Discovery Process

Scientific discovery is increasingly reliant on data-driven approaches, aiming to accelerate the identification of novel materials, efficient procedures, and improved processes. Current research focuses on developing sophisticated algorithms, such as graph-based methods and machine learning models (e.g., Gaussian processes, random forests), to analyze complex datasets and predict optimal outcomes, often incorporating human expertise for validation and refinement. Effective dataset engineering and the development of robust performance estimation methods are crucial for optimizing these discovery processes, ultimately leading to faster advancements across various scientific disciplines and practical applications.