add papers in 2022

Author: njzjz

source/_data/pub.bib

@@ -2000,7 +2000,7 @@ @Article{Wu_PhysRevB_2021_v103_p024108
 @Article{Liang_ACSApplMaterInterfaces_2021_v13_p4034,
     author =   {Wenshuo Liang and Guimin Lu and Jianguo Yu},
     title =    {{Machine-Learning-Driven Simulations on Microstructure and
-             Thermophysical Properties of MgCl<sub>2</sub>{\textendash}KCl Eutectic}},
+             Thermophysical Properties of MgCl<sub>2</sub>{-}KCl Eutectic}},
     journal =  {ACS Appl. Mater. Interfaces},
     year =     2021,
     volume =   13,
@@ -2081,3 +2081,330 @@ @Article{Yue_JChemPhys_2021_v154_p034111
     doi =      {10.1063/5.0031215},
 }
 
+@Article{Pegolo_npjComputMater_2022_v8_p24,
+    author =   {Paolo Pegolo and Stefano Baroni and Federico Grasselli},
+    title =    {{Temperature- and vacancy-concentration-dependence of heat transport in
+             Li3ClO from multi-method numerical simulations}},
+    journal =  {npj Comput Mater},
+    year =     2022,
+    volume =   8,
+    issue =    1,
+    pages =    24,
+    annote =   {<jats:title>Abstract</jats:title><jats:p>Despite governing heat
+             management in any realistic device, the microscopic mechanisms of heat
+             transport in all-solid-state electrolytes are poorly known: existing
+             calculations, all based on simplistic semi-empirical models, are
+             unreliable for superionic conductors and largely overestimate their
+             thermal conductivity. In this work, we deploy a combination of state-
+             of-the-art methods to calculate the thermal conductivity of a
+             prototypical Li-ion conductor, the Li<jats:sub>3</jats:sub>ClO
+             antiperovskite. By leveraging ab initio, machine learning, and force-
+             field descriptions of interatomic forces, we are able to reveal the
+             massive role of anharmonic interactions and diffusive defects on the
+             thermal conductivity and its temperature dependence, and to eventually
+             embed their effects into a simple rationale which is likely applicable
+             to a wide class of ionic conductors.</jats:p>},
+    doi =      {10.1038/s41524-021-00693-4},
+}
+
+@article{Dai_JMaterSciTech_2022_v123_p26,
+    title = {Grain boundary segregation induced strong UHTCs at elevated temperatures: A universal mechanism from conventional UHTCs to high entropy UHTCs},
+    journal = {Journal of Materials Science & Technology},
+    volume = {123},
+    pages = {26-33},
+    year = {2022},
+    issn = {1005-0302},
+    doi = {https://doi.org/10.1016/j.jmst.2021.12.074},
+    url = {https://www.sciencedirect.com/science/article/pii/S100503022200264X},
+    author = {Fu-Zhi Dai and Bo Wen and Yinjie Sun and Yixiao Ren and Huimin Xiang and Yanchun Zhou},
+    keywords = {UHTCs, High entropy ceramics, Grain boundary segregation, High-temperature strength, Machine learning potential},
+    abstract = {Ultra-high temperature ceramics (UHTCs) exhibit a unique combination of excellent properties, including ultra-high melting point, excellent chemical stability, and good oxidation resistance, which make them promising candidates for aerospace and nuclear applications. However, the degradation of high-temperature strength is one of the main limitations for their ultra-high temperature applications. Thus, searching for mechanisms that can help to develop high-performance UHTCs with good high-temperature mechanical properties is urgently needed. To achieve this goal, grain boundary segregation of a series of carbides, including conventional, medium entropy, and high entropy transition metal carbides, i.e., Zr0.95W0.05C, TiZrHfC3, ZrHfNbTaC4, TiZrHfNbTaC5, were studied by atomistic simulations with a fitted Deep Potential (DP), and the effects of segregation on grain boundary strength were emphasized. For all the studied carbides, grain boundary segregations are realized, which are dominated by the atomic size effect. In addition, tensile simulations indicate that grain boundaries (GBs) will usually be strengthened due to segregation. Our simulation results reveal that grain boundary segregation may be a universal mechanism in enhancing the high-temperature strength of both conventional UHTCs and medium/high entropy UHTCs, since GBs play a key role in controlling the fracture of UHTCs at elevated temperatures.}
+}
+
+@Article{Fu_JournalofEnergyChemistry_2022_v70_p59,
+    author =   {Zhong-Heng Fu and Xiang Chen and Nan Yao and Xin Shen and Xia-Xia Ma
+             and Shuai Feng and Shuhao Wang and Rui Zhang and Linfeng Zhang and
+             Qiang Zhang},
+    title =    {{The chemical origin of temperature-dependent lithium-ion concerted
+             diffusion in sulfide solid electrolyte Li10GeP2S12}},
+    journal =  {Journal of Energy Chemistry},
+    year =     2022,
+    volume =   70,
+    pages =    {59--66},
+    doi =      {10.1016/j.jechem.2022.01.018},
+}
+
+@Article{Sun_JournalofMolecularLiquids_2022_v353_p118787,
+    author =   {Yuhan Sun and Min Tan and Tao Li and Junguo Li and Bo Shang},
+    title =    {{Study on the structural properties of refining slags by molecular
+             dynamics with deep learning potential}},
+    journal =  {Journal of Molecular Liquids},
+    year =     2022,
+    volume =   353,
+    pages =    118787,
+    doi =      {10.1016/j.molliq.2022.118787},
+}
+
+@Article{Huang_EnergyandAI_2022_v8_p100135,
+    author =   {Xiaona Huang and Yidi Shen and Qi An},
+    title =    {{Nanotwinning induced decreased lattice thermal conductivity of high
+             temperature thermoelectric boron subphosphide (B12P2) from deep
+             learning potential simulations}},
+    journal =  {Energy and AI},
+    year =     2022,
+    volume =   8,
+    pages =    100135,
+    doi =      {10.1016/j.egyai.2022.100135},
+}
+
+@Article{Zhang_JChemPhys_2022_v156_p124107,
+    author =   {Linfeng Zhang and Han Wang and Maria Carolina Muniz and Athanassios Z
+             Panagiotopoulos and Roberto Car and Weinan E},
+    title =    {{A deep potential model with long-range electrostatic interactions}},
+    journal =  {J. Chem. Phys.},
+    year =     2022,
+    volume =   156,
+    issue =    12,
+    pages =    124107,
+    annote =   {Machine learning models for the potential energy of multi-atomic
+             systems, such as the deep potential (DP) model, make molecular
+             simulations with the accuracy of quantum mechanical density functional
+             theory possible at a cost only moderately higher than that of
+             empirical force fields. However, the majority of these models lack
+             explicit long-range interactions and fail to describe properties that
+             derive from the Coulombic tail of the forces. To overcome this
+             limitation, we extend the DP model by approximating the long-range
+             electrostatic interaction between ions (nuclei + core electrons) and
+             valence electrons with that of distributions of spherical Gaussian
+             charges located at ionic and electronic sites. The latter are
+             rigorously defined in terms of the centers of the maximally localized
+             Wannier distributions, whose dependence on the local atomic
+             environment is modeled accurately by a deep neural network. In the DP
+             long-range (DPLR) model, the electrostatic energy of the Gaussian
+             charge system is added to short-range interactions that are
+             represented as in the standard DP model. The resulting potential
+             energy surface is smooth and possesses analytical forces and virial.
+             Missing effects in the standard DP scheme are recovered, improving on
+             accuracy and predictive power. By including long-range electrostatics,
+             DPLR correctly extrapolates to large systems the potential energy
+             surface learned from quantum mechanical calculations on smaller
+             systems. We illustrate the approach with three examples: the potential
+             energy profile of the water dimer, the free energy of interaction of a
+             water molecule with a liquid water slab, and the phonon dispersion
+             curves of the NaCl crystal.},
+    doi =      {10.1063/5.0083669},
+}
+
+@Article{Sun_ACSApplMaterInterfaces_2022_v14_p11493,
+    author =   {Jie Sun and Cunzhi Zhang and Zhonghua Yang and Yiheng Shen and Ming Hu
+             and Qian Wang},
+    title =    {{Four-Phonon Scattering Effect and Two-Channel Thermal Transport in
+             Two-Dimensional Paraelectric SnSe}},
+    journal =  {ACS Appl. Mater. Interfaces},
+    year =     2022,
+    volume =   14,
+    issue =    9,
+    pages =    {11493--11499},
+    annote =   {In recent years, increased attention has been paid to the study of
+             four-phonon interactions and diffusion transport in three-dimensional
+             (3D) thermoelectric materials because they play an essential role in
+             understanding the thermal transport process. In this work, we study
+             four-phonon scattering and diffusion transport in two-dimensional (2D)
+             thermoelectric materials using the paraelectric phase of 2D SnSe as an
+             example. The inherent soft phonon modes are treated by the self-
+             consistent phonon theory, which considers the temperature-induced
+             renormalization of the phonons. Based on density functional theory and
+             the Peierls-Boltzmann transport equation for phonons, we show that the
+             four-phonon interactions can reduce the thermal conductivity of the 2D
+             SnSe sheet by nearly 40% due to the collapse of soft optical modes,
+             and the contribution of diffusion transport to the total thermal
+             conductivity accounts for 14% at a high temperature of 800 K due to
+             the short phonon mean free path approaching the Ioffe-Regel limit,
+             suggesting the two-channel transport in this system. The results are
+             further confirmed by using the machine learning-assisted molecular
+             dynamics simulations. This work provides a new insight into the
+             physical mechanisms for thermal transport in 2D systems with strong
+             anharmonic effects.},
+    doi =      {10.1021/acsami.1c24488},
+}
+
+@Article{Han_BriefBioinform_2022_v23_pNone,
+    author =   {Yanqiang Han and Zhilong Wang and An Chen and Imran Ali and Junfei Cai
+             and Simin Ye and Jinjin Li},
+    title =    {{An inductive transfer learning force field (ITLFF) protocol builds
+             protein force fields in seconds}},
+    journal =  {Brief. Bioinform.},
+    year =     2022,
+    volume =   23,
+    issue =    2,
+    annote =   {Accurate simulation of protein folding is a unique challenge in
+             understanding the physical process of protein folding, with important
+             implications for protein design and drug discovery. Molecular dynamics
+             simulation strongly requires advanced force fields with high accuracy
+             to achieve correct folding. However, the current force fields are
+             inaccurate, inapplicable and inefficient. We propose a machine
+             learning protocol, the inductive transfer learning force field
+             (ITLFF), to construct protein force fields in seconds with any level
+             of accuracy from a small dataset. This process is achieved by
+             incorporating an inductive transfer learning algorithm into deep
+             neural networks, which learn knowledge of any high-level calculations
+             from a large dataset of low-level method. Here, we use a double-hybrid
+             density functional theory (DFT) as a case functional, but ITLFF is
+             suitable for any high-precision functional. The performance of the
+             selected 18 proteins indicates that compared with the fragment-based
+             double-hybrid DFT algorithm, the force field constructed by ITLFF
+             achieves considerable accuracy with a mean absolute error of
+             0.0039{~}kcal/mol/atom for energy and a root mean square error of
+             2.57{~}$\mathrm{kcal}/\mathrm{mol}/{\AA}$ for force, and it is more
+             than 30{~}000 times faster and obtains more significant efficiency
+             benefits as the system increases. The outstanding performance of ITLFF
+             provides promising prospects for accurate and efficient protein
+             dynamic simulations and makes an important step toward protein folding
+             simulation. Due to the ability of ITLFF to utilize the knowledge
+             acquired in one task to solve related problems, it is also applicable
+             for various problems in biology, chemistry and material science.},
+    doi =      {10.1093/bib/bbab590},
+}
+
+@article{Liu_JVacuumSciTech_2022_v40_p023205,
+    author = {Liu,Da-Jiang  and Evans,James W. },
+    title = {Sulfur-enhanced dynamics of coinage metal(111) surfaces: Step edges versus terraces as locations for metal-sulfur complex formation},
+    journal = {Journal of Vacuum Science \& Technology A},
+    volume = {40},
+    number = {2},
+    pages = {023205},
+    year = {2022},
+    doi = {10.1116/6.0001408},
+}
+
+@Article{Wang_ModellingSimulMaterSciEng_2022_v30_p025003,
+    author =   {YiNan Wang and LinFeng Zhang and Ben Xu and XiaoYang Wang and Han Wang},
+    title =    {{A generalizable machine learning potential of Ag{\textendash}Au
+             nanoalloys and its application to surface reconstruction, segregation
+             and diffusion}},
+    journal =  {Modelling Simul. Mater. Sci. Eng.},
+    year =     2022,
+    volume =   30,
+    issue =    2,
+    pages =    025003,
+    annote =   {<jats:title>Abstract</jats:title>                <jats:p>Owing to the
+             excellent catalytic properties of Ag{\textendash}Au binary nanoalloys,
+             nanostructured Ag{\textendash}Au, such as Ag{\textendash}Au
+             nanoparticles and nanopillars, has been under intense investigation.
+             To achieve high accuracy in molecular simulations of Ag{\textendash}Au
+             nanoalloys, the surface properties must be modeled with first-
+             principles precision. In this work, we constructed a generalizable
+             machine learning interatomic potential for Ag{\textendash}Au
+             nanoalloys based on deep neural networks trained from a database
+             constructed with first-principles calculations. This potential is
+             highlighted by the accurate prediction of Au (111) surface
+             reconstruction and the segregation of Au toward the Ag{\textendash}Au
+             nanoalloy surface, where the empirical force field (EFF) failed in
+             both cases. Moreover, regarding the adsorption and diffusion of
+             adatoms on surfaces, the overall performance of our potential is
+             better than the EFFs. We stress that the reported surface properties
+             are blind to the potential modeling in the sense that none of the
+             surface configurations is explicitly included in the training
+             database; therefore, the reported potential is expected to have a
+             strong generalization ability to a wide range of properties and to
+             play a key role in investigating nanostructured Ag{\textendash}Au
+             evolution, where accurate descriptions of free surfaces are
+             necessary.</jats:p>},
+    doi =      {10.1088/1361-651X/ac4002},
+}
+
+@Article{Ryltsev_JournalofMolecularLiquids_2022_v349_p118181,
+    author =   {R.E. Ryltsev and N.M. Chtchelkatchev},
+    title =    {{Deep machine learning potentials for multicomponent metallic melts:
+             Development, predictability and compositional transferability}},
+    journal =  {Journal of Molecular Liquids},
+    year =     2022,
+    volume =   349,
+    pages =    118181,
+    doi =      {10.1016/j.molliq.2021.118181},
+}
+
+@Article{Xie_JPhysCondensMatter_2022_v34_p075402,
+    author =   {Kun Xie and Chong Qiao and Hong Shen and Riyi Yang and Ming Xu and
+             Chao Zhang and Yuxiang Zheng and Rongjun Zhang and Liangyao Chen and
+             Kai-Ming Ho and Cai-Zhuang Wang and Songyou Wang},
+    title =    {{Neural network potential for Zr{\textendash}Rh system by machine
+             learning}},
+    journal =  {J. Phys. Condens. Matter},
+    year =     2022,
+    volume =   34,
+    issue =    7,
+    pages =    075402,
+    annote =   {Zr-Rh metallic glass has enabled its many applications in vehicle
+             parts, sports equipment and so on due to its outstanding performance
+             in mechanical property, but the knowledge of the microstructure
+             determining the superb mechanical property remains yet insufficient.
+             Here, we develop a deep neural network potential of Zr-Rh system by
+             using machine learning, which breaks the dilemma between the accuracy
+             and efficiency in molecular dynamics simulations, and greatly improves
+             the simulation scale in both space and time. The results show that the
+             structural features obtained from the neural network method are in
+             good agreement with the cases inab initiomolecular dynamics
+             simulations. Furthermore, we build a large model of 5400 atoms to
+             explore the influences of simulated size and cooling rate on the melt-
+             quenching process of Zr77Rh23. Our study lays a foundation for
+             exploring the complex structures in amorphous Zr77Rh23, which is of
+             great significance for the design{~}and practical application.},
+    doi =      {10.1088/1361-648X/ac37dc},
+}
+
+@Article{Guo_JournalofMolecularLiquids_2022_v348_p118380,
+    author =   {Di Guo and Jia Zhao and Wenshuo Liang and Guimin Lu},
+    title =    {{Molecular dynamics simulation of molten strontium chloride based on
+             deep potential}},
+    journal =  {Journal of Molecular Liquids},
+    year =     2022,
+    volume =   348,
+    pages =    118380,
+    doi =      {10.1016/j.molliq.2021.118380},
+}
+
+
+@article{Urata_JPhysChemC_2022_v126_p4,
+    author = {Urata, Shingo and Nakamura, Nobuhiro and Tada, Tomofumi and Tan, Aik Rui and Gómez-Bombarelli, Rafael and Hosono, Hideo},
+    title = {Suppression of Rayleigh Scattering in Silica Glass by Codoping Boron and Fluorine: Molecular Dynamics Simulations with Force-Matching and Neural Network Potentials},
+    journal = {J. Phys. Chem. C},
+    volume = {126},
+    number = {4},
+    pages = {2264-2275},
+    year = {2022},
+    doi = {10.1021/acs.jpcc.1c10300},
+}
+@Article{Zhou_CementandConcreteResearch_2022_v152_p106685,
+    author =   {Yang Zhou and Haojie Zheng and Weihuan Li and Tao Ma and Changwen Miao},
+    title =    {{A deep learning potential applied in tobermorite phases and extended
+             to calcium silicate hydrates}},
+    journal =  {Cement and Concrete Research},
+    year =     2022,
+    volume =   152,
+    pages =    106685,
+    doi =      {10.1016/j.cemconres.2021.106685},
+}
+
+@Article{Balyakin_ComputationalMaterialsScience_2022_v202_p110963,
+    author =   {I.A. Balyakin and S.I. Sadovnikov},
+    title =    {{Deep learning potential for superionic phase of Ag2S}},
+    journal =  {Computational Materials Science},
+    year =     2022,
+    volume =   202,
+    pages =    110963,
+    doi =      {10.1016/j.commatsci.2021.110963},
+}
+
+@Article{Gu_ScienceBulletin_2022_v67_p29,
+    author =   {Qiangqiang Gu and Linfeng Zhang and Ji Feng},
+    title =    {{Neural network representation of electronic structure from ab initio
+             molecular dynamics}},
+    journal =  {Science Bulletin},
+    year =     2022,
+    volume =   67,
+    issue =    1,
+    pages =    {29--37},
+    doi =      {10.1016/j.scib.2021.09.010},
+}
+