To import a database stored as an xyz file, use

data = IPFitting.Data.read_xyz(fname)

See ?read_xyz for further options. This will return a Vector{Dat} where each Dat is a container storing the atomistic configurion (JuLIP.Atoms), the configtype as well as the "DFT observations".

A basis is defined by

• choice of bond-length or bond-angle PIPs.
• space transform
• choice of cut-off
• body-order
• polynomial degree

For example, using bond-angle PIPs with Morse coordinates and a cosine cut-off to model Si, we first define a descriptor

r0 = rnn(:Si)
rcut = 2.5 * r0
desc = BondAngleDesc("exp(- (r/$r0 - 1.0))", CosCut(rcut-1, rcut))

We can then generate basis functions using nbpolys, e.g.,

#            body-order, descriptor, degree
B4 = nbpolys(4,          desc,       8)

In practise, one would normally specify different cut-offs and space transforms for different body-orders. Suppose these give descriptors D2, D3, D4, then a 4-body basis can be constructed via

B = [ nbpolys(2, D2, 14); nbpolys(3, D3, 11); nbpolys(4, D4, 8) ]

For more details and more complex basis sets, see below.

Once the dft dataset and the basis functions have been specified, the least-squares system matrix can be assembled. This can be very time-consuming for high body-orders, large basis sets and large data sets. Therefore this matrix is stored in a block format that allows us to later re-use it in a variety of different ways. This is done via

db = LsqDB(fname, configs, basis)

• The db is stored in two files: fname_info.jld2 and fname_kron.h5. In particular, fname is the path + name of the db, but without ending. E.g, "~/scratch/nbodyips/W5Bdeg12env".
• configs is a Vector{Dat}
• basis is a Vector{<: AbstractCalculator}
• The command db = LsqDB(fname, configs, basis) evaluates the basis functions, e.g., energy(basis[i], configs[j].at) for all i, j, and stores these values which make up the lsq matrix.

To reload a pre-computed lsq system, use LsqDB(fname). To compute a lsq system without storing it on disk, use LsqDB("", configs, basis), i.e., pass an empty string as the filename.

The main function to call is lsqfit(db; kwargs...) -> IP, fitinfo The system is solved via (variants of) the QR factorisation. See ?lsqfit for details.

The output IP of lsqfit is a JuLIP.AbstractCalculator which supports energy, forces, virial, site_energies. (todo: write more here, in particular mention fast)

there are two functions filter_basis and filter_configs that can be used to choose a subset of the data and a subset of the basis. For example, to take only 2B:

Ib2 = filter_basis(db, b -> (bodyorder(b) < 2))

See inline documentation for more details.

Suppose configs::Vector{Dat} is a list of configurations, then we can add fitting error information by calling

add_fits!(myIP, configs, fitkey="myIP")

This will evaluate all observations stored in configs with the new IP and store them in configs[n].info[fitkey]["okey"]. These observation values can then be used to compute RMSE, produce scatter plots, etc.

This calculation can take a while. If myIP has just been fitted using lsqfit then there is a quicker way to generate the fitting errors, but this is not yet implemented. TODO: implement this!