Examples¶
The following forcefield optimization examples showcase the features of EZFF
lj-gulp-serial¶
Optimization of a Lennard-Jones forcefield for FCC Neon against 2 objectives – bulk modulus and lattice constant
Features demonstrated in this example
- Basic use of forcefield templates and variable_range files
- Reading-in elastic modulus tensor from GULP run
- Reading-in lattice constants after a GULP run
lj-lammps-serial¶
Optimization of a Lennard-Jones forcefield for FCC Neon against 2 objectives – bulk modulus and lattice constant
Features demonstrated in this example
- Reading-in elastic modulus tensor from LAMMPS run
- Reading-in lattice constants after a LAMMPS run
sw-gulp-serial¶
Optimization of a Stillinger-Weber forcefield for the 2H-MoSe2 monolayer system against 3 objectives – Lattice constant (a), Elastic modulus (\(C_{11}\)) and Phonon dispersion
Features demonstrated in this example
- Reading-in phonon dispersion from GULP and VASP data files
- Calculating error between phonon dispersions
- Calculating error between computed and ground-truth phonon dispersions
- Reading-in elastic modulus tensor from GULP run
- Usage of the Multi-objective Bayesian Optimizer
sw-gulp-multialgo¶
Parallel optimization of a Stillinger-Weber forcefield for the 1T’ monolayer system against 6 objectives – Two lattice constants (a and b), One elastic modulus (\(C_{11}\)) and Three phonon dispersion curves (one each for compressed, relaxed and expanded crystals) using a sequence of multiple multi-objective genetic algorithms. Here, the population from the last epoch of optimization with a single algorithm is used as the initial population for the next algorithm in the sequence.
Features demonstrated in this example
- Using multiple genetic algorithms in sequence for a single problem
- Use of different population sizes and epochs for each algorithm in the sequence
sw-gulp-parallel-multi¶
Parallel optimization of a Stillinger-Weber forcefield for the 1T’ monolayer system against 6 objectives – Two lattice constants (a and b), One elastic modulus (\(C_{11}\)) and Three phonon dispersion curves (one each for compressed, relaxed and expanded crystals)
Features demonstrated in this example
- Spawning and using Multiprocessing pools for optimization
- Non-uniform weighting schemes for calculating phonon dispersion errors
sw-gulp-parallel-mpi¶
Parallel optimization of a Stillinger-Weber forcefield for the 1T’ monolayer system against 6 objectives – Two lattice constants (a and b), One elastic modulus (\(C_{11}\)) and Three phonon dispersion curves (one each for compressed, relaxed and expanded crystals)
Features demonstrated in this example
- Spawning and using MPI pools for optimization
vashishta-lammps-serial¶
Optimization of a Stillinger-Weber forcefield for SiC crystal against 2 objectives – Lattice constant (a) and Elastic modulus (\(C_{11}\))
Features demonstrated in this example
- Reading-in elastic modulus tensor from LAMMPS run
- Optimization of Vashishta potential
reaxff-charge-gulp-serial¶
Optimization of charge parameters in the ReaxFF forcefield for a thio-ketone monomer against 1 objective – atomic charges
Features demonstrated in this example
- Use of make_template_qeq
- Use of ezff.error_atomic_charges
- Use of nevergrad single-objective optimizers
reaxff-distortion-gulp-serial¶
Optimization of charge and bond parameters in the ReaxFF forcefield for a thio-ketone monomer against 2 objective – atomic charges and structural distortion
Features demonstrated in this example
- Use of ezff.error_structure_distortion
reaxff-lammps-parallel-multi¶
Parallel optimization of ReaxFF forcefield for a thio-ketone monomer against 2 objectives – Dissociation energy of the C-S bond and C-S vibrational frequency
Features demonstrated in this example
- Using QChem interface to read-in QM energies
- Using LAMMPS interface to perform single-point calculations and read-in energy
- Using utils.reaxff methods for generating forcefields templates and variable range files
- Heterogeneous weighting scheme for calculating errors from potential energy surface scans
lj-gulp-save-restart¶
Serial optimization of Lennard Jones forcefield for solid Neon against 2 objectives – Lattice constant (a) and Elastic modulus (\(C_{11}\))
Features demonstrated in this example
- Save evaluated variables as numpy arrays
- Continue optimization after loading pre-evaluated variables
pedone-lammps-parallel-multi¶
Serial optimization of the Pedone forcefield (hybrid mixture of Coulombic + Morse + Repulsive interactions) for amorphous SiO2 against 2 objectives – Lattice constant (a) and Elastic modulus (\(C_{11}\))
Features demonstrated in this example
- Parameterization of hybrid forcefields (containing 2 or more forcefield types) in LAMMPS