Papers
Active Learning
Scaling Deep Learning for Materials Discovery (Nature, 2023) – Google DeepMind
Accelerated Discovery of Large Electrostrains in BaTiO3‐Based Piezoelectrics Using Active Learning (Advanced Materials, 2018)
Accelerated Search for Materials with Targeted Properties by Adaptive Design (Nature Communications, 2016)
Bias Free Multiobjective Active Learning for Materials Design and Discovery (Nature Communications, 2021)
Graph Neural Networks
MEGNet: Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals (Chemistry of Materials, 2019)
CHGNet as a Pretrained Universal Neural Network Potential for Charge-informed Atomistic Modelling (Nature Machine Intelligence, 2023)
Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties (Physical Review Letters, 2018)
GemNet: Universal Directional Graph Neural Networks for Molecules (arXiv, 2022)
E(3)-Equivariant Graph Neural Networks for Data-efficient and Accurate Interatomic Potentials (Nature Communication, 2022)
Natural Language Processing
ChemDataExtractor: A Toolkit for Automated Extraction of Chemical Information from the Scientific Literature (Journal of Chemical Information and Modeling, 2016)
Quantifying the Advantage of Domain-specific Pre-training on Named Entity Recognition Tasks in Materials Science (Patterns, 2022)
Leveraging Large Language Models for Predictive Chemistry (Nature Machine Intelligence, 2024)
A Multi-modal Pre-training Transformer for Universal Transfer Learning in Metal-organic Frameworks (Nature Machine Intelligence, 2023)
ChatGPT Chemistry Assistant for Text Mining and the Prediction of MOF Synthesis (Journal of the American Chemical Society, 2023)
Crystal Structure Generation with Autoregressive Large Language Modeling (arXiv, 2023)