In reply to @nw18rica:matrix.tu-darmstadt.de
Is there a function to find an Atoms object via coordinates instead of the identifier in the atoms list? I cant find one, but i think there should be one.
surely i could write it myself i just hoped for better performance if its already implemented.

In reply to @nw18rica:matrix.tu-darmstadt.de
furthermore is there a way to get auto completion working (I am using PyCharm) but auto completion doesnt work for ase objects

from ase.geometry import find_mic

def nearest_atom_index(atoms, position):
  disp_vec = atoms.get_positions() - position
  disp_vec_mic, disp_vec_mic_mag = find_mic(disp_vec, atoms.cell)
  nearest_atom_index = np.argsort(disp_vec_mic_mag)[0]
  return nearest_atom_index

In reply to @nw18rica:matrix.tu-darmstadt.de
furthermore is there a way to get auto completion working (I am using PyCharm) but auto completion doesnt work for ase objects

import numpy as np
from ase.geometry import find_mic

def nearest_atom_index(atoms, position):
  disp_vec = atoms.get_positions() - position
  disp_vec_mic, disp_vec_mic_mag = find_mic(disp_vec, atoms.cell)
  nearest_atom_index = np.argsort(disp_vec_mic_mag)[0]
  return nearest_atom_index

In reply to @mjwaters:matrix.org
I have a question for people that have experience with high-throughput work, how do you automate picking band paths for your band structures?

I generally recommend using the band path algorithm my Munro and Latimer: https://www.nature.com/articles/s41524-020-00383-7.

If I am allowed to speak the devil's name here: https://pymatgen.org/pymatgen.symmetry.html#pymatgen.symmetry.bandstructure.HighSymmKpath

In reply to @mjwaters:matrix.org
I have a question for people that have experience with high-throughput work, how do you automate picking band paths for your band structures?

I generally recommend using the band path algorithm by Munro and Latimer: https://www.nature.com/articles/s41524-020-00383-7.

If I am allowed to speak the devil's name here: https://pymatgen.org/pymatgen.symmetry.html#pymatgen.symmetry.bandstructure.HighSymmKpath

mjwaters: json.dump(..., default=<function which converts objects into something the base JSON encoder understands>)

ase.io.jsonio provides an implementation of that, which is what todict() is for. That's really what todict() is for, although it could conceivably be made to work with other encoders than JSON.

mjwaters: json.dump(..., default=<function which converts objects into something the base JSON encoder understands>)

ase.io.jsonio provides an implementation of that, which is what todict() is for. That's really the only thing todict() is for, although it could conceivably be made to work with other encoders than JSON.

In reply to @askhl:matrix.org

    opt = FIRE2(a,
                dt=0.1,
                maxstep=0.2,
                dtmax=1.0,
                Nmin=20,
                finc=1.1,
                fdec=0.5,
                astart=0.25,
                fa=0.99)
    opt.run(fmax=0.001)
    e1 = a.get_potential_energy()
    n1 = opt.nsteps

    a = system_setup()

    reset_history = []

    def callback(a, r, e, e_last):
        reset_history.append([e - e_last])

    opt = FIRE2(a,
                dt=0.1,
                maxstep=0.2,
                dtmax=1.0,
                Nmin=20,
                finc=1.1,
                fdec=0.5,
                astart=0.25,
                fa=0.99,
                abc=True,
                position_reset_callback=callback)

Here (for example) there are two calls to FIRE2. Many of the parameters are the same. What about:

def new_fire2(arg1, arg2):
    return FIRE2(arg1=arg1, arg2=arg2, <all the things that are the same go here>)