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"