Only isolated atom calculation is difficult for m3gnet?

[jinkera]

Thank you for the great repositry.
Like #37 I get the error in calculating H atom energy. However, after calculating H2 molecule energy, I don't get the error.
If possible, could you tell me the reason why?

from m3gnet.models import M3GNetCalculator, Potential, M3GNet
from ase.io import read
from ase import Atoms

def main():
    potential = Potential(M3GNet.load())
    calc = M3GNetCalculator(potential=potential)

    print("H")
    atoms = Atoms('H', positions=[(0, 0, -0.35)])
    atoms.set_calculator(calc)
    try:
        print(atoms.get_potential_energy())
    except Exception as e:
        print(e)

if __name__ == '__main__':
    main()

H
in user code:

    File "/usr/local/lib/python3.9/site-packages/m3gnet/models/_base.py", line 186, in get_efs_tensor  *
        energies = self.get_energies(graph)
    File "/usr/local/lib/python3.9/site-packages/m3gnet/models/_base.py", line 261, in get_energies  *
        return self.model(graph)
    File "/usr/local/lib/python3.9/site-packages/keras/utils/traceback_utils.py", line 67, in error_handler  **
        raise e.with_traceback(filtered_tb) from None
    File "/tmp/__autograph_generated_fileot8cd98z.py", line 37, in tf__call
        ag__.for_stmt(ag__.converted_call(ag__.ld(range), (ag__.ld(self).n_blocks,), None, fscope), None, loop_body, get_state, set_state, ('g',), {'iterate_names': 'i'})
    File "/tmp/__autograph_generated_fileot8cd98z.py", line 35, in loop_body
        g = ag__.converted_call(ag__.ld(self).graph_layers[ag__.ld(i)], (ag__.ld(g),), None, fscope)
    File "/tmp/__autograph_generated_filetgzexm7i.py", line 16, in tf__call
        out = ag__.converted_call(ag__.ld(self).state_network, (ag__.converted_call(ag__.ld(self).atom_network, (ag__.converted_call(ag__.ld(self).bond_network, (ag__.ld(graph),), None, fscope),), None, fscope),), None, fscope)
    File "/tmp/__autograph_generated_file786n0fz6.py", line 18, in tf__call
        bonds = ag__.converted_call(ag__.ld(self).update_bonds, (ag__.ld(graph),), None, fscope)
    File "/tmp/__autograph_generated_filez4rbuqc7.py", line 40, in tf__update_bonds
        retval_ = ag__.converted_call(ag__.ld(self).update_func, (ag__.ld(concat),), None, fscope) * ag__.converted_call(ag__.ld(self).weight_func, (ag__.ld(graph)[ag__.ld(Index).BOND_WEIGHTS],), None, fscope) + ag__.ld(graph)[ag__.ld(Index).BONDS]
    File "/tmp/__autograph_generated_file4fv31nqr.py", line 20, in tf__call
        retval_ = ag__.converted_call(ag__.ld(self).pipe.call, (ag__.ld(inputs),), dict(**ag__.ld(kwargs)), fscope) * ag__.converted_call(ag__.ld(self).gate.call, (ag__.ld(inputs),), dict(**ag__.ld(kwargs)), fscope)
    File "/tmp/__autograph_generated_filehl5vv6vt.py", line 31, in tf__call
        ag__.for_stmt(ag__.ld(self).layers, None, loop_body, get_state, set_state, ('out',), {'iterate_names': 'layer'})
    File "/tmp/__autograph_generated_filehl5vv6vt.py", line 29, in loop_body
        out = ag__.converted_call(ag__.ld(layer), (ag__.ld(out),), None, fscope)

    ValueError: Exception encountered when calling layer "m3g_net" (type M3GNet).
    
    in user code:
    
        File "/usr/local/lib/python3.9/site-packages/m3gnet/models/_m3gnet.py", line 259, in call  *
            g = self.graph_layers[i](g)
        File "/usr/local/lib/python3.9/site-packages/keras/utils/traceback_utils.py", line 67, in error_handler  **
            raise e.with_traceback(filtered_tb) from None
        File "/tmp/__autograph_generated_filetgzexm7i.py", line 16, in tf__call
            out = ag__.converted_call(ag__.ld(self).state_network, (ag__.converted_call(ag__.ld(self).atom_network, (ag__.converted_call(ag__.ld(self).bond_network, (ag__.ld(graph),), None, fscope),), None, fscope),), None, fscope)
        File "/tmp/__autograph_generated_file786n0fz6.py", line 18, in tf__call
            bonds = ag__.converted_call(ag__.ld(self).update_bonds, (ag__.ld(graph),), None, fscope)
        File "/tmp/__autograph_generated_filez4rbuqc7.py", line 40, in tf__update_bonds
            retval_ = ag__.converted_call(ag__.ld(self).update_func, (ag__.ld(concat),), None, fscope) * ag__.converted_call(ag__.ld(self).weight_func, (ag__.ld(graph)[ag__.ld(Index).BOND_WEIGHTS],), None, fscope) + ag__.ld(graph)[ag__.ld(Index).BONDS]
        File "/tmp/__autograph_generated_file4fv31nqr.py", line 20, in tf__call
            retval_ = ag__.converted_call(ag__.ld(self).pipe.call, (ag__.ld(inputs),), dict(**ag__.ld(kwargs)), fscope) * ag__.converted_call(ag__.ld(self).gate.call, (ag__.ld(inputs),), dict(**ag__.ld(kwargs)), fscope)
        File "/tmp/__autograph_generated_filehl5vv6vt.py", line 31, in tf__call
            ag__.for_stmt(ag__.ld(self).layers, None, loop_body, get_state, set_state, ('out',), {'iterate_names': 'layer'})
        File "/tmp/__autograph_generated_filehl5vv6vt.py", line 29, in loop_body
            out = ag__.converted_call(ag__.ld(layer), (ag__.ld(out),), None, fscope)
    
        ValueError: Exception encountered when calling layer "graph_network_layer" (type GraphNetworkLayer).
        
        in user code:
        
            File "/usr/local/lib/python3.9/site-packages/m3gnet/layers/_gn.py", line 52, in call  *
                out = self.state_network(self.atom_network(self.bond_network(graph)))
            File "/usr/local/lib/python3.9/site-packages/keras/utils/traceback_utils.py", line 67, in error_handler  **
                raise e.with_traceback(filtered_tb) from None
            File "/tmp/__autograph_generated_file786n0fz6.py", line 18, in tf__call
                bonds = ag__.converted_call(ag__.ld(self).update_bonds, (ag__.ld(graph),), None, fscope)
            File "/tmp/__autograph_generated_filez4rbuqc7.py", line 40, in tf__update_bonds
                retval_ = ag__.converted_call(ag__.ld(self).update_func, (ag__.ld(concat),), None, fscope) * ag__.converted_call(ag__.ld(self).weight_func, (ag__.ld(graph)[ag__.ld(Index).BOND_WEIGHTS],), None, fscope) + ag__.ld(graph)[ag__.ld(Index).BONDS]
            File "/tmp/__autograph_generated_file4fv31nqr.py", line 20, in tf__call
                retval_ = ag__.converted_call(ag__.ld(self).pipe.call, (ag__.ld(inputs),), dict(**ag__.ld(kwargs)), fscope) * ag__.converted_call(ag__.ld(self).gate.call, (ag__.ld(inputs),), dict(**ag__.ld(kwargs)), fscope)
            File "/tmp/__autograph_generated_filehl5vv6vt.py", line 31, in tf__call
                ag__.for_stmt(ag__.ld(self).layers, None, loop_body, get_state, set_state, ('out',), {'iterate_names': 'layer'})
            File "/tmp/__autograph_generated_filehl5vv6vt.py", line 29, in loop_body
                out = ag__.converted_call(ag__.ld(layer), (ag__.ld(out),), None, fscope)
        
            ValueError: Exception encountered when calling layer "concat_atoms" (type ConcatAtoms).
            
            in user code:
            
                File "/usr/local/lib/python3.9/site-packages/m3gnet/layers/_bond.py", line 45, in call  *
                    bonds = self.update_bonds(graph)
                File "/usr/local/lib/python3.9/site-packages/m3gnet/layers/_bond.py", line 161, in update_bonds  *
                    return self.update_func(concat) * self.weight_func(graph[Index.BOND_WEIGHTS]) + graph[Index.BONDS]
                File "/usr/local/lib/python3.9/site-packages/keras/utils/traceback_utils.py", line 67, in error_handler  **
                    raise e.with_traceback(filtered_tb) from None
                File "/tmp/__autograph_generated_file4fv31nqr.py", line 20, in tf__call
                    retval_ = ag__.converted_call(ag__.ld(self).pipe.call, (ag__.ld(inputs),), dict(**ag__.ld(kwargs)), fscope) * ag__.converted_call(ag__.ld(self).gate.call, (ag__.ld(inputs),), dict(**ag__.ld(kwargs)), fscope)
                File "/tmp/__autograph_generated_filehl5vv6vt.py", line 31, in tf__call
                    ag__.for_stmt(ag__.ld(self).layers, None, loop_body, get_state, set_state, ('out',), {'iterate_names': 'layer'})
                File "/tmp/__autograph_generated_filehl5vv6vt.py", line 29, in loop_body
                    out = ag__.converted_call(ag__.ld(layer), (ag__.ld(out),), None, fscope)
            
                ValueError: Exception encountered when calling layer "gated_mlp_4" (type GatedMLP).
                
                in user code:
                
                    File "/usr/local/lib/python3.9/site-packages/m3gnet/layers/_core.py", line 229, in call  *
                        return self.pipe.call(inputs, **kwargs) * self.gate.call(inputs, **kwargs)
                    File "/usr/local/lib/python3.9/site-packages/m3gnet/layers/_core.py", line 38, in call  *
                        out = layer(out)
                    File "/usr/local/lib/python3.9/site-packages/keras/utils/traceback_utils.py", line 67, in error_handler  **
                        raise e.with_traceback(filtered_tb) from None
                    File "/usr/local/lib/python3.9/site-packages/keras/layers/core/dense.py", line 141, in build
                        raise ValueError('The last dimension of the inputs to a Dense layer '
                
                    ValueError: The last dimension of the inputs to a Dense layer should be defined. Found None. Full input shape received: (0, None)
                
                
                Call arguments received by layer "gated_mlp_4" (type GatedMLP):
                  • inputs=tf.Tensor(shape=(0, None), dtype=float32)
                  • kwargs={'training': 'None'}
            
            
            Call arguments received by layer "concat_atoms" (type ConcatAtoms):
              • graph=['tf.Tensor(shape=(1, 64), dtype=float32)', 'tf.Tensor(shape=(None, 64), dtype=float32)', 'None', 'tf.Tensor(shape=(None, 3), dtype=float32)', 'tf.Tensor(shape=(0, 2), dtype=int32)', 'tf.Tensor(shape=(0, 3), dtype=int32)', 'tf.Tensor(shape=(1,), dtype=int32)', 'tf.Tensor(shape=(1,), dtype=int32)', 'tf.Tensor(shape=(None, 3), dtype=float32)', 'tf.Tensor(shape=(1, 3, 3), dtype=float32)', 'tf.Tensor(shape=(0, 2), dtype=int32)', 'tf.Tensor(shape=(0,), dtype=float32)', 'tf.Tensor(shape=(0,), dtype=float32)', 'tf.Tensor(shape=(0,), dtype=float32)', 'tf.Tensor(shape=(0,), dtype=int32)', 'tf.Tensor(shape=(1,), dtype=int32)', 'tf.Tensor(shape=(1,), dtype=int32)']
              • kwargs={'training': 'None'}
        
        
        Call arguments received by layer "graph_network_layer" (type GraphNetworkLayer):
          • graph=['tf.Tensor(shape=(1, 64), dtype=float32)', 'tf.Tensor(shape=(None, 64), dtype=float32)', 'None', 'tf.Tensor(shape=(None, 3), dtype=float32)', 'tf.Tensor(shape=(0, 2), dtype=int32)', 'tf.Tensor(shape=(0, 3), dtype=int32)', 'tf.Tensor(shape=(1,), dtype=int32)', 'tf.Tensor(shape=(1,), dtype=int32)', 'tf.Tensor(shape=(None, 3), dtype=float32)', 'tf.Tensor(shape=(1, 3, 3), dtype=float32)', 'tf.Tensor(shape=(0, 2), dtype=int32)', 'tf.Tensor(shape=(0,), dtype=float32)', 'tf.Tensor(shape=(0,), dtype=float32)', 'tf.Tensor(shape=(0,), dtype=float32)', 'tf.Tensor(shape=(0,), dtype=int32)', 'tf.Tensor(shape=(1,), dtype=int32)', 'tf.Tensor(shape=(1,), dtype=int32)']
          • kwargs={'training': 'None'}
    
    
    Call arguments received by layer "m3g_net" (type M3GNet):
      • graph=['tf.Tensor(shape=(1, 1), dtype=int32)', 'tf.Tensor(shape=(0, 1), dtype=float32)', 'None', 'tf.Tensor(shape=(None, 3), dtype=float32)', 'tf.Tensor(shape=(0, 2), dtype=int32)', 'tf.Tensor(shape=(0, 3), dtype=int32)', 'tf.Tensor(shape=(1,), dtype=int32)', 'tf.Tensor(shape=(1,), dtype=int32)', 'tf.Tensor(shape=(0,), dtype=float32)', 'tf.Tensor(shape=(1, 3, 3), dtype=float32)', 'tf.Tensor(shape=(0, 2), dtype=int32)', 'None', 'None', 'None', 'tf.Tensor(shape=(0,), dtype=int32)', 'tf.Tensor(shape=(1,), dtype=int32)', 'tf.Tensor(shape=(1,), dtype=int32)']
      • kwargs={'training': 'None'}

↓no errors occur

from m3gnet.models import M3GNetCalculator, Potential, M3GNet
from ase.io import read
from ase import Atoms

def main():
    potential = Potential(M3GNet.load())
    calc = M3GNetCalculator(potential=potential)
    print("H2")
    atoms = Atoms('H2', positions=[(0, 0, -0.35), (0, 0, 0.35)])
    atoms.set_calculator(calc)
    try:
        print(atoms.get_potential_energy())
    except Exception as e:
        print(e)

    print("H")
    atoms = Atoms('H', positions=[(0, 0, -0.35)])
    atoms.set_calculator(calc)
    try:
        print(atoms.get_potential_energy())
    except Exception as e:
        print(e)

if __name__ == '__main__':
    main()

H2
[-6.506641]
H
[-1.1176894]

[sgbaird]

Since M3GNet uses a graph network under the hood, when you pass only one atom, there is only a single node (no edges) in the "graph". That's probably why you're seeing that error. If it's only important to you for testing, then it's probably safe to ignore. Note: I'm not one of the repo owners/writers