MolSSI’s 2022 Software Fellows


Edan Bainglass

University of North Texas

Adviser: Prof. Oliviero Andreussi

MolSSI Software Mentor:

“Development of Python modules for continuum-embedding, integrated quantum-embedding, and multi-scale simulation automation and optimization”

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.”

Aria Hosseini

Massachusetts Institute of Technology

Adviser: Profs. Giuseppe Romano and Keith A. Nelson

MolSSI Software Mentor:

“Development of a package, called OpenKapitza, will wrap around molecular dynamics and density functional theory codes to compute heat transport across inhomogeneous interfaces”

Heejune Park

University of California, Davis

Adviser: Prof. Lee-Ping Wang

MolSSI Software Mentor:

“Development of reaction path finding and optimization methods and implementation into the QCArchive Infrastructure”

Linqing Peng

California Institute of Technology

Adviser: Prof. Garnet K. Chan

MolSSI Software Mentor:

“Creation of a general, open-sourced implementation of density matrix embedding framework to predict correlated properties with both high accuracy and affordable cost”

Isabela Quintela Matos

Cornell University

Adviser: Prof. Fernando Escobedo

MolSSI Software Mentor:

“Development of Python modules for continuum-embedding, integrated “Development of free-energy-based extrapolation methods to optimize Hamiltonian’s parameters that stabilize sought-after mesophases in nanoparticle systems”

Dominic Rufa

Weill Cornell Graduate School of Medical Sciences

Adviser: Prof. John D. Chodera

MolSSI Software Mentor:

“Development of Python modules for continuum-embedding, integrated “Development of hybrid machine learning/molecular mechanics-based simulation and free energy calculation software for accurate molecular property predictions”