replace \textendash

Author: njzjz

source/_data/pub.bib

@@ -1528,7 +1528,7 @@ @Article{Jin_PhysChemChemPhys_2021_v23_p21470
 @Article{Bu_SolarEnergyMaterialsandSolarCells_2021_v232_p111346,
     author =   {Min Bu and Wenshuo Liang and Guimin Lu and Jianguo Yu},
     title =    {{Local structure elucidation and properties prediction on
-             KCl{\textendash}CaCl2 molten salt: A deep potential molecular dynamics
+             KCl{-}CaCl2 molten salt: A deep potential molecular dynamics
              study}},
     journal =  {Solar Energy Materials and Solar Cells},
     year =     2021,
@@ -1828,7 +1828,7 @@ @Article{Zhao_Ionics_2021_v27_p2079
 
 @Article{Jiang_ChinesePhysB_2021_v30_p050706,
     author =   {Wanrun Jiang and Yuzhi Zhang and Linfeng Zhang and Han Wang},
-    title =    {{Accurate Deep Potential model for the Al{\textendash}Cu{\textendash}Mg
+    title =    {{Accurate Deep Potential model for the Al{-}Cu{-}Mg
              alloy in the full concentration space*}},
     journal =  {Chinese Phys. B},
     year =     2021,
@@ -1841,7 +1841,7 @@ @Article{Jiang_ChinesePhysB_2021_v30_p050706
              of matter via atomistic simulation. This has been particularly
              challenging for multi-component alloy systems due to the complex and
              non-linear nature of the associated PES. In this work, we develop an
-             accurate PES model for the Al{\textendash}Cu{\textendash}Mg system by
+             accurate PES model for the Al{-}Cu{-}Mg system by
              employing deep potential (DP), a neural network based representation
              of the PES, and DP generator (DP-GEN), a concurrent-learning scheme
              that generates a compact set of <jats:italic>ab initio</jats:italic>
@@ -1852,7 +1852,7 @@ @Article{Jiang_ChinesePhysB_2021_v30_p050706
              interstitial energy, vacancy and surface formation energy, as well as
              elastic moduli. Extensive benchmark shows that the DP model is ready
              and will be useful for atomistic modeling of the
-             Al{\textendash}Cu{\textendash}Mg system within the full range of
+             Al{-}Cu{-}Mg system within the full range of
              concentration.</jats:p>},
     doi =      {10.1088/1674-1056/abf134},
 }
@@ -2272,7 +2272,7 @@ @article{Liu_JVacuumSciTech_2022_v40_p023205
 
 @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
+    title =    {{A generalizable machine learning potential of Ag{-}Au
              nanoalloys and its application to surface reconstruction, segregation
              and diffusion}},
     journal =  {Modelling Simul. Mater. Sci. Eng.},
@@ -2281,17 +2281,17 @@ @Article{Wang_ModellingSimulMaterSciEng_2022_v30_p025003
     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
+             excellent catalytic properties of Ag{-}Au binary nanoalloys,
+             nanostructured Ag{-}Au, such as Ag{-}Au
              nanoparticles and nanopillars, has been under intense investigation.
-             To achieve high accuracy in molecular simulations of Ag{\textendash}Au
+             To achieve high accuracy in molecular simulations of Ag{-}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
+             machine learning interatomic potential for Ag{-}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
+             reconstruction and the segregation of Au toward the Ag{-}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
@@ -2300,7 +2300,7 @@ @Article{Wang_ModellingSimulMaterSciEng_2022_v30_p025003
              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
+             play a key role in investigating nanostructured Ag{-}Au
              evolution, where accurate descriptions of free surfaces are
              necessary.</jats:p>},
     doi =      {10.1088/1361-651X/ac4002},
@@ -2321,7 +2321,7 @@ @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
+    title =    {{Neural network potential for Zr{-}Rh system by machine
              learning}},
     journal =  {J. Phys. Condens. Matter},
     year =     2022,