Research team
Javier E. ALFONSO-RAMOS
Javier E. ALFONSO-RAMOS
Javier E. ALFONSO-RAMOS
Javier received his B.Sc. in Chemistry from the University of Havana (Cuba) in 2022. He is currently a PhD student at ChimieParistech - PSL University. His research is focused on combining machine learning methods and density functional theory calculations to accelerate the exploration and analysis of chemical reactivity.
Bowen DENG
Javier E. ALFONSO-RAMOS
Javier E. ALFONSO-RAMOS
Bowen received his M.Sc. in Materials and chemical engineering from Guangxi University (China) in 2023. Since early 2024, he is a PhD student at ChimieParistech - PSL University. His research is focused on building innovative deep learning network architectures for rapid thermodynamic and kinetic screening of reaction pathways in chemical reaction networks.
Maxime FERRER
Javier E. ALFONSO-RAMOS
Basile PARMELLI
Maxime received his Ph.D in Theoretical Chemistry and Computational Modelling from the Autonomous University in Madrid in 2024. Currently a Postdoctoral researcher, his expertise lies in chemical reactivity, with a focus on systems able to capture carbon dioxide. His work integrates deep learning to automate and accelerate the exploration of the chemical reaction space.
Basile PARMELLI
Javier E. ALFONSO-RAMOS
Basile PARMELLI
Basile obtained his M.Sc. in Molecular Chemistry from ENS-PSL University in 2024. He joined ChimieParistech for his Master internship, which was focused on the use of machine learning to accelerate the development of metal complexes with anti-cancer applications. Subsequently, he started a PhD, in co-supervision with Gilles Gasser and Kevin Cariou, on the same topic.
Emilien CHEN
Bhavesh Gnnanapareddy
Emilien CHEN
Guanming obtained his M.Sc. in Systems Engineering from Beihang University and his engineering title from Ecole Centrale de Pékin in 2024. Since Nov. 2024, he is a PhD student at ChimieParistech - PSL University. His research focuses on molecular property prediction. More specifically, he explores the roughness of the molecular property landscape and its impact on predictive performance.
Armand Bonn
Bhavesh Gnnanapareddy
Emilien CHEN
Armand obtained his double BSc in Econometrics and Economics at Erasmus University of Rotterdam in 2024. He is currently studying an MSc in Machine Learning and AI at Sorbonne University and works as an AI Chemistry Engineer at ChimieParistech - PSL University. His research and work consist of developing an AI based software package that
Armand obtained his double BSc in Econometrics and Economics at Erasmus University of Rotterdam in 2024. He is currently studying an MSc in Machine Learning and AI at Sorbonne University and works as an AI Chemistry Engineer at ChimieParistech - PSL University. His research and work consist of developing an AI based software package that automates the complete experimental workflow of the plasma flow-reactor "MicroLab", developed by the 2PM team.
Bhavesh Gnnanapareddy
Bhavesh Gnnanapareddy
Bhavesh Gnnanapareddy
Bhavesh received his M.Sc. in Chemistry from the University of British Columbia, Canada, in 2025. Since late 2025, he has been a PhD student at ChimieParistech - PSL University. His research focuses on using machine-learned interatomic potentials for large-scale reactivity exploration in solution.
Maximilian Fleck
Bhavesh Gnnanapareddy
Bhavesh Gnnanapareddy
Maximilian received his PhD in chemical engineering from the University of Stuttgart in February 2021. In June 2025, he joined PSL University as a research engineer in support of the CHEMAI major research programme. Within the context this university-wide initiative, he aims to assist research groups in embracing AI and ML approaches, gui
Maximilian received his PhD in chemical engineering from the University of Stuttgart in February 2021. In June 2025, he joined PSL University as a research engineer in support of the CHEMAI major research programme. Within the context this university-wide initiative, he aims to assist research groups in embracing AI and ML approaches, guiding the various chemistry departments of PSL toward the forefront of the ongoing data revolution in chemistry.