Perovskites

[rakuten]

Data

• https://www.perovskitedatabase.com
• An open-access database and analysis tool for perovskite solar cells based on the FAIR data principles | Nature Energy
  – 16000+

Review of Material Design

• Inverse molecular design using machine learning: Generative models for matter engineering
• Molecular representations in AI-driven drug discovery: a review and practical guide | Journal of Cheminformatics | Full Text
• Deep learning for molecular design—a review of the state of the art - Molecular Systems Design & Engineering (RSC Publishing)
• Machine learning the ropes: principles, applications and directions in synthetic chemistry - Chemical Society Reviews (RSC Publishing)
• https://wires.onlinelibrary.wiley.com/doi/full/10.1002/wcms.1603
• https://www.annualreviews.org/doi/abs/10.1146/annurev-matsci-070218-010015?casa_token=XPXPEhh5eY0AAAAA:cdv3Fu2KdzG3kLlNZw3PFIV9Rd1-Vz2HSgY68TLZEn3T3yyvUOyZ28t53ES5Tiv9UElPTHexLzhj
• https://onlinelibrary.wiley.com/doi/full/10.1002/inf2.12028
• Infusing theory into deep learning for interpretable reactivity prediction | Nature Communications

Graph Machine Learning for Material Design

• KDD 20 ASGN: An active semi-supervised graph neural network for molecular property prediction
• ICML 21 Improving Molecular Graph Neural Network Explainability with Orthonormalization and Induced Sparsity
• ICDM 20 Heterogeneous molecular graph neural networks for predicting molecule properties
• Nature Molecular Geometry Prediction using a Deep Generative Graph Neural Network | Scientific Reports
• IJCAI 20 [2005.05537] GoGNN: Graph of Graphs Neural Network for Predicting Structured Entity Interactions
• Nature Benchmarking graph neural networks for materials chemistry | npj Computational Materials
• NIPS 20 On the equivalence of molecular graph convolution and molecular wave function with poor basis set

Tasks

• Attribute prediction on normal attributes
• Attribute prediction on molecular graphs
• (inverse prediction) molecular generation
• (retro)synthesis prediction
• interpretable / explainable prediction/generation
• automatic knowledge discovery
• from chemical structure to crystal

GitHub - mufeili/DL4MolecularGraph: Literature of deep learning for graphs in...

Material Science with Machine Learning

Review Nature: Machine learning for molecular and materials science Deep learning for molecular design—a review of the state of the art [Journal Version] From DFT to machine learning: recent approaches to materials science–a review A Critical Review of Machine Learning of Energy Materials Deep Learning in Chemistry Journal Nature: Machine-learning-assisted materials discovery using failed experiments Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals Accele…

Awesome Molecular Graph

Generative Model ICML 21 Equivariant message passing for the prediction of tensorial properties and molecular spectra ICML 21 An End-to-End Framework for Molecular Conformation Generation via Bilevel Programming ICML 21 Fold2Seq: A Joint Sequence(1D)-Fold(3D) Embedding-based Generative Model for Protein Design ICML 21 Improving Molecular Graph Neural Network Explainability with Orthonormalization and Induced Sparsity ICML 21 Efficient Generative Modelling of Protein Structure Fragments u…