ML LHEAT: Difference between revisions
No edit summary |
No edit summary |
||
Line 13: | Line 13: | ||
</math> | </math> | ||
where <math>\mathbf{r}_{i}</math>, <math>\mathbf{v}_{i}</math> and <math>E_{i}</math> denote the position vector, velocity and energy of atom <math>i</math>, respectively. The heat flux can be further rewritten as | where <math>\mathbf{r}_{i}</math>, <math>\mathbf{v}_{i}</math> and <math>E_{i}</math> denote the position vector, velocity and energy of atom <math>i</math>, respectively. The number of atoms in the system is denoted by <math>N_{a}</math>. The heat flux can be further rewritten as | ||
<math> | <math> | ||
Line 19: | Line 19: | ||
</math> | </math> | ||
Using the equation of motions | |||
<math> | |||
m_{i} \frac{d \mathbf{v}_{i}}{dt} = - \sum\limits_{j=1}{N_{a}} \nabla_{i} U_{j} | |||
</math> | |||
Finally (in a post-processing step), the thermal conductivity at temperature <math>T</math> in the Green-Kubo formalism can be calculated from the correlation of the heat flux <math>\mathbf{q}</math> as | |||
<math> | <math> |
Revision as of 07:54, 9 June 2021
ML_FF_LHEAT_MB = [logical]
Default: ML_FF_LHEAT_MB = .FALSE.
Description: This flag specifies whether the heat flux is calculated or not in the machine learning force field method.
The heat flux within machine learning force fields can is decomposed into atomic contributions written as
where , and denote the position vector, velocity and energy of atom , respectively. The number of atoms in the system is denoted by . The heat flux can be further rewritten as
Using the equation of motions
Finally (in a post-processing step), the thermal conductivity at temperature in the Green-Kubo formalism can be calculated from the correlation of the heat flux as
where and denotes the volume of the system and the Boltzmann constant, respectively.
The heat flux is written to the file ML_HEAT.