Merge pull request #25 from cesmix-mit/active_learning_refactor

Active learning refactor
cesmix-mit · Feb 1, 2023 · d79d53e · d79d53e
2 parents 10e645a + eaf2b5a
commit d79d53e
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diff --git a/.gitignore b/.gitignore
@@ -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/
diff --git a/Project.toml b/Project.toml
@@ -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"
diff --git a/docs/src/index.md b/docs/src/index.md
@@ -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.