Jeff Kodosky received a BS degree in Physics from Rensselaer in 1970. He is Co‐founder, Director, and Fellow at National Instruments, a leading developer and manufacturer of integrated software and hardware for engineers and scientists. LabVIEW, cocreated by Kodosky, is the industry‐standard graphical programming environment for measurement and automation.  Jeff is a Trustee of Rensselaer since 2002. Gail is a native of Troy, NY and is a retired administrative assistant. Gail and Jeff live in Austin, TX and have two daughters and four grandchildren.

2019

Mar
27
2019
Material informatics is a new initiative which has attracted a lot of attention in recent scientific research. The basic strategy is to construct comprehensive data sets and use machine learning to solve a wide variety of problems in material design and discovery. In pursuit of this goal, a key element is the quality and completeness of the databases used. Recent advance in the development of crystal structure prediction algorithms has made it a complementary and more efficient approach to explore the structure/phase space in materials using computers. In this talk, we discuss the importance of the structural motifs and motif-networks in crystal structure predictions. Correspondingly, powerful methods are developed to improve the sampling of the low-energy structure landscape.
Darrin Communication Center (DCC) 337 4:00 pm

2018

Apr
11
2018
"The Fascinating Quantum World of Two-dimensional Materials: Symmetry, Interaction and Topological Effects"

Symmetry, interaction and topological effects, as well as environmental screening, dominate many of the quantum properties of reduced-dimensional systems and nanostructures. These effects often lead to manifestation of counter-intuitive concepts and phenomena that may not be so prominent or have not been seen in bulk materials.  In this talk, I present some fascinating physical phenomena discovered in recent studies of atomically thin two-dimensional (2D) materials.  A number of highly interesting and unexpected behaviors have been found – e.g., strongly bound excitons (electron-hole pairs) with unusual energy level structures and new topology-dictated optical selection rules, massless excitons, tunable magnetism and plasmonic properties, electron supercollimation, novel topological phases, etc. – adding to the promise of these 2D materials for exploration of new science and valuable applications. 

Darrin Communications Center (DCC) 337 4:00 pm

2017

Mar
1
2017
Density Functional Theory for Materials Discovery: Systematic Approximation and the SCAN Functional

Discovery of useful or interesting new materials and molecules by computation requires an efficient, accurate, and reliable theoretical method, and the preferred method is still Kohn-Sham density functional theory[1]. In this theory, the exact ground-state energy and electron density (and thus the nuclear positions) can be found by solving self-consistent one-electron equations. The exchange-correlation energy as a functional of the electron density must in practice be approximated. I will discuss a systematic and proven way to improve the approximations, making them more accurate and reliable at a modest increase of computational cost. Then I will show how this approach has led to SCAN [2], a strongly-constrained and appropriately normed functional that, without being fitted to any bonded system, makes accurate predictions for diversely-bonded materials and molecules [3].


[1] W. Kohn and L.J. Sham, Self-Consistent Equations Including Exchange and Correlation Effects, Phys. Rev. 140, A1133 (1965).
[2] J. Sun, A. Ruzsinszky, and J.P. Perdew, Strongly Constrained and Appropriately Normed Semi-local Density Functional, Phys. Rev. Lett. 115, 036402 (2015).
[3] J. Sun, et al., Accurate First-Principles Structures and Energies of Diversely-Bonded Systems from an Efficient Density Functional, Nat. Chem. 8, 831 (2016).

DCC 324 4:00 pm
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2015

2014

Dec
3
2014

2013

Sep
18
2013
Marvin L. Cohen, University of California, Berkeley

Apr
17
2013

2011

Oct
19
2011
Mildred Dresselhaus, Massachusetts Institute of Technology