Research

I am a theoretical condensed matter physicist and material scientist, that specializes in computational - physics/materials science/chemistry/engineering, and many-body theory. In other words, I study methods for solving the Schrödinger's or Dirac's equation, machine learning equations, among others. These methods include development of computational algorithms and their mathematical properties.

My research focus is on the development of theoretical and computational techniques for Quantum Simulations, Machine Learning and Multiscale simulations to study energy, processes and materials, especially structural, electronic, transport and optical properties, low-dimensional systems, and nanostructures.

Our objective is to form renaissance scientists and engineers. In a similar fashion, we attack problems with a renaissance approach, where we combine several areas of knowledge, independently of their formal separation by the human mind.

Current research interests: Quantum and Atomistic Simulations, Machine Learning, Condensed Matter Theory, Theoretical Chemistry, Computational Material Sciences, Scientific Computing, Quantum Computing Algorithms, Energy/Materials.

Our expertise is in quantum simulations, 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.

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

Work performed in my group have addressed physical and chemical properties of 2D-materials, porous materials, catalysts, nanotubes, polymers, catalytic/metallic/magnetic clusters and molecular machines. The problems we study are studied require developing or expanding established methods related to: Multiscale - Multiparadigm simulations: Quantum Mechanics (DFT, CCSD), Atomistic Simulations (MD, Force Field development, ReaxFF, Coarse grained FF), Statistical Mechanics and Computational Engineering (Chemical Engineering and Materials Sci.), Big Data and Machine Learning. Thus, we create and combine many approaches from diverse areas including engineering, basic sciences.

Some of the recent projects include:

[2015-2020]

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:

[2010-2015]

• Electronic Structure Development

• Molecular Dynamics Development

• Force Field Development

• Artificial Photosynthesis

• Energy Storage

• Fuel Cells

• Light Capture

• Bioaplications (Artificial Enzymes & Artificial bone scaffolds)

• Topology in Solid State Physics/Chemistry

• high energy materials/molecules

• prediction of crystal packings

• materiainformatics

• cheminformatics

• statistical mechanics

• photocatalysts/photovoltaics