Silvia Gomez Coca

Dr. Silvia Gomez Coca serves as a Lecturer in the Department of Inorganic and Organic Chemistry at the University of Barcelona's Faculty of Chemistry, where she integrates computational and experimental methodologies to advance molecular science. Her academic foundation includes degrees from the University of Alicante and University of Barcelona.

Her educational pathway comprises:

• Bachelor of Chemistry, University of Alicante (2007)
• Master's Degree in Chemistry, University of Barcelona (2010)
• PhD in Chemistry, University of Barcelona (2013)

Research expertise spans molecular magnetism, supramolecular chemistry, and AI-driven materials design, with emphasis on single-molecule magnets for quantum computing applications. Her interdisciplinary approach bridges inorganic synthesis, theoretical modeling, and machine learning to engineer functional molecular systems.

Recent publications (2015-2025) reveal a strategic evolution toward computational innovation, particularly in spin dynamics modeling and neural network applications for crystal structure prediction. This trajectory demonstrates increasing integration of artificial intelligence with traditional quantum chemistry methods.

Key scientific recognitions include:

• FPU Fellow (Spanish Government PhD Grant, 2009-2013)
• Beatriu de Pinós Fellow (Catalan Government Postdoctoral Grant, 2018-2021)
• Juan de la Cierva Fellowship (Spanish Government Postdoctoral Grant)

As Principal Investigator for AGAUR's 'Encapsulation of Mononuclear Single Molecule Magnets' (2018-2021) and co-PI for multiple national grants including 'AI for Quantum and Neuromorphic Magnetic Devices' (2022-2024), she directs significant research funding. Current projects focus on radical-metal magnetic coupling for digital technologies (2024-2026) and theoretical chemistry infrastructure development.

She actively contributes to the Electronic Structure Group (GEE) and Institute of Theoretical and Computational Chemistry (IQTC), fostering collaborative research in quantum materials design and computational methodology development.