Papers
1
Total Citations
22
H-Index
1
About
Jin Da Tan is a leading researcher at the intersection of materials science and software engineering, specializing in the development of digital infrastructures for autonomous materials discovery. His work centers on creating object-oriented frameworks and workflow management systems that enable the seamless evolution and interoperability of materials acceleration platforms (MAPs). Tan’s most-cited paper, “An object-oriented framework to enable workflow evolution across materials acceleration platforms” (2022, 22 citations), introduces a modular architecture that allows researchers to adapt and scale automated experimentation workflows—a critical step toward realizing self-driving laboratories. This contribution addresses a key bottleneck in high-throughput materials science: the rigidity of platform-specific code. By designing reusable, platform-agnostic components, Tan empowers laboratories to integrate diverse instruments and algorithms, accelerating the discovery of novel functional materials. His work has been recognized for bridging the gap between domain science and computational design, making him a pivotal figure in the emerging field of AI-driven materials research. For students and researchers, Tan’s approach exemplifies how thoughtful software engineering can transform experimental workflows, reducing time-to-discovery and fostering collaboration across global materials initiatives.
Research Focus
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Top Papers
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