Computational

Our group designs hypothetical materials to help address energy and environmental challenges. We are interested in creating sophisticated nanostructures; potentially as complex (and useful) as molecular machines found in Nature. Our strategy is to computationally design and study new materials and then work work with our experimental collaborators to synthesize those materials in the lab. We are active software developers, and we build new computational tools to address problems nobody has tackled before.

Dr. Nicholas A. (Nick) Nystrom is the chief scientist of the Pittsburgh Supercomputing Center (PSC), a national computing center founded 1986 that is a joint effort of Carnegie Mellon University and the University of Pittsburgh. He joined PSC in 1992 as scientific programmer. He most recently served as interim director and senior research director. He has held the position of research physicist at Carnegie Mellon University since 2004. He received his Ph.D. in chemistry in 1992 from the University of Pittsburgh.

Dr. Nystorm is the architect, principal investigator (PI), and project director (PD) for “Bridges”, PSC’s flagship platform that was the first to successfully converge HPC, AI, and Big Data. He is also PI for the Data Exacell, a research pilot for enabling high performance data analytics on novel storage; co-PI for Open Compass, which brings emerging AI technologies to important problems in research; co-I for the Center for Causal Discovery, an NIH Big Data to Knowledge (BD2K) Center of Excellence; and co-I for Big Data for Better Health, which applies machine learning to lung and breast cancer research.

Dr. Nystorm's research interest includes data analytics, Big Data, causal modeling, graph algorithms, genomics, machine learning / deep learning, extreme scalability, hardware and software architecture, software engineering for HPC, performance modeling and prediction, impacts of programming models and languages on productivity and efficiency, information visualization, and quantum chemistry. Recent work has focused on enabling data-intensive research in domains new to HPC, scaling diverse computational science codes and workflows to extreme-scale systems, deep hierarchies of parallelism, advanced filesystems, and architectural innovations in processors and interconnects.

The overarching goals of our research is to develop and use multiscale simulation tools to understand, predict, and design novel materials for applications in energy conversion and storage, surfaces and interfaces, spectroscopy, and nanoparticles. Our group has extensive expertise in different levels of theories in computational materials design that span a wide range of accuracy levels and length scales, including force-field, density-functional theory, quantum Monte Carlo and quantum chemistry methods.

Specific Research Directions:

1. Solar Cells
2. Novel two Dimensional Materials: Graphene and Beyond
3. Innovative Materials for Electrocatalysis
4. Metal Oxidation
5. Surfaces and Interfaces
6. Ferroelectric Materials
7. Raman Spectroscopy
8. Van der Waals interactions

Our research interest is focused on developing analytical and computational multiscale techniques, and applying these techniques to engineering and biomedicine. Some current application topics include (i) response of charged defects to electric fields in solid oxides and ferroelectrics for energy applications; (ii) mechanics of drug transport across biological membranes; (iii) electron transport in deformed biomolecules under stress; (iv) response of nanostructured materials under dynamic loading for impact and blast protection; (v) composite functional materials for high-temperature sensors and actuators for hypersonic aircraft; (vi) quantum mechanical calculation of electromechanical properties of nanomaterials (graphene, nanotubes, chalcogenides)