Angeles Pulido
Papers
2
Total Citations
103
H-Index
2
About
Angeles Pulido is a leading researcher in computational chemistry and materials science, whose work bridges the gap between molecular prediction and experimental realization. Her primary research areas include supramolecular chemistry, crystal structure prediction, and the design of multi-component organic cage structures. Pulido’s major contribution lies in pioneering a seamless workflow that integrates computational modeling with robotic synthesis to create complex molecular architectures. Her most cited work, “From Concept to Crystals via Prediction: Multi‐Component Organic Cage Pots by Social Self‐Sorting” (2019), has accumulated over 100 citations, demonstrating its significant impact. This study showcases how a priori computational predictions of molecular precursors and crystal structures can guide the automated, high-throughput synthesis of multi-component cage pots through social self-sorting—a process where molecules selectively assemble into distinct, functional structures. Pulido’s innovative approach has advanced the field by enabling the rational design of porous materials with tailored properties, offering a powerful toolkit for researchers in drug delivery, catalysis, and gas storage. Her work exemplifies the future of materials discovery, where computation and automation accelerate the path from concept to crystalline reality.
Research Focus
Key Achievements
Top Papers
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