# Autonomous discovery in the chemical sciences part I: Progress

arXiv: https://arxiv.org/abs/2003.13754 · HTML: https://mlsanity.com/paper/2003.13754/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Mar 2020.

## Built on by (most cited first, 25 of 25)

- [Local Latent Space Bayesian Optimization over Structured Inputs](https://mlsanity.com/paper/2201.11872/) (2022)
- [Amortized Tree Generation for Bottom-up Synthesis Planning and Synthesizable Molecular Design](https://mlsanity.com/paper/2110.06389/) (2021)
- [Generative Artificial Intelligence for Navigating Synthesizable Chemical Space](https://mlsanity.com/paper/2410.03494/) (2024)
- [Bayesian optimization with known experimental and design constraints for chemistry applications](https://mlsanity.com/paper/2203.17241/) (2022)
- [Artificial Intelligence and Generative Models for Materials Discovery -- A Review](https://mlsanity.com/paper/2508.03278/) (2025)
- [Agentic AI Scientists Are Not Built For Autonomous Scientific Discovery](https://mlsanity.com/paper/2605.08956/) (2026)
- [Golem: An algorithm for robust experiment and process optimization](https://mlsanity.com/paper/2103.03716/) (2021)
- [Graph Machine Learning for Design of High-Octane Fuels](https://mlsanity.com/paper/2206.00619/) (2022)
- [Procedural Synthesis of Synthesizable Molecules](https://mlsanity.com/paper/2409.05873/) (2024)
- [Towards Futuristic Autonomous Experimentation--A Surprise-Reacting Sequential Experiment Policy](https://mlsanity.com/paper/2112.00600/) (2021)
- [Active learning for affinity prediction of antibodies](https://mlsanity.com/paper/2406.07263/) (2024)
- [A Transferable Recommender Approach for Selecting the Best Density Functional Approximations in Chemical Discovery](https://mlsanity.com/paper/2207.10747/) (2022)
- [Synthetic data enable experiments in atomistic machine learning](https://mlsanity.com/paper/2211.16443/) (2022)
- [Gemini: Dynamic Bias Correction for Autonomous Experimentation and Molecular Simulation](https://mlsanity.com/paper/2103.03391/) (2021)
- [Autonomous Drug Design with Multi-Armed Bandits](https://mlsanity.com/paper/2207.01393/) (2022)
- [Position: Intelligent Science Laboratory Requires the Integration of Cognitive and Embodied AI](https://mlsanity.com/paper/2506.19613/) (2025)
- [AtomBridge: Agentic VLA Inference Plugin for Long-Horizon Tasks in Scientific Experiments](https://mlsanity.com/paper/2602.09430/) (2026)
- [An In-depth Summary of Recent Artificial Intelligence Applications in Drug Design](https://mlsanity.com/paper/2110.05478/) (2021)
- [Speculative Exploration on the Concept of Artificial Agents Conducting Autonomous Research](https://mlsanity.com/paper/2312.03497/) (2023)
- [Toward Building Science Discovery Machines](https://mlsanity.com/paper/2103.15551/) (2021)
- [Meaningful machine learning models and machine-learned pharmacophores from fragment screening campaigns](https://mlsanity.com/paper/2204.06348/) (2022)
- [Transforming organic chemistry research paradigms: moving from manual efforts to the intersection of automation and artificial intelligence](https://mlsanity.com/paper/2312.00808/) (2023)
- [A Comprehensive Benchmarking Platform for Deep Generative Models in Molecular Design](https://mlsanity.com/paper/2505.12848/) (2025)
- [ARCO-BO: Adaptive Resource-aware COllaborative Bayesian Optimization for Heterogeneous Multi-Agent Design](https://mlsanity.com/paper/2510.16652/) (2025)
- [Beyond Structure: Revolutionising Materials Discovery via AI-Driven Synthesis Protocol-Property Relationships](https://mlsanity.com/paper/2605.00313/) (2026)

## Abstract (arXiv)

This two-part review examines how automation has contributed to different aspects of discovery in the chemical sciences. In this first part, we describe a classification for discoveries of physical matter (molecules, materials, devices), processes, and models and how they are unified as search problems. We then introduce a set of questions and considerations relevant to assessing the extent of autonomy. Finally, we describe many case studies of discoveries accelerated by or resulting from computer assistance and automation from the domains of synthetic chemistry, drug discovery, inorganic chemistry, and materials science. These illustrate how rapid advancements in hardware automation and machine learning continue to transform the nature of experimentation and modelling. Part two reflects on these case studies and identifies a set of open challenges for the field.