CompChem Map

This is a draft of areas I'd like to organise in some taxonomy.

Finding Useful Molecules

• Get a Materials Database(s), either method

Method 1: Direct (Compounds to Properties)

• Use DFT to guide towards one that fits the requirements (slow if we have billions of compounds.),
• Or use the DB to train a NN to make predictions (needs labelled data for training)
• Or similarity metrics to find new (similar) molecules.
• ...

Method 2: Inverse (Properties to Compounds)

1. Use the gradient to update embedding.
2. Maximise or Minimise the needed properties.

The paper's approach is more towards a Direct method. It is a method to generate embeddings that can then be used to train a neural network to predict properties.

This can be arranged (not very tidily) in a chart:

---
config:
    flowchart:
        htmlLabels:false
---

flowchart TB

A[("Compounds Database")]

subgraph Direct["`**Direct**`"]
    direction LR
    B("`Electronic Structure
    Predictions`")
    C("`Train NN on DB
    (with labelled data)`")
    D("`Use similarity metrics to
        find nearby candidates`")
end

subgraph Inverse["`**Inverse**`"]
    direction TB
    E("`Train VAE to create
    smooth surface`")
    E --> F("`Link MLP to
        latent vector`")
    F --> G("`Minimise or Maximise pps.
            by changing vector`")
end

A --> Inverse
A --> Direct

Unsupervised-Learning of Representations of Atoms

Other investigations of unsupervised learning of machine representation of atoms are:

• Zhou, Q. et al. Learning atoms for materials discovery. (2018).
• Tshitoyan, V. et al. Unsupervised word embeddings capture latent knowledge from materials science literature. (2019).
• Chakravarti, S. K. Distributed representation of chemical fragments. (2018).
• Butler K. et al. Distributed Representations of Atoms and Materials for Machine Learning. (2022).

Supervised-Learning of Representations of Atoms

• Jha, D. et al. ElemNet: deep learning the chemistry of materials from only elemental composition. (2018).
• Goodall, R. E. & Lee, A. A. Predicting materials properties without crystal structure: deep representation learning from stoichiometry. (2020).