Scientific Discovery

Scientific discovery is increasingly being automated through the development of AI agents capable of performing the entire scientific workflow, from hypothesis generation to experimental design and analysis. Current research focuses on developing and evaluating these agents using benchmarks and novel algorithms, including large language models (LLMs), neural networks (e.g., recurrent convolutional neural networks, variational autoencoders), and evolutionary computation methods, often applied to specific scientific domains like materials science and biological research. This automation promises to accelerate the pace of scientific discovery across various fields by handling large datasets, complex simulations, and the synthesis of information from diverse sources, ultimately leading to more efficient and impactful research.