Comments (2)
Hi @rfhari , thank you for using PACE!
We explicitly provide "pair" forces for each atom to LAMMPS (https://github.com/lammps/lammps/blob/develop/src/ML-PACE/pair_pace.cpp#L223) so, I would assume that atomic virials also should be computed by LAMMPS correctly.
Please, check it and let us know if it work.
Regards,
Yury
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I think ACE does not have the required 9-component per-atom virial for correctly calculating the heat flux yet.
In terms of code, one must use cvflag_atom
in LAMMPS interface.
For theoretical background, one can check this review article that demonstrates how MTP (very similar to ACE) is failing to produce the correct heat flux: Molecular dynamics simulations of heat transport using machine-learned potentials: A mini-review and tutorial on GPUMD with neuroevolution potential. Therefore, it is not sufficient to provide pair forces for each atom to produce the correct atomic virials for heat flux calculation. More efforts are needed to make heat flux right.
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Related Issues (20)
- Issues when running LAMMPS with ACE HOT 2
- - HOT 1
- Large Force Errors HOT 5
- specifying embedding-function for PyACECalculator HOT 1
- [feature request] OpenMP support for LAMMPS ML-PACE HOT 5
- Suggestions for interfacing with RMC HOT 4
- Try to continue fitting part of the potential
- YAML safe_load has been removed HOT 3
- Run PyACECalculator on GPU. HOT 2
- Minimum GCC/G++ version?
- Empty radcoefficients list when initializing only first order terms
- New version on pip?
- magnetic ace
- Error when using trf/lm/dogbox optimizer HOT 1
- Need suggestion for fitting a five-component system HOT 1
- Maximum number of species ACE can fit in one potential HOT 5
- Active learning with unparametrized ACE potential HOT 2
- Unphysical LAMMPS results using the trained Al potential HOT 6
- Effectively use D-optimality with existing dataset HOT 5
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