add papers using DP-GEN

Author: njzjz

source/_data/pub.bib

@@ -2424,3 +2424,89 @@ @Article{Zhang_PhysRevLett_2018_v120_p143001
     doi =      {10.1103/PhysRevLett.120.143001},
 }
 
+@article{He_PhysRevB_2022_v105_p064104,
+  title = {Structural phase transitions in $\mathrm{SrTi}{\mathrm{O}}_{3}$ from deep potential molecular dynamics},
+  author = {He, Ri and Wu, Hongyu and Zhang, Linfeng and Wang, Xiaoxu and Fu, Fangjia and Liu, Shi and Zhong, Zhicheng},
+  journal = {Phys. Rev. B},
+  volume = {105},
+  issue = {6},
+  pages = {064104},
+  numpages = {10},
+  year = {2022},
+  month = {Feb},
+  publisher = {American Physical Society},
+  doi = {10.1103/PhysRevB.105.064104},
+}
+
+@Article{Yang_Chemphyschem_2022_v23_pe202100841,
+    author =   {Weijie Yang and Jiajia Li and Xuelu Chen and Yajun Feng and Chongchong
+             Wu and Ian D Gates and Zhengyang Gao and Xunlei Ding and Jianxi Yao
+             and Hao Li},
+    title =    {{Exploring the Effects of Ionic Defects on the Stability of CsPbI
+             <sub>3</sub>             with a Deep Learning Potential}},
+    journal =  {Chemphyschem},
+    year =     2022,
+    volume =   23,
+    issue =    7,
+    pages =    {e202100841},
+    annote =   {Inorganic metal halide perovskites, such as CsPbI3 , have recently
+             drawn extensive attention due to their excellent optical properties
+             and high photoelectric efficiencies. However, the structural
+             instability originating from inherent ionic defects leads to a sharp
+             drop in the photoelectric efficiency, which significantly limits their
+             applications in solar cells. The instability induced by ionic defects
+             remains unresolved due to its complicated reaction process. Herein, to
+             explore the effects of ionic defects on stability, we develop a deep
+             learning potential for a CsPbI3 ternary system based upon density
+             functional theory (DFT) calculated data for large-scale molecular
+             dynamics (MD) simulations. By exploring 2.4 million configurations, of
+             which 7,730 structures are used for the training set, the deep
+             learning potential shows an accuracy approaching DFT-level.
+             Furthermore, MD simulations with a 5,000-atom system and a one
+             nanosecond timeframe are performed to explore the effects of bulk and
+             surface defects on the stability of CsPbI3 . This deep learning
+             potential based MD simulation provides solid evidence together with
+             the derived radial distribution functions, simulated diffraction of
+             X-rays, instability temperature, molecular trajectory, and
+             coordination number for revealing the instability mechanism of CsPbI3
+             . Among bulk defects, Cs defects have the most significant influence
+             on the stability of CsPbI3 with a defect tolerance concentration of
+             0.32{\,}%, followed by Pb and I defects. With regards to surface
+             defects, Cs defects have the largest impact on the stability of CsPbI3
+             when the defect concentration is less than 15{\,}%, whereas Pb defects
+             act play a dominant role for defect concentrations exceeding 20{\,}%.
+             Most importantly, this machine-learning-based MD simulation strategy
+             provides a new avenue to explore the ionic defect effects on the
+             stability of perovskite-like materials, laying a theoretical
+             foundation for the design of stable perovskite materials.},
+    doi =      {10.1002/cphc.202100841},
+}
+
+@Article{Zhang_NatCommun_2022_v13_p822,
+    author =   {Chunyi Zhang and Shuwen Yue and Athanassios Z Panagiotopoulos and
+             Michael L Klein and Xifan Wu},
+    title =    {{Dissolving salt is not equivalent to applying a pressure on water}},
+    journal =  {Nat. Commun.},
+    year =     2022,
+    volume =   13,
+    issue =    1,
+    pages =    822,
+    annote =   {Salt water is ubiquitous, playing crucial roles in geological and
+             physiological processes. Despite centuries of investigations, whether
+             or not water's structure is drastically changed by dissolved ions is
+             still debated. Based on density functional theory, we employ machine
+             learning based molecular dynamics to model sodium chloride, potassium
+             chloride, and sodium bromide solutions at different concentrations.
+             The resulting reciprocal-space structure factors agree quantitatively
+             with neutron diffraction data. Here we provide clear evidence that the
+             ions in salt water do not distort the structure of water in the same
+             way as neat water responds to elevated pressure. Rather, the computed
+             structural changes are restricted to the ionic first solvation shells
+             intruding into the hydrogen bond network, beyond which the oxygen
+             radial-distribution function does not undergo major change relative to
+             neat water. Our findings suggest that the widely cited pressure-like
+             effect on the solvent in Hofmeister series ionic solutions should be
+             carefully revisited.},
+    PMCID =    {PMC8831556},
+    doi =      {10.1038/s41467-022-28538-8},
+}

source/papers/deepmd-kit/index.md

@@ -9,6 +9,9 @@ The following publications have used the DeePMD-kit software. Publications that
 
 ## 2022
 {% publications %}
+Zhang_NatCommun_2022_v13_p822,
+Yang_Chemphyschem_2022_v23_pe202100841,
+He_PhysRevB_2022_v105_p064104,
 Ding_MaterialsScienceinSemiconductorProcessing_2022_v143_p106513,
 Jiao_AdvSci_2022_v9_pe2105574,
 Wang_Carbon_2022_v186_p1,

source/papers/dpgen/index.md

@@ -0,0 +1,56 @@
+---
+title: Publications driven by DP-GEN
+date: 2022-05-11
+update: 2022-05-11
+mathjax: true
+---
+
+The following publications have used the DP-GEN software. Publications that only mentioned the DP-GEN will not be included below.
+
+## 2022
+{% publications %}
+Zhang_NatCommun_2022_v13_p822,
+Pegolo_npjComputMater_2022_v8_p24,
+Fu_JournalofEnergyChemistry_2022_v70_p59,
+Ding_MaterialsScienceinSemiconductorProcessing_2022_v143_p106513,
+Yang_Chemphyschem_2022_v23_pe202100841,
+Jiao_AdvSci_2022_v9_pe2105574,
+Zhang_JChemPhys_2022_v156_p124107,
+Wang_ModellingSimulMaterSciEng_2022_v30_p025003,
+Yang_CatalysisToday_2022_v387_p143,
+Guo_JournalofMolecularLiquids_2022_v348_p118380,
+He_PhysRevB_2022_v105_p064104,
+Zhou_CementandConcreteResearch_2022_v152_p106685
+{% endpublications %}
+
+## 2021
+{% publications %}
+Garcia_JPhysChemC_2021_v125_p27130,
+Wen_npjComputMater_2021_v7_p206,
+Shi_MaterialsScienceinSemiconductorProcessing_2021_v136_p106146,
+Zeng_JChemTheoryComput_2021_v17_p6993,
+Wu_PhysRevB_2021_v104_p174107,
+Yang_PhysRevLett_2021_v127_p080603,
+Luo_GeophysResLett_2021_v48_p093573,
+Zhang_PhysRevLett_2021_v126_p236001,
+Guo_MaterialsTodayEnergy_2021_v20_p100665,
+Piaggi_JChemTheoryComput_2021_v17_p3065,
+Jiang_ChinesePhysB_2021_v30_p050706,
+Huang_JChemPhys_2021_v154_p094703,
+Shang_AIPAdvances_2021_v11_p035105,
+Wu_PhysRevB_2021_v103_p024108,
+Li_InorgChemFront_2021_v8_p425,
+Zeng_EnergyFuels_2021_v35_p762,
+Luo_GeochemPerspLet_2021_vNone_p19,
+Cheng_AIPAdvances_2021_v11_p015043
+{% endpublications %}
+
+## 2020
+{% publications %}
+Wang_FrontChem_2020_v8_p589795,
+Gartner_ProcNatlAcadSciUSA_2020_v117_p26040,
+Xu_JPhysChemC_2020_v124_p16278,
+Zhang_PhysRevB_2020_v102_p041121,
+Sommers_PhysChemChemPhys_2020_v22_p10592,
+Zhang_ComputerPhysicsCommunications_2020_v253_p107206
+{% endpublications %}

source/papers/index.md

@@ -8,7 +8,7 @@ title: Publications citing DeepModeling's work
 ## Classified by Project (in progress)
 
 - [DeePMD-kit](deepmd-kit/)
-- DP-GEN
+- [DP-GEN](dpgen/)
 - DeePKS-kit
 - RiD-kit
 - ABACUS