add missing paper

Author: njzjz

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@@ -1011,3 +1011,67 @@ @Article{Balyakin_PhysRevE_2020_v102_p052125
     doi =      {10.1103/PhysRevE.102.052125},
     image = {https://journals.aps.org/pre/article/10.1103/PhysRevE.102.052125/figures/1/thumbnail},
 }
+@Article{CalegariAndrade_PhysRevMaterials_2020_v4_p113803,
+    author =   {Marcos F. {Calegari Andrade} and Annabella Selloni},
+    title =    {{Structure of disordered  <mml:math xmlns:mml="http://www.w3.org/1998/M
+             ath/MathML"><mml:msub><mml:mi>TiO</mml:mi><mml:mn>2</mml:mn></mml:msub
+             ></mml:math>  phases from  <i>ab initio</i>  based deep neural network
+             simulations}},
+    journal =  {Phys. Rev. Materials},
+    year =     2020,
+    volume =   4,
+    issue =    11,
+    pages =    113803,
+    doi =      {10.1103/PhysRevMaterials.4.113803},
+    image = {https://journals.aps.org/prmaterials/article/10.1103/PhysRevMaterials.4.113803/figures/1/thumbnail},
+}
+@Article{Gartner_ProcNatlAcadSciUSA_2020_v117_p26040,
+    author =   {Thomas E {Gartner 3rd} and Linfeng Zhang and Pablo M Piaggi and
+             Roberto Car and Athanassios Z Panagiotopoulos and Pablo G Debenedetti},
+    title =    {{Signatures of a liquid{\textendash}liquid transition in an ab initio
+             deep neural network model for water}},
+    journal =  {Proc. Natl. Acad. Sci. U. S. A.},
+    year =     2020,
+    volume =   117,
+    issue =    42,
+    pages =    {26040--26046},
+    annote =   {The possible existence of a metastable liquid-liquid transition (LLT)
+             and a corresponding liquid-liquid critical point (LLCP) in supercooled
+             liquid water remains a topic of much debate. An LLT has been
+             rigorously proved in three empirically parametrized molecular models
+             of water, and evidence consistent with an LLT has been reported for
+             several other such models. In contrast, experimental proof of this
+             phenomenon has been elusive due to rapid ice nucleation under deeply
+             supercooled conditions. In this work, we combined density functional
+             theory (DFT), machine learning, and molecular simulations to shed
+             additional light on the possible existence of an LLT in water. We
+             trained a deep neural network (DNN) model to represent the ab initio
+             potential energy surface of water from DFT calculations using the
+             Strongly Constrained and Appropriately Normed (SCAN) functional. We
+             then used advanced sampling simulations in the multithermal-multibaric
+             ensemble to efficiently explore the thermophysical properties of the
+             DNN model. The simulation results are consistent with the existence of
+             an LLCP, although they do not constitute a rigorous proof thereof. We
+             fit the simulation data to a two-state equation of state to provide an
+             estimate of the LLCP's location. These combined results-obtained from
+             a purely first-principles approach with no empirical parameters-are
+             strongly suggestive of the existence of an LLT, bolstering the
+             hypothesis that water can separate into two distinct liquid forms.},
+    PMCID =    {PMC7584908},
+    doi =      {10.1073/pnas.2015440117},
+    image = {https://www.pnas.org/cms/10.1073/pnas.2015440117/asset/a4e91def-afd1-4efd-afbf-6ef13c2e014c/assets/images/large/pnas.2015440117fig01.jpg},
+}
+@Article{Wen_PhysRevB_2019_v100_p174101,
+    author =   {Tongqi Wen and Cai-Zhuang Wang and M. J. Kramer and Yang Sun and
+             Beilin Ye and Haidi Wang and Xueyuan Liu and Chao Zhang and Feng Zhang
+             and Kai-Ming Ho and Nan Wang},
+    title =    {{Development of a deep machine learning interatomic potential for
+             metalloid-containing Pd-Si compounds}},
+    journal =  {Phys. Rev. B},
+    year =     2019,
+    volume =   100,
+    issue =    17,
+    pages =    174101,
+    doi =      {10.1103/PhysRevB.100.174101},
+    image = {https://journals.aps.org/prb/article/10.1103/PhysRevB.100.174101/figures/1/thumbnail},
+}