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”