add the PRL paper

Author: njzjz

source/_data/pub.bib

@@ -2408,3 +2408,27 @@ @Article{Gu_ScienceBulletin_2022_v67_p29
     doi =      {10.1016/j.scib.2021.09.010},
 }
 
+@Article{Zhang_PhysRevLett_2018_v120_p143001,
+    author =   {Linfeng Zhang and Jiequn Han and Han Wang and Roberto Car and Weinan E},
+    title =    {{Deep Potential Molecular Dynamics: A Scalable Model with the Accuracy
+             of Quantum Mechanics}},
+    journal =  {Phys. Rev. Lett.},
+    year =     2018,
+    volume =   120,
+    issue =    14,
+    pages =    143001,
+    annote =   {We introduce a scheme for molecular simulations, the deep potential
+             molecular dynamics (DPMD) method, based on a many-body potential and
+             interatomic forces generated by a carefully crafted deep neural
+             network trained with ab{~}initio data. The neural network model
+             preserves all the natural symmetries in the problem. It is first-
+             principles based in the sense that there are no ad{~}hoc components
+             aside from the network model. We show that the proposed scheme
+             provides an efficient and accurate protocol in a variety of systems,
+             including bulk materials and molecules. In all these cases, DPMD gives
+             results that are essentially indistinguishable from the original data,
+             at a cost that scales linearly with system size.},
+    doi =      {10.1103/PhysRevLett.120.143001},
+    image = {https://journals.aps.org/prl/article/10.1103/PhysRevLett.120.143001/figures/1/thumbnail},
+}
+

source/papers/deepmd-kit/index.md

@@ -140,5 +140,6 @@ Chen_JPhysChemLett_2018_v9_p6702,
 Zhang_JChemPhys_2018_v149_p154107,
 Zhang_JChemPhys_2018_v149_p034101,
 Zhang_PhysRevLett_2018_v120_p143001,
-Wang_ComputerPhysicsCommunications_2018_v228_p178
+Wang_ComputerPhysicsCommunications_2018_v228_p178,
+Zhang_PhysRevLett_2018_v120_p143001
 {% endpublications %}