Implementation

Methods for calculating the curvature effective mass

Three methods are used to calculate the curvature effective mass (Eqn.1 in the main text).

Finite difference

We use a three point forward finite difference equation to calculate the curvature at point i:
```
\frac{\partial^2E}{\partial k^2} = \frac{E_{i+2} - 2E_{i+1} + E_{i}}{\left|k_{i+1} - k_i\right|},
```
where E_i is the energy eigenvalue at position k_i in reciprocal space.
Unweighted least-squares fit

To obtain estimates for the coefficient of a parabolic dispersion
```
E = ck^2,
```
we use the least-squares method as implemented in the NumPy Python library to minimise the summed square of residuals
```
\sum^{5}_{i=1}(ck_i^2 - E_i)^2.
```
We fit to five points; three points from the DFT-calculated dispersion plus two from the symmetry of the dispersion (E(k)=E(-k)).
Weighted least-squares fit

To obtain estimates for the coefficients of the dispersion
```
E = {c}k^2,
```
we use the least-squares method as implemented in the NumPy Python library to minimise the summed square of residuals
```
\sum^{n}_{i=1}W_i(ck_i^2 - E_i)^2.
```
The summation is over all points up to an energy of 0.25\,eV, including points generated from the symmetry of the dispersion, E(k)=E(-k). W_i is given by
```
W_i(E_i,T) = \frac{1}{\exp\left(\frac{E_i-E_f}{k_BT}\right)+1}.
```