Dr Francisco Martin-Martinez is a Senior Lecturer in Chemistry and Natural Sciences in the Department of Chemistry at King’s College London.

Fran began his academic journey at University of Granada in Spain, where he studied Chemical Engineering and earned a PhD in Theoretical and Computational Chemistry. Following his PhD, he held a postdoctoral position with the Quantum Chemistry Group at the Vrije Universiteit Brussel. He then moved to the Massachusetts Institute of Technology (MIT), where he worked as a Research Scientist under Prof. Markus Buehler for almost 6 years before starting a lecturer position at the Chemistry Department of Swansea University in 2020.

In 2024, Fran transitioned to King’s College London, where he leads the MMLab on nature-learned matter.

In 2022, he was selected as a Google Cloud Research Innovator. In addition to his primary academic roles, Fran is a co-instructor at Station1, a start-up focused on socially driven innovation based in the USA.

Research Interests

• Computational chemistry and multiscale modelling
• Nature-inspired materials and biomimicry
• Biomass and biobased materials
• Precision agriculture and soil remediation
• Self-healing infrastructure materials

Teaching

• Advanced Topics in Physical and Computational Chemistry
• Natural Sciences Experimental Skills
• Research Methods Literature Reviews
• MSCi Research Project & Dissertation
• MRES Research Project in Interdisciplinary Chemistry

Research profile

For more information on Dr Martinez's research please see his Research Portal page

The Martin-Martinez Group

The Martin-Martinez Group integrates computational chemistry and biomimicry, with the objective of developing more sustainable molecules and materials. Learning Nature’s intelligence to engineer materials for circularity as well as functionality, their research finds applications in diverse fields that are pivotal to our sustainable development, such as energy harvesting and storage, self-healing infrastructure, and precision agriculture. They employ a variety of tools such as Density Functional Theory (DFT), Molecular Dynamics (MD) simulations, and Coarse-Grained (CG) modelling to simulate the behaviour of molecules and materials across different scales, from the nanoscale to the mesoscale. They simulate a range of phenomena, including chemical reactivity, electron transport, mechanical properties, self-assembly and degradation mechanisms. The extensive datasets generated from their computational work serve as valuable resources for training machine learning (ML) models that expedite the prediction of properties and facilitate the discovery of more sustainable molecules and materials.

Visit the Martin-Martinez Group website to find out more.