ML LHEAT: Difference between revisions

Latest revision as of 07:24, 10 May 2022

ML_LHEAT = [logical]
Default: ML_LHEAT = .FALSE.

Description: This tag specifies whether the heat flux is calculated or not in the machine learning force field method.

The heat flux within machine learning force fields is decomposed into atomic contributions written as

${\displaystyle \mathbf{q}(t) = \sum\limits_{i=1}^{N_{a}} \frac{d}{dt} \left( \mathbf{r}_{i} E_{i} \right), }$

${\displaystyle E_{i}=\frac{m_{i} \left|\mathbf{v}_{i} \right|^{2}}{2} + U_{i} }$

where $\mathbf{r}_{i}$ , $\mathbf{v}_{i}$ and $E_{i}$ denote the position vector, velocity and energy of atom $i$ , respectively. The number of atoms in the system is denoted by $N_{a}$ . The heat flux can be further rewritten as

${\displaystyle \mathbf{q}(t) = \sum\limits_{i=1}^{N_{a}} \mathbf{v}_{i} E_{i} + \sum\limits_{i=1}^{N_{a}} \mathbf{r}_{i} \left( m_{i} \mathbf{v}_{i} \cdot \frac{d\mathbf{v}_{i}}{dt} + \sum\limits_{j=1}^{N_{a}} \mathbf{v}_{j} \cdot \nabla_{j} U_{i} \right). }$

Using the equation of motions

${\displaystyle m_{i} \frac{d \mathbf{v}_{i}}{dt} = - \sum\limits_{j=1}^{N_{a}} \nabla_{i} U_{j} }$

the heat flux can be simplified to

${\displaystyle \mathbf{q}(t) = \sum\limits_{i=1}^{N_{a}} \mathbf{v}_{i} E_{i} - \sum\limits_{i=1}^{N_{a}} \sum\limits_{j=1}^{N_{a}} \mathbf{r}_{i} \left( \mathbf{v}_{i} \cdot \nabla_{i} U_{j} \right) + \sum\limits_{i=1}^{N_{a}} \sum\limits_{j=1}^{N_{a}} \mathbf{r}_{i} \left( \mathbf{v}_{j} \cdot \nabla_{j} U_{i} \right) = \sum\limits_{i=1}^{N_{a}} \mathbf{v}_{i} E_{i} + \sum\limits_{i=1}^{N_{a}} \sum\limits_{j=1}^{N_{a}} \left( \mathbf{r}_{i} - \mathbf{r}_{j} \right) \left( \mathbf{v}_{j} \cdot \nabla_{j} U_{i} \right). }$

Finally (in a post-processing step), the thermal conductivity at temperature $T$ in the Green-Kubo formalism can be calculated from the correlation of the heat flux $\mathbf{q}$ as

${\displaystyle \kappa = \frac{1}{3Vk_{b}T^{2}} \int\limits_{0}^{\infty} \langle \mathbf{q}(t) \cdot \mathbf{q}(0) \rangle dt, }$

where $V$ and $k_{b}$ denotes the volume of the system and the Boltzmann constant, respectively.

The heat flux is written to the file ML_HEAT.

@@ Line 1: / Line 1: @@
-{{TAGDEF|ML_FF_LHEAT_MB|[logical]|.FALSE.}}
+{{DISPLAYTITLE:ML_LHEAT}}
+{{TAGDEF|ML_LHEAT|[logical]|.FALSE.}}
-Description: This flag specifies whether the heat flux is calculated or not in the machine learning force field method.
+Description: This tag 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
+The heat flux within machine learning force fields is decomposed into atomic contributions written as
 <math>
@@ Line 13: / Line 14: @@
 </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>
-\mathbf{q}(t) = \sum\limits_{i=1}^{N_{a}} \mathbf{v}_{i} E_{i} + \sum\limits_{i=1}^{N_{a}} \mathbf{r}_{i} \left( m_{i} \mathbf{v}_{i} \cdot \frac{d\mathbf{v}_{i}}{dt} + \sum\limits_{j=1}^{N_{a}} \mathbf{v}_{j} \cdot \Nabla_{j} U_{i} \right).
+\mathbf{q}(t) = \sum\limits_{i=1}^{N_{a}} \mathbf{v}_{i} E_{i} + \sum\limits_{i=1}^{N_{a}} \mathbf{r}_{i} \left( m_{i} \mathbf{v}_{i} \cdot \frac{d\mathbf{v}_{i}}{dt} + \sum\limits_{j=1}^{N_{a}} \mathbf{v}_{j} \cdot \nabla_{j} U_{i} \right).
 </math>
+Using the equation of motions
-The thermal conductivity at temperature <math>T</math> in the Green-Kubo formalism is calculated from the correlation of the heat flux <math>\mathbf{q}</math> as
+<math>
+m_{i} \frac{d \mathbf{v}_{i}}{dt} = - \sum\limits_{j=1}^{N_{a}} \nabla_{i} U_{j}
+</math>
+the heat flux can be simplified to
+<math>
+\mathbf{q}(t) = \sum\limits_{i=1}^{N_{a}} \mathbf{v}_{i} E_{i} - \sum\limits_{i=1}^{N_{a}} \sum\limits_{j=1}^{N_{a}} \mathbf{r}_{i} \left( \mathbf{v}_{i} \cdot \nabla_{i} U_{j} \right) + \sum\limits_{i=1}^{N_{a}} \sum\limits_{j=1}^{N_{a}} \mathbf{r}_{i} \left( \mathbf{v}_{j} \cdot \nabla_{j} U_{i} \right) = \sum\limits_{i=1}^{N_{a}} \mathbf{v}_{i} E_{i} + \sum\limits_{i=1}^{N_{a}} \sum\limits_{j=1}^{N_{a}} \left( \mathbf{r}_{i} - \mathbf{r}_{j} \right) \left( \mathbf{v}_{j} \cdot \nabla_{j} U_{i} \right).
+</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>
@@ Line 33: / Line 45: @@
 The heat flux is written to the file {{TAG|ML_HEAT}}.
-== Related Tags and Sections ==
+== Related tags and articles ==
-{{TAG|ML_FF_LMLFF}}, {{TAG|ML_FF_LEATOM_MB}}
+{{TAG|ML_LMLFF}}, {{TAG|ML_LEATOM}}
-{{sc|ML_FF_LHEAT_MB|Examples|Examples that use this tag}}
+{{sc|ML_LHEAT|Examples|Examples that use this tag}}
 ----
-[[Category:INCAR]][[Category:Machine Learning]][[Category:Machine Learned Force Fields]][[Category: Alpha]]
+[[Category:INCAR tag]][[Category:Machine-learned force fields]]