Update documentation.

cesmix-mit · Jun 12, 2024 · 197996c · 197996c
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diff --git a/docs/Project.toml b/docs/Project.toml
@@ -8,12 +8,16 @@ Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
 DocumenterCitations = "daee34ce-89f3-4625-b898-19384cb65244"
 InteratomicPotentials = "a9efe35a-c65d-452d-b8a8-82646cd5cb04"
 InvertedIndices = "41ab1584-1d38-5bbf-9106-f11c6c58b48f"
+IterTools = "c8e1da08-722c-5040-9ed9-7db0dc04731e"
 JLD = "4138dd39-2aa7-5051-a626-17a0bb65d9c8"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 Literate = "98b081ad-f1c9-55d3-8b20-4c87d4299306"
+OrderedCollections = "bac558e1-5e72-5ebc-8fee-abe8a469f55d"
 Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
 PotentialLearning = "82b0a93c-c2e3-44bc-a418-f0f89b0ae5c2"
+ProgressBars = "49802e3a-d2f1-5c88-81d8-b72133a6f568"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
+StaticArrays = "90137ffa-7385-5640-81b9-e52037218182"
 Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
 StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
 Unitful = "1986cc42-f94f-5a68-af5c-568840ba703d"

diff --git a/docs/make.jl b/docs/make.jl
@@ -21,6 +21,7 @@ const EXAMPLES_DIR = joinpath(@__DIR__, "..", "examples")
 const OUTPUT_DIR   = joinpath(@__DIR__, "src/generated")
 
 examples = [
+    "Fit a-HfO2 dataset with ACE" => "ACE-aHfO2/fit-ace-aHfO2.jl",
     "Subsample Na dataset with DPP and fit with ACE" => "DPP-ACE-Na/fit-dpp-ace-na.jl",
     "Subsample Si dataset with DPP, fit with ACE, and cross validate" => "DPP-ACE-Si/fit-dpp-ace-si.jl",
     "Load Ar+Lennard-Jones dataset and postprocess" => "LJ-Ar/lennard-jones-ar.jl"

diff --git a/docs/src/index.md b/docs/src/index.md
@@ -1,15 +1,15 @@
 # [WIP] PotentialLearning.jl
 
-PotentialLerning.jl: **Developing optimization workflows for fast and accurate interatomic potentials**. This package is part of a software suite developed for the [CESMIX](https://computing.mit.edu/cesmix/) project.
+**Developing optimization workflows for fast and accurate interatomic potentials**. This package is part of a software suite developed for the [CESMIX](https://computing.mit.edu/cesmix/) project.
 
 ## Goals
 
-**Optimize your atomistic data: intelligent subsampling of large datasets to reduce DFT computations**
+**Optimize your atomistic data:** intelligent subsampling of large datasets to reduce DFT computations
 - Intelligent subsampling of atomistic configurations using algorithms based on [DPP](https://github.com/dahtah/Determinantal.jl), [DBSCAN](https://docs.google.com/document/d/1SWAanEWQkpsbr2lqetMO3uvdX_QK-Z7dwrgPaM1Dl0o/edit), [CUR](https://github.com/JuliaLinearAlgebra/LowRankApprox.jl), etc.
 - Highly scalable parallel subsampling via hierarchical subsampling and distributed parallelism ([Dagger.jl](https://github.com/JuliaParallel/Dagger.jl)).
 - Optimal subsampler choosing via [Hyperopt.jl](https://github.com/baggepinnen/Hyperopt.jl).
 
-**Optimize your interatomic potential model: hyperparameters, coefficients, model compression, and model selection.**
+**Optimize your interatomic potential model:** hyperparameters, coefficients, model compression, and model selection.
 - Parallel optimization of hyperparameters, coefficients, and model selection via [Hyperopt.jl](https://github.com/baggepinnen/Hyperopt.jl); multi-objective optimization (Pareto fronts): force execution time vs fitting accuracy (e.g. MAE of energies and forces).
 - Model compression via feature selection (e.g. [CUR](https://github.com/JuliaLinearAlgebra/LowRankApprox.jl)) and dimensionality reduction (e.g [PCA](https://juliastats.org/MultivariateStats.jl/dev/pca/), Active Subspaces) of atomistic descriptors.
 - Fitting of linear potentials and inference of parameter uncertainties. Training of neural versions of [Julia-ACE](https://github.com/ACEsuit/ACE1.jl) and [LAMMPS-POD](https://docs.lammps.org/pair_pod.html).