Pimpernel Science, Software and Information Technoglogy &
Honorary Professor of Physics,
University of Edinburgh, Uk
Research Overview
Dr. Martyna's research is centered on a synthesis of hardware, software and physics-based methods acting to create novel scientific insight and new patentable technology from theory and modeling for societal impact. In his career, he has written large parallel software applications (PINY, OpenAtom) based on physics-based methods (RESPA, EES-NL, QDO, ...) and used them to invent novel devices (PET), understand the science underlying technology, and to investigate long standing scientific challenges such as the metal- insulator transition in metal-ammonia solutions and rare-earth chalcogenides, the mechanism underlying high temperature superconductivity in the cuprates and the properties of water. Recently he has developed strong activity in cognitive science and machine learning. Blue skies projects combined with risk management has led to success which will now be leveraged by his startup company Pimpernel, Science, Software and Information Technology dedicated to creating novel Intellectual Property from Science followed by realization as products.
RESEARCH INTERESTS
Metal-Insulator Transitions:
Rare earth chalogenides, Metal ammonia solution.
Graphene-based solar cell TCE:
Graphene nanomeshes, Grapahene/CNT hybrids.
Fine-grained Parallel Electronic Structure Software
OpenAtom - highly parallel DFT/CPAIMD/GW app.
PINY_QDO - Electronically coarse-graining w path
integral solutions in strong coupling for biophysics.
Physics-Based Methods:
Advanced statistical sampling, Reduced order methods for e-structure.
Electronic coarse-graining and strong coupling solutions to reveal
new and emergent physics in soft-condensed matter systems.
Electronic Devices:
Piezoelectronic Transistor, Graphene devices.
High Temperature Supercoductors
Fluctuating Bond Model - nonlinear coupling and charge order.
Soft Condensed Matter - Antimicrobials and aqueous solutions
Translational biophysics - modeling, theory and experiment combine in biology to study new and emergent physics and discover novel antimicrobial peptides.
Cognitive Science and Machine Learning:
Advanced kernel methods for antimicrobial discovery. Combining neuroscience, computer science and physics for novel cognitive paradigms instantiated on large parallel architectures.