This reporitory collects and bookmarks the materials related to Machine Learning that I have read or find important.
- The rise of self-driving labs in chemical and materials sciences [2024]- Nature Synthesis, Review
- High-entropy materials for energy and electronic applications [2024]
- Toward Making Polymer Chemistry Autonomous [2024]
- ChemOS 2.0: An orchestration architecture for chemical self-driving laboratories [2024]
- Self-Driving Laboratories for Chemistry and Materials Science [2024]
- Autonomous chemistry: Navigating self-driving labs in chemical and material sciences [2024]
- Autonomous experiments using active learning and AI [2023]
- The rise of self-driving labs in chemical and materials sciences [2023]
- Adaptively driven X-ray diffraction guided by machine learning for autonomous phase identification [2023]
- Bayesian Optimization of Catalysis With In-Context Learning [2023]
- On-the-fly closed-loop materials discovery via Bayesian active learning [2020] - NComm
- Large language models for scientific discovery in molecular property prediction [2025]
- Are LLMs Ready for Real-World Materials Discovery? [2024]
- Fine-tuning large language models for chemical text mining [2024]
- Are large language models superhuman chemists? [2024]
- Creation of a structured solar cell material dataset and performance prediction using large language models [2024]
- AtomGPT: Atomistic Generative Pretrained Transformer for Forward and Inverse Materials Design [2024]
- Mistral 7B [2023]
- Defect graph neural networks for materials discovery in high-temperature clean-energy applications [2023]
- Fast evaluation of the adsorption energy of organic molecules on metals via graph neural networks [2023]
- A universal graph deep learning interatomic potential for the periodic table [2022]
- Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals [2019]
- Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties [2018]
- Developing an improved crystal graph convolutional neural network framework for accelerated materials discovery [2020]
- Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer [2020] - T5, Encoder-Decoder
- BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension [2019] - BART, Encoder-Decoder
- Physics Nobel scooped by machine-learning pioneers - Nature, 2024 Nobel Physics Prize
- Chemistry Nobel goes to developers of AlphaFold AI that predicts protein structures [2024] - Nature, 2024 Nobel Chemistry Prize
- Detecting hallucinations in large language models using semantic entropy [2024] - Nature
- Autonomous chemical research with large language models [2023] - Nature
- Evolutionary-scale prediction of atomic-level protein structure with a language model [2023] - Science
- Scientific discovery in the age of artificial intelligence [2023] - Nature
- Scaling deep learning for materials discovery [2023] - Nature
- Empowering Generalist Material Intelligence with Large Language Models [2025]
- AI in materials science: Charting the course to Nobel-worthy breakthroughs [2024]
- Structure prediction and materials design with generative neural networks [2023]
- Knowledge-integrated machine learning for materials: lessons from gameplaying and robotics [2023]
- Human- and machine-centred designs of molecules and materials for sustainability and decarbonization [2022]
- Machine learning for a sustainable energy future [2022]
- Towards the computational design of solid catalysts [2009]