Eqnorm MPtrj

Version: 0.1.0 Added: 2025-05-26 Published: 2025-05-26 1.31M parameters Missing preds: 0
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Predictions
κSRMEGeometry OptimizationDiscovery

Convex hull distance prediction errors projected onto elements

1 H 0.25
2 He 0.00
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 0.00
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 0.00
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 0.00
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 0.00
85 At 0.00
86 Rn 0.00
87 Fr 0.00
88 Ra 0.00
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 0.00
96 Cm 0.00
97 Bk 0.00
98 Cf 0.00
99 Es 0.00
100 Fm 0.00
101 Md 0.00
102 No 0.00
103 Lr 0.00
104 Rf 0.00
105 Db 0.00
106 Sg 0.00
107 Bh 0.00
108 Hs 0.00
109 Mt 0.00
110 Ds 0.00
111 Rg 0.00
112 Cn 0.00
113 Nh 0.00
114 Fl 0.00
115 Mc 0.00
116 Lv 0.00
117 Ts 0.00
118 Og 0.00
57-71 La-Lu Lanthanides
89-103 Ac-Lr Actinides

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

  • Model Version 0.1.0
  • Model Type UIP
  • Targets EFSG
  • Openness OSOD
  • Train Task S2EFS
  • Test Task IS2RE-SR
  • Trained for Benchmark Yes

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.

Hyperparameters

  • max_force: 0.02
  • max_steps: 500
  • ase_optimizer: "FIRE"
  • cell_filter: "FrechetCellFilter"
  • loss: "Huber"
  • loss_weights: {"energy":20,"force":20,"stress":320}
  • optimizer: "AdamW"
  • 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
  • epochs: 100
  • batch_train: 128
  • n_layers: 4
  • num_embedding_features: 128
  • num_bessel_basis: 8
  • invariant_layers: 2
  • invariant_neurons: 64
  • poly_p: 6
  • graph_construction_radius: 6
  • max_neighbors: null
  • 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