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Active learning refactor #25

Merged
merged 13 commits into from
Feb 1, 2023
Merged

Active learning refactor #25

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12eca60
add Active Learning code
dallasfoster May 21, 2022
ba71d99
Merge branch 'main' into active_learning_refactor
dallasfoster Aug 8, 2022
7eb4a4c
Updates to Potential Learning structure
dallasfoster Sep 30, 2022
6e2472f
Big updates v2
dallasfoster Oct 6, 2022
a5ef60b
Updates through testing
dallasfoster Oct 7, 2022
912042e
Update src/Data/datatypes.jl
dallasfoster Oct 10, 2022
d135ed8
Update src/Data/datatypes.jl
dallasfoster Oct 10, 2022
ae37749
bug fixing for pr
dallasfoster Oct 10, 2022
49f68af
Merge branch 'active_learning_refactor' of https://github.com/cesmix-…
dallasfoster Oct 10, 2022
fbe7b34
small fixes
dallasfoster Nov 11, 2022
ffa32a1
Minor updates and workflow additions.
dallasfoster Dec 22, 2022
a742296
Minor bug fix
dallasfoster Dec 29, 2022
eaf2b5a
Completing merge conflicts for Merge branch 'main' of https://github.…
dallasfoster Feb 1, 2023
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10 changes: 9 additions & 1 deletion .gitignore
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Original file line number Diff line number Diff line change
@@ -1,5 +1,13 @@
Manifest.toml

*.DS_Store
*.yaml
*.pdf
*.jld
*.npz
*.ace
# generated docs
docs/build/
docs/src/generated/

# data too large for github
examples/aHfO2/
12 changes: 5 additions & 7 deletions Project.toml
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Expand Up @@ -5,27 +5,25 @@ version = "0.1.0"

[deps]
AtomsBase = "a963bdd2-2df7-4f54-a1ee-49d51e6be12a"
BlackBoxOptim = "a134a8b2-14d6-55f6-9291-3336d3ab0209"
CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b"
DPP = "2673d5e8-682c-11e9-2dfd-471b09c6c819"
Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
Flux = "587475ba-b771-5e3f-ad9e-33799f191a9c"
GalacticOptim = "a75be94c-b780-496d-a8a9-0878b188d577"
InteratomicBasisPotentials = "37c59853-c2ad-4e3a-930c-a41b2395fb19"
InteratomicPotentials = "a9efe35a-c65d-452d-b8a8-82646cd5cb04"
IterTools = "c8e1da08-722c-5040-9ed9-7db0dc04731e"
LAMMPS = "ee2e13b9-eee9-4449-aafa-cfa6a2dbe14d"
LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
Optim = "429524aa-4258-5aef-a3af-852621145aeb"
Optimization = "7f7a1694-90dd-40f0-9382-eb1efda571ba"
OptimizationOptimJL = "36348300-93cb-4f02-beb5-3c3902f8871e"
OrderedCollections = "bac558e1-5e72-5ebc-8fee-abe8a469f55d"
Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
Printf = "de0858da-6303-5e67-8744-51eddeeeb8d7"
Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
StaticArrays = "90137ffa-7385-5640-81b9-e52037218182"
Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
Unitful = "1986cc42-f94f-5a68-af5c-568840ba703d"
UnitfulAtomic = "a7773ee8-282e-5fa2-be4e-bd808c38a91a"
YAML = "ddb6d928-2868-570f-bddf-ab3f9cf99eb6"
Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"

[compat]
julia = "1.7"
InteratomicPotentials = "0.2.4"
julia = "1.8"
1 change: 1 addition & 0 deletions docs/src/index.md
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Expand Up @@ -5,6 +5,7 @@ An open source Julia library for active learning of interatomic potentials in at
## Specific goals
- Intelligent data subsampling: iteratively query a large pool of unlabeled data to extract a minimum number of training data that would lead to a supervised ML model with superior accuracy compared to a training model with educated handpicking.
- Via [DPP](), [clustering](https://docs.google.com/document/d/1SWAanEWQkpsbr2lqetMO3uvdX_QK-Z7dwrgPaM1Dl0o/edit).
- Quantity of Interest based dimension reduction through the theory of Active Subspaces.
- Inference of the optimal values and uncertainties of the model parameters, to propagate them through the atomistic simulation.
- Interatomic potential hyper-parameter optimization. E.g. estimation of the optimum cutoff radius.
- Interatomic potential fitting. The potentials addressed in this package are defined in [InteratomicPotentials.jl](https://github.com/cesmix-mit/InteratomicPotentials.jl) and [InteratomicBasisPotentials.jl](https://github.com/cesmix-mit/InteratomicBasisPotentials.jl). E.g. ACE, SNAP, Neural Network Potentials.
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