwal 23955-6900 Kingdom of Saudi Arabia
| | - Prashant Bhatt
- Division of Physical Science and Engineering, Advanced Membrane and Porous Materials Center, King Abdullah, University of Science and Technology (KAUST) Thuwal 23955-6900 Kingdom of Saudi Arabia
| | - Mohamed Eddaoudi
- Division of Physical Science and Engineering, Advanced Membrane and Porous Materials Center, King Abdullah, University of Science and Technology (KAUST) Thuwal 23955-6900 Kingdom of Saudi Arabia
| | - Guillaume Maurin
- UMR 5253, CNRS, ENSCM, Institute Charles Gerhardt Montpellier, University of Montpellier Montpellier 34293 France
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Strasser N, Wieser S, Zojer E. Predicting Spin-Dependent Phonon Band Structures of HKUST-1 Using Density Functional Theory and Machine-Learned Interatomic Potentials. Int J Mol Sci 2024; 25:3023. [PMID: 38474269 DOI: 10.3390/ijms25053023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Revised: 02/28/2024] [Accepted: 02/29/2024] [Indexed: 03/14/2024] Open
Abstract
The present study focuses on the spin-dependent vibrational properties of HKUST-1, a metal-organic framework with potential applications in gas storage and separation. Employing density functional theory (DFT), we explore the consequences of spin couplings in the copper paddle wheels (as the secondary building units of HKUST-1) on the material's vibrational properties. By systematically screening the impact of the spin state on the phonon bands and densities of states in the various frequency regions, we identify asymmetric -COO- stretching vibrations as being most affected by different types of magnetic couplings. Notably, we also show that the DFT-derived insights can be quantitatively reproduced employing suitably parametrized, state-of-the-art machine-learned classical potentials with root-mean-square deviations from the DFT results between 3 cm-1 and 7 cm-1. This demonstrates the potential of machine-learned classical force fields for predicting the spin-dependent properties of complex materials, even when explicitly considering spins only for the generation of the reference data used in the force-field parametrization process.
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Affiliation(s)
- Nina Strasser
- Institute of Solid State Physics, NAWI Graz, Graz University of Technology, 8010 Graz, Austria
| | - Sandro Wieser
- Institute of Solid State Physics, NAWI Graz, Graz University of Technology, 8010 Graz, Austria
| | - Egbert Zojer
- Institute of Solid State Physics, NAWI Graz, Graz University of Technology, 8010 Graz, Austria
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Fan D, Ozcan A, Lyu P, Maurin G. Unravelling abnormal in-plane stretchability of two-dimensional metal-organic frameworks by machine learning potential molecular dynamics. NANOSCALE 2024; 16:3438-3447. [PMID: 38265127 DOI: 10.1039/d3nr05966a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/25/2024]
Abstract
Two-dimensional (2D) metal-organic frameworks (MOFs) hold immense potential for various applications due to their distinctive intrinsic properties compared to their 3D analogues. Herein, we designed a highly stable NiF2(pyrazine)2 2D MOF in silico with a two-dimensional periodic wine-rack architecture. Extensive first-principles calculations and molecular dynamics (MD) simulations based on a newly developed machine learning potential (MLP) revealed that this 2D MOF exhibits huge in-plane Poisson's ratio anisotropy. This results in anomalous negative in-plane stretchability, as evidenced by an uncommon decrease in its in-plane area upon the application of uniaxial tensile strain, which makes this 2D MOF particularly attractive for flexible wearable electronics and ultra-thin sensor applications. We further demonstrated the unique capability of MLP to accurately predict the finite-temperature properties of MOFs on a large scale, exemplified by MLP-MD simulations with a dimension of 28.2 × 28.2 nm2, relevant to the length scale experimentally attainable for the fabrication of MOF films.
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Affiliation(s)
- Dong Fan
- ICGM, Univ. Montpellier, CNRS, ENSCM, Montpellier, 34095, France.
| | - Aydin Ozcan
- TUBİTAK Marmara Research Center, Materials Technologies, Gebze, Kocaeli, 41470, Turkey
| | - Pengbo Lyu
- Hunan Provincial Key Laboratory of Thin Film Materials and Devices, School of Material Sciences and Engineering, Xiangtan University, Xiangtan, 411105, People's Republic of China
| | - Guillaume Maurin
- ICGM, Univ. Montpellier, CNRS, ENSCM, Montpellier, 34095, France.
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Ying P, Fan Z. Combining the D3 dispersion correction with the neuroevolution machine-learned potential. JOURNAL OF PHYSICS. CONDENSED MATTER : AN INSTITUTE OF PHYSICS JOURNAL 2023; 36:125901. [PMID: 38052090 DOI: 10.1088/1361-648x/ad1278] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/08/2023] [Accepted: 12/05/2023] [Indexed: 12/07/2023]
Abstract
Machine-learned potentials (MLPs) have become a popular approach of modeling interatomic interactions in atomistic simulations, but to keep the computational cost under control, a relatively short cutoff must be imposed, which put serious restrictions on the capability of the MLPs for modeling relatively long-ranged dispersion interactions. In this paper, we propose to combine the neuroevolution potential (NEP) with the popular D3 correction to achieve a unified NEP-D3 model that can simultaneously model relatively short-ranged bonded interactions and relatively long-ranged dispersion interactions. We show that improved descriptions of the binding and sliding energies in bilayer graphene can be obtained by the NEP-D3 approach compared to the pure NEP approach. We implement the D3 part into thegpumdpackage such that it can be used out of the box for many exchange-correlation functionals. As a realistic application, we show that dispersion interactions result in approximately a 10% reduction in thermal conductivity for three typical metal-organic frameworks.
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Affiliation(s)
- Penghua Ying
- Department of Physical Chemistry, School of Chemistry, Tel Aviv University, Tel Aviv 6997801, Israel
| | - Zheyong Fan
- College of Physical Science and Technology, Bohai University, Jinzhou 121013, People's Republic of China
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