An autonomous synthesis of organic flow battery molecules using flexible batch Bayesian optimization to handle varying variables sizes in a single batch.

MolDAIS enables data-efficient molecular discovery by adaptively learning sparse, task-relevant subspaces from descriptor libraries, outperforming prior methods in predictive accuracy, optimization efficiency, and interpretability.

An automated electrolyte-gate FET test system enables rapid and precise screening of 96 sensors in a single run, offering enhanced reproducibility and efficiency for diverse chemical sensing applications.

With ever-increasing computational capabilities, robust and automated research workflows have become essential for orchestrating large numbers of interdependent simulations. However, significant technical expertise is still required to configure execution environments, define calculation inputs, interpret outputs, and manage the complexity of parallel code execution on remote machines. To address these challenges, we developed AiiDAlab, a Jupyter-based web platform powered by the AiiDA computational infrastructure that provides a framework for managing and automating computational workflows while ensuring reproducibility through full provenance tracking. Through a collection of open-source user-friendly applications, AiiDAlab enables scientists to set up, execute, and analyze complex computational workflows without interacting directly with the underlying technical details, allowing them to focus on their research questions. In this paper, we discuss how AiiDAlab has matured over the past few years, expanding beyond computational materials science and its AiiDA origins. We present recent developments toward integrating with electronic laboratory notebooks (ELNs) for FAIR-compliant data management, adoption in large-scale facilities for secure access to experimental data and analytical tools, and applications in educational settings. Together with community-driven efforts to simplify onboarding, improve access to computational resources, and support large-scale data workflows, these advancements position AiiDAlab as a powerful platform for accelerating scientific discovery and fostering collaboration across disciplines.

A systematic study of nitration of (hetero)arenes was conducted using HTE. The generated data were used to develop a predictive tool relying on machine learning to evaluate the best reaction conditions depending on the targeted substrate.

Perovskite materials have demonstrated great potential for a wide range of optoelectronic applications due to their exceptional electronic and optical properties.

Kinetics-informed neural networks improve fit quality for multi-pulse and noisy temporal analysis of products datasets.

We have developed an autonomous robotic experimentation system for powder X-ray diffraction. The system demonstrates high precision in sample preparation and analysis, enabling accurate phase quantification with reduced sample quantities.

A Bayesian algorithm with self-optimizing capabilities, tailored for process optimization in continuous flow synthesis with small datasets enhancing efficiency.