I am currently a Doctoral researcher at the Max Planck Institute for Dynamics of Complex Technical Systems in the Process Systems Engineering group led by Prof. Kai Sundmacher. Here, I mainly investigate how Graph Neural Networks can help us create better models for phase equilibria of mixtures. Also, I research the use of Graph Neural Networks for thermophysical and environmental molecular property prediction and the application of machine learning methods for process optimization. The overall goal of this is to develop more sustainable separation processes. I received my M.Sc. in Advanced Chemical Engineering with Process Systems Engineering from Imperial College London in August 2019, and my B.Eng. in Chemical Engineering from UNAM in June 2018.

I can be reached at sanchez [at] mpi-magdeburg [dot] mpg [dot] de.

News

• 30-05-2023 - I attended PASI 2023 at Buenos Aires, Argentina.
• 12-05-2023 - I gave a talk regarding hybrid GNNs at the meeting of the DECHEMA expert group “Model-based process development and optimization” at Frankfurt, Germany.
• 02-05-2023 - Our work on the Gibbs-Helmholtz Graph Neural Network was published in Digital Discovery!
• 25-04-2023 - My Masters student Kunchapu successfully defended his thesis on GNNs for prediction of polymer solutions!
• 13-03-2023 - I presented a poster on the 14th IMPRS ProEng workshop regarding the prediction of activity coefficients at infinite dilution using hybrid GNNs in Naumburg, Germany.
• 15-11-2022 - I presented a poster on the AIChE Annual Meeting 2022 regarding hybrid GNNs for predicting infinite dilution activity coefficients.
• 30-08-2022 - I gave a talk on the 13th IMPRS workshop in Wernigerode, Germany regarding how to use GNNs for predicting infinite dilution activity coefficients.
• 13-06-2022 - Our recent work on GNNs for the prediction of infinite dilution activity coefficients was featured on the front cover of Digital Discovery!
• 23-02-2022 - Our paper on Graph Neural Networks for the prediction of infinite dilution activity coefficients was accepted at Digital Discovery. Check out the paper here.

Last updated 10-07-2023.