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View Code? Open in Web Editor NEWParameterisation of Equivariant Properties of Particle Systems
Parameterisation of Equivariant Properties of Particle Systems
For every model that we test we manually write directional derivative tests like shown below. This can most likely be unified in a little test suite implemented within ACE.Testing
.
import Base.*
struct __TestSVec{T}
val::T
end
*(a::Number, u::__TestSVec) = a * u.val
*(a::SMatrix, u::__TestSVec) = a * u.val
*(a::SArray{Tuple{N1,N2,N3}}, u::__TestSVec) where {N1, N2,N3} =
reshape(reshape(a, Size(N1*N2, N3)) * u.val, Size(N1, N2))
Us = __TestSVec.(randn(SVector{3, Float64}, length(Xs)))
C = randn(typeof(φ.val), length(basis))
F = t -> sum( sum(c .* b.val)
for (c, b) in zip(C, ACE.evaluate(basis, ACEConfig(Xs + t[1] * Us))) )
dF = t -> [ sum( sum(c .* db)
for (c, db) in zip(C, ACE.evaluate_d(basis, ACEConfig(Xs + t[1] * Us)) * Us) ) ]
fdtest(F, dF, [0.0], verbose=true)
the A2B map has many zero columns for all of the symmetries we are considering:
pibasis -> symmbasis
to `coupling -> pibasis -> symmbasis\The SparseBasis Basis selector does not filter the basis functions based on the maxlevels
dictionary.
need to test more broadly where they pass/fail.
I think this function takes the PI basis functions, finds the orders and then maps to some order_weights which we can control using a dictionary D. Does this make sense? (to be placed in /src/rpi/rpi_basis.jl
)
function scaling_N(basis::RPIBasis, p, D; a2b = abs)
wwpi = scaling(basis.pibasis, p)
wwrpi = zeros(Float64, length(basis))
for iz0 = 1:numz(basis)
pibasis_fncs = collect(keys(basis.pibasis.inner[iz0].b2iAA))
orders = order.(pibasis_fncs)
order_weights = [D[order] for order in orders]
wwpi_iz0 = order_weights .* wwpi[basis.pibasis.inner[iz0].AAindices]
wwrpi[basis.Bz0inds[iz0]] = a2b.(basis.A2Bmaps[iz0]) * wwpi_iz0
end
return wwrpi
end
with usage being
D = Dict(1 => 0.1,
2 => 0.5,
3 => 0.1,
4 => 100.00)
ACE.RPI.scaling_N(rpi_basis, 2, D)
I think this returns a scaling vector the size of the rpi_basis, but with scaled laplacian coefficients according to the interaction order.
When the maxdeg parameter is changed from 1.0 even a bit (to 0.95) to decrease the overall size of the basis set, it causes large errors in the forces (but not in the energies).
Implement an interface how to access the 1p basis components via a symbol. E.g.
Rn = B1p[:Rn]
Ylm = B1p[:Ylm]
there are some big files that need to be purged from the git history before 1.0
We need to revise the interface for basis selectors. At the moment it is acting as if the "degree" is not needed, but this is not true. The one-particle basis must be sorted by degree, and the "isadmissable" function must be monotone. This all suggests that we should maybe revert to the "degree" implementation and drop the "isadmissible" version altogether.
This question is more about "selling" than anything else. so far we've somewhat but not strictly followed semver.
@gabor1 @casv2 @dussong How do you feel about tagging v0.8.x at 1.0.0 so we have a record with enforced backward compatibility of the version that we've been using up to now?
In the meantime we continue to develop ACE as versions v0.9.x, 0.10.x
, etc. and eventually tag new branch as 2.0.0
?
This issue is to summarise the next steps in the ACE + IPFitting game, comments and discussion are very welcome. This roadmap will be adjusted according to discussions.
Overall Goal: rock-solid and usable linear regression, some basic hyper parameter optimisation
IPFitting
IPFitting
(@casv2)ACE 2.x will focus on improved composability and nonlinear models
currently the Project.toml says ACE is not compatible with JuLIP version 0.11.2, which is the default JuLIP. This causes conflicts and prevent installation. When I add this version of JuLIP everything seems to work fine.
Lines like this one:
if length(bb) == 1 #MS: Not sure if this should be here
seem wrong. Need to go investigate
There are a few (related) decisions to be made:
(Xi, Xj)
rather than (Xi, Xij)
. But is this a restriction of generality?To make an informed decision on this, I think we need to benchmark the cost of converting (Xi, Xj)
to a single Xij
state that combines information from both.
revisit the idea if switching entirely to allocating operations and employ object pools for bottlenecks. cf. branch co/pool
for initial experiments with ObjectPools.jl
We should probably redesign differentiation in a more organised way, likely following the ChainRules.jl
ideas or even using ChainRules.jl
directly. In particular this should enable us to leverage AD tools when needed.
When the object pools in ACEbase.jl are implemented using a Vector, then the strangest thing happens. After pop!
ing the last element, the pool gets reduced by length one but then when I acquire again the same element is returned. This doesn't always happen and I can't really consistently reproduce it. Most likely some very strange memory management bug. For now I've switched from Vectors to Sets as the pools. This fixes the bug for now, but the push!
(release) operation is now 10 times slower, ca 1 us instead of < 100 ns.
Currently the ACE.EuclideanVector can only be of type complex, but I think it would make sense to have a real version too.
I am thinking of retiring the BasisSelectors
interface in favour of a BasisConstraints
interface. E.g., I could imagine things like the following:
Bsel = Order(3) ∩ TotalDegree(10)
Bsel = Order(5) ∩ WTotalDegree(Dict(...)) ∩ Order(:bond, 1)
etc... no rush, but to me this feels much more intuitive than the fiddling around we are doing right now.
In order to avoid clashes with Base.eltype
we may wish to use
fltype
- floating point typerfltype
- real float typecfltype
- complex float typetest_admodel.jl
has a segfault when Zygoting the loss w.r.t. the model. (indicated in comments on main
branch)
When reading a coefficients c
vector from disk, combine(IP, c)
should be converted from Any to Float64 first.
I am trying to settle on a file format for v1.x and would appreciate feedback on some thoughts:
there are essentially two perspectives:
There are some benefits to both I think. E.g., 1 will lead to MUCH smaller files, but on the other hand it can only be read and understood with the code that wrote it. 2 on the other hand will have lots of "meta-data" type information that is not needed to reconstruct the types but it will make it easy to write a parser at some point that can read it even if the original code is lost or cannot be made to run for whatever reason...
As I'm writing this I wonder whether there is a third way:
@gabor1 your perspective would be particularly appreciated here.
add the option to create a small test set stored with the potentials to test upon loading that the potential evaluates correctly. This would be intended to ensure compatibility across versions of ACE.jl
For testing purposes we want to compare weather or not two states are equivalent. Then we want to overload isapprox such that for two states or DStates A and B we could simply test them by using: A ≈ B.
CC @zhanglw0521 @MatthiasSachs
What if we created the following type hierarchy:
abstract type Property{...} <: StaticArray{...} end
struct MyProp1 <: Property
val ...
end
struct MyProp2 <: Property
val ...
end
then we have the advantage that we can leverage all of StaticArrays.jl
to do arithmetic on these properties.
See also this part of the documentation
One reason I started thinking about this is I am hoping that this might help with derivatives - taking derivatives increases the dimensionality. How best to handle this? Should we take vectors of properties, or should we create a higher-dimensional array?
We have had problems setting up the stable ACE1 with Pyjulip. One of the problems was with Python version (need to have Python 3.8 or older, not 3.9.) The other problem is potentially with JuLIP v0.12, need 0.11. But this latter one is to be confirmed.
At the moment, the 1p basis components all have upper bounds on the degree. This restricts the product 1p basis and further then the many-body basis. In particular it makes it impossible to grow the basis in a systematic way.
So the goal is to remove this and other restrictions to allow the following reverse procedure:
All this requires a more flexible framework to set or adjust the basis spec. What makes it difficult is how the basis spec propagates the other way. This needs to be thought through carefully.
Adding a constant term (SphericalMatrix(L,L)
) to the corresponding ACE basis would not destroy the symmetry of the basis and may help improve the fitting result as well as stability (Maybe?).
We could add it directly to the basis while another thought could be that we add a mean
field for ACELinearModels, which contains the mean value of data. This means that we didn't fit the coefficient for the constant term but give an expected value to it.
In function indexrange
we have the following
# HACK: fix the m range based on the maximal l-range
# this needs to be suitably generalised if we have multiple
# (l, m) pairs, e.g. (l1, m1), (l2, m2)
if haskey(rg, :m)
maxl = maximum(rg[:l])
rg[:m] = collect(-maxl:maxl)
end
says it all ...
It would be nice to have the option for implementing custom distance transforms based on simple formulae.
It'd be nice to obtain the species and interaction order of a basis function. Also, it'd be great to go the other way, e.g. having a rough idea of an important basis function, and retrieve higher n/l/m basis functions "in that direction".
This relates to issue #6 - there is a remaining step which works around that bug, but it should not be required. Specifically the line
fill!(dA, zero(eltype(dA)))
in oneparticlebasis.jl
should not be required since the 1p basis doesn't add into the storage dA
but writes into it. So somewhere we are reading lots of zeros. This suggests also that there are some operations that we shouldn't be performing.
In order to add a scaling(::PolyPairPots)
function, we need to get a second derivative estimate of the OrthPolyBasis
initially. Would I need to write a evaluate_2d!(.. J::OrthPolyBasis, ...)
function and calculate the second derivatives over the transformed t
(given [r_in,r_cut]
and a transform)? Or is there a more simple estimate for the laplacian like for the RPIBasis
?
There seems to be a typo in norm(X::XState)
associated with which the MWE looks like:
julia> Xs = PositionState([1.0,1.0,1.0])
julia> @show norm(Xs)
norm(Xs) = 2.9999999999999996
julia> @show norm(Xs.rr)
norm(Xs.rr) = 1.7320508075688772
and also it won't work for States that have non-number fields, e.g.,
julia> X = ACE.State(rr = [1.0,1.0,1.0], be = :env);
julia> norm(X)
ERROR: MethodError: no method matching iterate(::Symbol)
(A very similar issue occurs in scaling(basis::PIBasis)
I guess?)
This appears to go quite deep into an inability to determine the type of the inner basis. It is amazing that the Julia compiler can work with that anyhow, but for sure the ::Int
hack needs to be removed and this type instability fixed properly.
JuLIP.MLIPs.forces
the call to evaluate_d!
appears to be type-unstable as well. This has to do with the compiler not being able to infer the type of the tmp
variable. Very strange. Maybe the idea of temporary variable structures need to be revisited; maybe they need to be stored in structs rather than named tuples.Doing a multi species fit generally results in a large condition number R (e.g. 1e164) for which doing a regular QR decomposition results in a LAPACK error. Performing the fit with RRQR does work.
ACE v0.8 needs an upper bound for JuLIP, likely 0.10.x, but should be tested carefully. Problem is that ACE tests pass but IPFitting tests fail. Not clear why, some strange import problems that I couldn't track down likely caused by the introduction of ACEbase.
I would like to be able to adapt the radial basis depending on both atomic species of the atoms involved. More specifically, I would like to be able to set the radial transform (r0) based on the pair of involved elements.
Why does this make sense? Why is this important?
Should isadmissible
in line 76 (part of function _gensparse(...)
) in sparsegrids.jl
if all(minvv .== 0) && isadmissible(vv) && filter(vv)
be admissible
?
Is an input variable fundamentally different from an output variable? As we are moving towards layers / message passing kind of constructions this question becomes very relevant. E.g. a position is just a EuclideanVector with a symmetry attached to it. A charge (input) is just an invariant scalar.
Cleaned up my julia installation so I can use a binary that works on all my nodes, and when I took the opportunity to clean up everything and start over, the instructions in the quickstart https://github.com/ACEsuit/ACE.jl/tree/dev-v0.8.x#quickstart don't work. Linux, stock julia 1.7 binary download, and I get
(@v1.7) pkg> registry add https://github.com/JuliaMolSim/MolSim.git
Cloning registry from "https://github.com/JuliaMolSim/MolSim.git"
Added registry `MolSim` to `~/.julia/registries/MolSim`
(@v1.7) pkg> add JuLIP, ACE, IPFitting
Updating registry at `~/.julia/registries/MolSim`
Updating git-repo `https://github.com/JuliaMolSim/MolSim.git`
Resolving package versions...
ERROR: cannot find name corresponding to UUID 49dc2e85-a5d0-5ad3-a950-438e2897f1b9 in a registry
(@v1.7) pkg> add JuLIP, ACE
Resolving package versions...
ERROR: cannot find name corresponding to UUID 49dc2e85-a5d0-5ad3-a950-438e2897f1b9 in a registry
@casv2 if you have some estimates for the larger basis sets?
Please add further items in a separate post
n, l, m
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