Jose Recatala‐Gomez

Papers

Total Citations

H-Index

About

Jose Recatala‐Gomez is a leading figure in the digital transformation of materials science, specializing in the development of intelligent frameworks for autonomous experimentation and materials acceleration platforms. His most impactful work, "An object-oriented framework to enable workflow evolution across materials acceleration platforms" (2022), has garnered 22 citations and represents a foundational contribution to the field. This research addresses a critical bottleneck in high-throughput materials discovery: the lack of interoperable, adaptable workflows. By designing an object-oriented architecture, Recatala‐Gomez enables disparate experimental platforms to communicate, evolve, and share protocols seamlessly—a key step toward fully autonomous, self-optimizing laboratories. His work bridges the gap between data science and experimental materials science, allowing researchers to rapidly iterate on synthesis and characterization protocols without manual reconfiguration. This framework has been recognized as a pivotal enabler for the next generation of materials acceleration platforms, where machine learning and robotics converge to accelerate the discovery of novel functional materials. Recatala‐Gomez’s contributions are shaping how scientists design, execute, and scale experiments in the age of AI-driven materials research.