Your first calculation

Choose the desktop workflow to explore a preset visually, or the CLI workflow for a small, reproducible Schrödinger–Poisson calculation.

Desktop: inspect a quantum-well preset

  1. Start aestimo-gui in your installed environment.

  2. Select Dingle 1975 Confinement (Validation) from Example Presets.

  3. Inspect Structure and Solver & Grid. Keep the preset’s settings for the first run.

  4. Use Save Project and name the copy my_dingle_qw.json. Keeping dingle in the filename retains the named comparison suite.

  5. Click Run Simulation and follow Console until it finishes.

  6. Open Plotting & Results and check the selected Simulation Run.

The QW workflow displays band edges and confined-state wavefunctions, transition information, overlaps and carrier profiles. The Dingle comparison additionally runs a predefined well-width series. Its comparison curve is a study across multiple structures; it is not simply the band diagram of the one displayed layer stack.

As a physical trend, stronger confinement in a narrower well generally raises electron confinement energies relative to the well edge. In a finite, self-consistent heterostructure the result also depends on barriers, masses and charge redistribution. Inspect the actual computed trend and convergence rather than assigning a universal numerical shift.

The historical word “Validation” remains in this preset’s name. Check the reference status and comparison metrics described in Validation before using the result as evidence of experimental agreement.

CLI: run the barrier-doped single-well example

This workflow uses a source checkout, because the complete Python example is provided there. From the checkout root, with Aestimo installed:

aestimo -i examples/sample_1qw_barrierdope_ingaas.py

Despite its historical filename, the current layer list contains a GaAs well with AlGaAs barriers. The input selects scheme 2, temperature 300 K and a 1 nm grid. Read the actual material list when identifying the structure.

A successful run creates:

sample_1qw_barrierdope_ingaas_output/

Open aestimo.log there. Inspect the band profiles in potn_eh_equi_cond.dat, the carrier profiles in np_data0_equi_cond.dat, and available states_e_QWR*_equi_cond.dat / states_h_QWR*_equi_cond.dat files. State-file energies are in meV and effective masses are ratios to the free electron mass. Outputs explains the columns.

To display the figures after calculating:

aestimo -d -i examples/sample_1qw_barrierdope_ingaas.py

Make one controlled change

Keep the original input. Copy it to a new filename, change one physical or numerical setting, and run the copy. For example, compare gridfactor = 1.0 with gridfactor = 0.5, keeping the layer structure and doping unchanged. The smaller spacing increases the number of grid points; compare energies or another selected observable to assess sensitivity to the mesh.

When changing a layer thickness, also review expressions derived from the total thickness and grid size. Python inputs execute in order. Do not change the physical structure and mesh simultaneously if you want to identify which change caused a difference in the result.

Use a new input stem or separate working directory for each retained run to avoid replacing earlier output. Keep the input, any database changes, log and Aestimo version/commit together.