DPA-3.1-3M-FT

Version: v0.3 Added: 2025-06-05 Published: 2025-06-05 3.27M parameters
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Predictions
DiscoveryGeometry OptimizationDiatomicsMolecular DynamicsκSRME
Leaderboard ranks CPS #19 /41Discovery F1 #17 /52Geo Opt RMSD #18 /42Phonons κSRME #25 /41MD CMDS #15 /34Diatomics CDS #23 /33

Discovery: energy and convex hull diagnostics

Missing preds: 0

Per-element convex hull distance errors

1 H 0.13
2 He Helium
3 Li 0.03
4 Be 0.03
5 B 0.06
6 C 0.06
7 N 0.08
8 O 0.11
9 F 0.11
10 Ne Neon
11 Na 0.03
12 Mg 0.04
13 Al 0.05
14 Si 0.06
15 P 0.05
16 S 0.07
17 Cl 0.09
18 Ar Argon
19 K 0.04
20 Ca 0.04
21 Sc 0.03
22 Ti 0.04
23 V 0.06
24 Cr 0.08
25 Mn 0.11
26 Fe 0.09
27 Co 0.05
28 Ni 0.04
29 Cu 0.04
30 Zn 0.04
31 Ga 0.05
32 Ge 0.05
33 As 0.05
34 Se 0.09
35 Br 0.08
36 Kr Krypton
37 Rb 0.04
38 Sr 0.04
39 Y 0.04
40 Zr 0.04
41 Nb 0.05
42 Mo 0.05
43 Tc 0.04
44 Ru 0.05
45 Rh 0.05
46 Pd 0.05
47 Ag 0.04
48 Cd 0.03
49 In 0.06
50 Sn 0.05
51 Sb 0.05
52 Te 0.11
53 I 0.07
54 Xe 0.03
55 Cs 0.04
56 Ba 0.04
57 La 0.03
58 Ce 0.04
59 Pr 0.04
60 Nd 0.03
61 Pm 0.03
62 Sm 0.03
63 Eu 0.06
64 Gd 0.04
65 Tb 0.03
66 Dy 0.04
67 Ho 0.03
68 Er 0.03
69 Tm 0.03
70 Yb 0.05
71 Lu 0.04
72 Hf 0.04
73 Ta 0.07
74 W 0.05
75 Re 0.04
76 Os 0.05
77 Ir 0.06
78 Pt 0.06
79 Au 0.06
80 Hg 0.03
81 Tl 0.03
82 Pb 0.06
83 Bi 0.04
84 Po Polonium
85 At Astatine
86 Rn Radon
87 Fr Francium
88 Ra Radium
89 Ac 0.03
90 Th 0.05
91 Pa 0.05
92 U 0.06
93 Np 0.09
94 Pu 0.20
95 Am Americium
96 Cm Curium
97 Bk Berkelium
98 Cf Californium
99 Es Einsteinium
100 Fm Fermium
101 Md Mendelevium
102 No Nobelium
103 Lr Lawrencium
104 Rf Rutherfordium
105 Db Dubnium
106 Sg Seaborgium
107 Bh Bohrium
108 Hs Hassium
109 Mt Meitnerium
110 Ds Darmstadtium
111 Rg Roentgenium
112 Cn Copernicum
113 Nh Nihonium
114 Fl Flerovium
115 Mc Moscovium
116 Lv Livermorium
117 Ts Tennessine
118 Og Oganesson
57-71 La-Lu Lanthanides
89-103 Ac-Lr Actinides

ML vs DFT Lattice Thermal Conductivity

Model Authors

  1. Duo Zhang  AI for Science Institute, Beijing  
  2. Anyang Peng  AI for Science Institute, Beijing  
  3. Chun Cai  AI for Science Institute, Beijing  
  4. Linfeng Zhang  AI for Science Institute, Beijing; DP Technology  
  5. Han Wang  Beijing Institute of Applied Physics and Computational Mathematics (IAPCM)  

Trained By

  1. Anyang Peng  AI for Science Institute, Beijing  

Model Info

  • Version v0.3
  • Role Interatomic potential
  • Architecture gnn
  • Targets EFSG
  • Openness OSCD
  • Discovery Train Task S2EFS
  • Discovery Test Task IS2RE-SR

Training Set

OpenLAM dataset v1: 163M structures

description

DPA3 is an advanced interatomic potential leveraging the message passing architecture, implemented within the DeePMD-kit framework, available on GitHub. Designed as a large atomic model (LAM), DPA3 is tailored to integrate and simultaneously train on datasets from various disciplines, encompassing diverse chemical and materials systems across different research domains. Its model design ensures exceptional fitting accuracy and robust generalization both within and beyond the training domain. Furthermore, DPA3 maintains energy conservation and respects the physical symmetries of the potential energy surface, making it a dependable tool for a wide range of scientific applications.

Hyperparams

Dependencies