Update pub.bib (#298)

Author: cloudcloud666

source/_data/pub.bib

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+@Article{Zhu_npjDrugDiscov_2025_v2_p1,
+    author =   {Hui Zhu and Xuelian Li and Baoquan Chen and Niu Huang},
+    title =    {{Augmented BindingNet dataset for enhanced ligand binding pose
+             predictions using deep learning}},
+    journal =  {npj Drug Discov,},
+    year =     2025,
+    volume =   2,
+    number =   1,
+    pages =    1,
+    doi =      {10.1038/s44386-024-00003-0},
+}
+
+@Article{Dai_AdvPhysRes_2025_v4,
+    author =   {Yin Dai and Menghao Wu},
+    title =    {{Giant Inverse Barocaloric Effect of Ferroelectric Salts Driven by
+             Negative Thermal Expansion}},
+    journal =  {Adv. Phys. Res.},
+    year =     2025,
+    volume =   4,
+    number =   4,
+    doi =      {10.1002/apxr.202400125},
+    abstract = {AbstractRefrigeration technologies based on the barocaloric effect
+             have garnered significant attention, while their potential
+             applications are limited by the poor performance of current materials.
+             Here it is proposed that ferroelectric ionic salts with
+             covalent{-}like bondings like LiI may become ideal candidates. The
+             pressure{-}induced phase transition between two phases with distinct
+             densities and entropies leads to tremendous negative thermal expansion
+             (with volume reduced by 13{\%}) and inverse barocaloric effect, which
+             are facilitated by the low transition barriers due to the long{-}range
+             Coulomb interaction. Using ab initio{-}based training database, we
+             trained the machine learning potential of LiI based on a deep neutral
+             network{-}based mode, and this simulations of its barocaloric effect
+             reveal a high entropy change and thermal conductivity, and in
+             particular, the estimated adiabatic temperature change, pressure
+             sensitivity and relative cooling power are all unprecedented. This
+             prediction provides a high{-}performance barocaloric mechanism for
+             practical applications and also expands the scope of barocaloric
+             materials to simple and facile binary salts.},
+}
+
+@Article{Zeng_JChemInfModel_2025_v65_p3154,
+    author =   {Jinzhe Zeng and Timothy J. Giese and Duo Zhang and Han Wang and Darrin
+             M. York},
+    title =    {{DeePMD-GNN: A DeePMD-kit Plugin for External Graph Neural Network
+             Potentials}},
+    journal =  {J. Chem. Inf. Model.},
+    year =     2025,
+    volume =   65,
+    number =   7,
+    pages =    {3154--3160},
+    doi =      {10.1021/acs.jcim.4c02441},
+    abstract = {Machine learning potentials (MLPs) have revolutionized molecular
+             simulation by providing efficient and accurate models for predicting
+             atomic interactions. MLPs continue to advance and have had profound
+             impact in applications that include drug discovery, enzyme catalysis,
+             and materials design. The current landscape of MLP software presents
+             challenges due to the limited interoperability between packages, which
+             can lead to inconsistent benchmarking practices and necessitates
+             separate interfaces with molecular dynamics (MD) software. To address
+             these issues, we present DeePMD-GNN, a plugin for the DeePMD-kit
+             framework that extends its capabilities to support external graph
+             neural network (GNN) potentials.DeePMD-GNN enables the seamless
+             integration of popular GNN-based models, such as NequIP and MACE,
+             within the DeePMD-kit ecosystem. Furthermore, the new software
+             infrastructure allows GNN models to be used within combined quantum
+             mechanical/molecular mechanical (QM/MM) applications using the range
+             corrected {\ensuremath{\Delta}}MLP formalism.We demonstrate the
+             application of DeePMD-GNN by performing benchmark calculations of
+             NequIP, MACE, and DPA-2 models developed under consistent training
+             conditions to ensure fair comparison.},
+}
+
 @Article{Shi_arXiv_2025_p2503.06039,
     author =   {Guoyong Shi and Fenglin Deng and Ri He and Dachuan Chen and Xuejiao
              Chen and Peiheng Jiang and Zhicheng Zhong},