Research Interests

Jose L. Mendoza-Cortes

Sponsors: NSF-DMR, NSF-CHE, DOE, ONR, NVIDIA, Research Foundation, SCM, Caltech-SURF.

Our expertise is in Materials by Design, Multiscale Simulations, Big Data, Machine Learning and Quantum Computing Algorithms. Our research philosophy focuses on attacking problems in engineering and pure sciences and developing methods needed to solve them. These problems are studied by developing or using established methods related to: Multiscale - Multiparadigm simulations (from atoms to continuum), Quantum Mechanics (DFT, MP, CCSD), Atomistic Simulations (MD, Force Field development, ReaxFF, Coarse grained FF), Statistical Mechanics (Soft matter), Computational Engineering (Chemical and Mechanical Eng., and Materials), Big Data and Machine Learning. Thus, we combine and develop many techniques and approaches from diverse areas including engineering, basic sciences (material sciences, chemistry, physics), mathematics & computer science. Our work has been cited more than 4,004 times according to Google Scholar (March, 2018) with an average of 215 citations per paper.

Some of the recent projects include:
  • Machine Learning (Materials, Phase Transitions, Biomedicine, GPU computing)
  • Big Data (Bioinformatics, Cheminformatics)
  • Materials by Design (2D materials, 3D frameworks, Polymers, Microchips)
  • Highly Correlated Electrons (Parallel Computing, Scalable methods)
  • Artificial Photosynthesis Renewable Energy (Solar, Chemical, Electrical)
  • Energy Storage (Batteries, Fuel Cells, Artificial Photosynthesis)
  • Biomaterials (Biocompatible and Biomimetic)
  • Bioaplications (Drug delivery, Artificial enzymes)
  • Catalysis (organometallics, homogeneous and heterogeneous)
  • Electrochemistry (new materials and interfaces)
  • Crystallization Mechanisms (pharmaceuticals, high energy molecules)
  • Nanotechnology (Nanocrystals, Nanoparticles, Single Molecule Electronics)
  • Processes (Separation, devices)
In the past we have been dealing with particular problems of the phenomena mentioned above, some of these approaches have been published. the links below will explain briefly, what we have done and how we have done it:

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