Update DeePMD_14_05_2025.md (#253)

Author: cloudcloud666

source/_posts/DeePMD_14_05_2025.md

@@ -1,30 +1,35 @@
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-title : "What Can DP Do too? | Unraveling the Mystery of Water's 'Second Critical Point' "
+title : "What Can DP Do too? | Unraveling the Mystery of Water's "Second Critical Point" "
 date : 2025-05-14
 categories :
 - DeePMD
 ---
 
 Water, one of the most common yet complex molecules, has long perplexed researchers with its thermodynamic properties. A controversial hypothesis suggests that under supercooled conditions, water may exhibit a "liquid-liquid transition (LLT)"—a transformation between low-density liquid (LDL) and high-density liquid (HDL)—governed by a "second critical point (LLCP)."
-However, direct observation of the LLCP has been extremely challenging. Within experimentally accessible temperature-pressure ranges, liquid water readily freezes into ice, while simulations struggle to reach the microsecond timescale. As a result, this hypothesis has long remained an unresolved "mystery."
+
+However, direct observation of the LLCP has been extremely challenging. Within experimentally accessible temperature-pressure ranges, liquid water readily freezes into ice, while simulations struggle to reach the microsecond timescale. As a result, this hypothesis has long remained an unresolved "mystery".
+
 Recently, F. Sciortino et al. leveraged the DeePMD framework with the DNN@MB-pol potential model to conduct microsecond-scale molecular dynamics simulations, achieving high precision approaching CCSD(T) calculations. For the first time, they provided strong constraints on the location of the liquid-liquid phase transition critical point in water. Published in Nature Physics under the title "Constraints on the location of the liquid-liquid critical point in water," this work opens a new chapter in understanding water's anomalous behavior.
 
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 ## From MB-pol to DNN@MB-pol: Balancing Precision and Efficiency
 
 The most reliable approach for high-precision simulations of water's microscopic behavior remains quantum chemistry-based potential energy surface modeling. MB-pol, a many-body interaction water potential model proposed in recent years, uses CCSD(T)-calculated two-body and three-body potential terms to achieve excellent fitting across all temperature-pressure conditions.
+
 However, precision comes at a cost. Simulating thousands of molecules over microsecond timescales is nearly unfeasible with MB-pol. To address this, the researchers introduced the DNN@MB-pol potential function within the DeePMD framework. Trained on MB-pol data, this deep neural network potential retains high precision while improving computational efficiency to acceptable levels.
 
 <center>
   <img src="https://dp-public.oss-cn-beijing.aliyuncs.com/community/Blog%20Files/DeePMD_14_05_2025/p1.png">
 </center>
 
-*Figure1a: Water density curves simulated by DNN@MB-pol under different temperatures and pressures, showing abnormal density increase at low temperatures; Figure1b: Time evolution of water density at 188 K under varying pressures, with density fluctuating between two values near 1000 atm, suggesting switching between two liquid structures at the phase transition boundary*
+*Figure1a: Water density curves simulated by DNN@MB-pol under different temperatures and pressures, showing abnormal density increase at low temperatures; 
+Figure1b: Time evolution of water density at 188 K under varying pressures, with density fluctuating between two values near 1000 atm, suggesting switching between two liquid structures at the phase transition boundary*
 
 ## Breakthrough in Long-Time-Scale Simulations
 
 Using DNN@MB-pol, the study performed 8-microsecond molecular dynamics simulations on 512 water molecules across temperature ranges from 180 K to 220 K, directly observing the dynamics of liquid-liquid phase transitions at the microscale for the first time. Simulations showed clear transitions from a single liquid phase to a two-phase coexistence state under specific temperature-pressure conditions, providing direct evidence for the LLCP and validating numerous indirect inferences.
+
 Furthermore, the authors constructed free energy landscapes of supercooled water at different state points using the weighted histogram analysis method (WHAM). This method estimates the density of states at different volumes and energies from limited simulation trajectories, deriving the trend of Gibbs free energy with density. 
 
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@@ -35,7 +40,7 @@ Furthermore, the authors constructed free energy landscapes of supercooled water
 
 ## Redrawing Water's Phase Diagram
 
-To comprehensively describe water's thermodynamic behavior in the supercooled region, the authors compiled and 绘制 (Note: "plotted" in English) a phase diagram based on DNN@MB-pol simulations (Figure3). It marks key boundaries including the liquid-liquid coexistence line, critical point, spinodal lines (indicating phase stability limits), Widom line (maximum thermodynamic fluctuations), and the temperature of maximum density (TMD) line.
+To comprehensively characterize the thermodynamic behavior of water in the supercooled region, the authors compiled and plotted a phase diagram based on DNN@MB-pol simulations (Figure 3). It marks critical boundaries such as the liquid-liquid coexistence line, critical point, spinodal lines (indicating phase stability limits), Widom line (denoting the locus of maximum thermodynamic fluctuations), and the temperature of maximum density (TMD) line.
 
 The coexistence line near the LLCP exhibits a negative slope, indicating that the LDL phase has lower entropy (more ordered), while the HDL phase, though denser, is more "disordered"—consistent with compressibility calculations. Comparing simulation results with experimental phase diagrams, DNN@MB-pol shows high overall consistency, despite a ~10 K temperature offset attributed to minor differences between the MB-pol training data and real systems, which does not affect its ability to reconstruct the physical landscape.