Previous Software Fellows
Victor Chavez
Purdue University
2020-2021 Software Fellow
Adviser: Prof. Adam Wasserman
MolSSI Software Mentor: Dr. Taylor Barnes
“Development and implementation of Partition DFT"
Yunsie Chung
Massachusetts Institute of Technology
2021-A Seed Fellow
Adviser: Prof. William H. Green
MolSSI Software Mentor:
“Development of an automatic liquid phase kinetic modeling tool with solvation-corrected kinetic rates and thermochemistry”
Jennifer Clark
North Carolina State University
2019-2021 Software Fellow
Adviser: Prof. Erik Santiso
MolSSI Software Mentor: Dr. Andrew Abi-Mansour
“Developing the first open-source application for thermodynamic calculations for the Statistical Associating Fluid Theory equation of state and associated coarse-graining method"
Ryan J. DiRisio
University of Washington
2020 Software Fellow
Adviser: Prof. Anne B. McCoy
MolSSI Software Mentor: Dr. Jonathan Moussa
“Development of an open source, general-purpose Diffusion Monte Carlo software suited for high-performance computing environments to study vibrational problems"
Emiliano Deustua
Michigan State University
2018-2020 Software Fellow
Adviser: Prof. Piotr Piecuch
MolSSI Software Mentor: Dr. Johnathan Moussa
“Coupled-cluster software using moment expansions and stochastic wave function sampling"
Bradley Dice
University of Michigan
2019-2020 Software Fellow
Adviser: Prof. Sharon C. Glotzer
MolSSI Software Mentor: Dr. Doaa Altarawy
“Developing powerful, parallel analysis tools for studies of soft matter, nanoparticles, and colloidal self-assembly
Sebastian Dick
Stony Brook University
2019-2021 Software Fellow
Adviser: Prof. Marivi Fernandez-Serra
MolSSI Software Mentor: Dr. Sam Ellis
(a) “Improving electronic structure calculations with the help of machine learning”; and (b) ”Development of an open source library for machine learned density functionals"
Cody Drisko
University of Notre Dame
Adviser: Prof. J. Daniel Gezelter
MolSSI Software Mentor:
“Increasing the accessibility of reverse non-equilibrium molecular dynamics (RNEMD) algorithms for the efficient calculation of select transport properties”
Chenru Duan
Massachusetts Institute of Technology
Adviser: Prof. Heather Kulik
MolSSI Software Mentor:
“Integrating machine learning in the process of quantum chemistry calculations to improve the efficiency and accuracy for high throughput computation, one of the most vital steps for fruitful materials discovery.”
Heta A. Gandhi
University of Rochester
2020-2021 Software Fellow
Adviser: Prof. Andrew D. White
MolSSI Software Mentor: Dr. Jessica A. Nash
“Extending machine learning software to coarse-grained molecular dynamics"