Eqnorm MPtrj

Version: 0.1.0 Added: 2025-05-26 Published: 2025-05-26 1.31M parameters
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Predictions
DiscoveryGeometry OptimizationDiatomicsMolecular DynamicsκSRME
Leaderboard ranks CPS #26 /40Discovery F1 #30 /50Geo Opt RMSD #30 /41Phonons κSRME #21 /40MD CMDS #15 /34Diatomics CDS #9 /33

Discovery: energy and convex hull diagnostics

Missing preds: 0

Per-element convex hull distance errors

1 H 0.25
2 He Helium
3 Li 0.04
4 Be 0.08
5 B 0.11
6 C 0.10
7 N 0.13
8 O 0.15
9 F 0.15
10 Ne Neon
11 Na 0.05
12 Mg 0.06
13 Al 0.10
14 Si 0.11
15 P 0.11
16 S 0.13
17 Cl 0.14
18 Ar Argon
19 K 0.06
20 Ca 0.06
21 Sc 0.07
22 Ti 0.08
23 V 0.11
24 Cr 0.13
25 Mn 0.15
26 Fe 0.14
27 Co 0.08
28 Ni 0.08
29 Cu 0.06
30 Zn 0.07
31 Ga 0.09
32 Ge 0.10
33 As 0.09
34 Se 0.14
35 Br 0.13
36 Kr Krypton
37 Rb 0.06
38 Sr 0.06
39 Y 0.07
40 Zr 0.08
41 Nb 0.10
42 Mo 0.08
43 Tc 0.08
44 Ru 0.11
45 Rh 0.09
46 Pd 0.09
47 Ag 0.06
48 Cd 0.06
49 In 0.09
50 Sn 0.09
51 Sb 0.09
52 Te 0.16
53 I 0.12
54 Xe 0.00
55 Cs 0.06
56 Ba 0.06
57 La 0.06
58 Ce 0.07
59 Pr 0.06
60 Nd 0.06
61 Pm 0.05
62 Sm 0.06
63 Eu 0.08
64 Gd 0.06
65 Tb 0.06
66 Dy 0.06
67 Ho 0.06
68 Er 0.06
69 Tm 0.07
70 Yb 0.07
71 Lu 0.06
72 Hf 0.09
73 Ta 0.13
74 W 0.09
75 Re 0.09
76 Os 0.11
77 Ir 0.12
78 Pt 0.10
79 Au 0.10
80 Hg 0.06
81 Tl 0.06
82 Pb 0.09
83 Bi 0.08
84 Po Polonium
85 At Astatine
86 Rn Radon
87 Fr Francium
88 Ra Radium
89 Ac 0.06
90 Th 0.09
91 Pa 0.09
92 U 0.10
93 Np 0.16
94 Pu 0.36
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. Yuzhuo Chen  Zhejiang Lab  
  2. Lyuwen Fu  Zhejiang Lab  
  3. Shuxiang Yang  Zhejiang Lab  
  4. Lipeng Chen  Zhejiang Lab  

Trained By

  1. Yuzhuo Chen  Zhejiang Lab  

Model Info

  • Version 0.1.0
  • Role Interatomic potential
  • Architecture gnn
  • Targets EFSG
  • Openness OSOD
  • Discovery Train Task S2EFS
  • Discovery Test Task IS2RE-SR

Training Set

MPtrj: 1.58M structures from 146k materials

description

eqnorm is a graph neural network model designed for predicting the energy, forces, and stresses of materials. The model utilizes a combination of invariant and equivariant layers to effectively capture the symmetries present in material structures.

Hyperparams

  • evaluation: {"max_force":0.02,"max_steps":500,"ase_optimizer":"FIRE","cell_filter":"FrechetCellFilter","kappa":{"protocol":"phonondb-v1","displacement_distance":0.03,"max_steps":500,"save_forces":true}}
  • architecture: {"n_layers":4,"graph_construction_radius":6}
  • training: {"epochs":100,"optimizer":"AdamW"}
  • upstream_config: {"loss":"Huber","loss_weights":{"energy":20,"force":20,"stress":320},"weight_decay":0.001,"clip_grad_norm":100,"ema_decay":0.999,"max_learning_rate":0.01,"min_learning_rate":0.000001,"learning_rate_schedule":"warmcosine","warmup_factor":0.2,"batch_train":128,"num_embedding_features":128,"num_bessel_basis":8,"invariant_layers":2,"invariant_neurons":64,"poly_p":6,"irreps_hidden":"128x0e+64x1o+32x2e+32x3o","irreps_sh":"1x0e+1x1o+1x2e+1x3o","energy_shift":"per_species","energy_scale":"force_rms","shift_trainable":false,"scale_trainable":false}

Dependencies