MolSSI Intro
The Molecular Sciences Software Institute
A nexus for science, education, and cooperation for the global computational molecular sciences community.
Projects
Software Infrastructure
Education
Education Initiative
Industrial Training
Industrial Training
Fellowship
Software Fellowship Program
Workshops
Community Workshops
previous arrow
next arrow
Education at the MolSSI

Education of students, post-docs, and faculty on programming and Best Practices in Software Development is a large part of MolSSI's mission. Our education program consists of our cohorts of Software Fellows, online training materials, and multiple workshops online and at various locations each year around the US.
Software Fellowships

Meet Our Fellows

The MolSSI continues to fund prestigious software fellowships that recognize advanced graduate students and postdocs pursuing the development of software infrastructure, middleware, and frameworks that will benefit the broader field of computational molecular sciences, including biomolecular and macromolecular simulation, quantum chemistry, and materials science.

Timotej Bernat, currently a PhD student at the University of Colorado Boulder under the direction of Prof. Michael Shirts, first became intrigued by computational chemistry during his undergraduate studies. After being introduced to organic chemistry, he found himself fascinated by the intricate, interconnected nature of the field. Once beyond the outward-facing veil of difficulty of the field, Tim began to see chemistry not just as a discipline of arbitrary rules, but as a powerful framework for understanding the patterns behind real-world molecular behavior. 

Tim’s passion for software development is not just theoretical. During his final undergraduate year, he undertook a 14-month internship at Sandia National Laboratories, where he developed both the graphical user interface (GUI) and backend systems for a software tool which automated defect analysis in silicon wafer devices. This software has had a tangible impact on semiconductor manufacturing, informed process improvements that enhanced device yield and accelerating the process of introducing process changes; to this day, the software remains in active use at Sandia’s silicon processing facility. 

Tim has been deeply involved in scientific computing, particularly in the development and distribution of molecular modeling software. He has recognized a critical gap in many chemistry-focused academic programs: while students are often taught to write code, there is little emphasis on writing good software—code that is maintainable, well-documented, and easily distributable. His tenure with the MolSSI as a Software Fellow has been instrumental in bridging this gap. Under the mentorship of Software Scientist Dr. Sam Ellis, he has gained hands-on expertise in packaging and distributing Python code, learning best practices that go beyond what is trained in most research labs. 

As a MolSSI Software Fellow and PhD candidate, Tim is developing methods and software which enable automated, high-throughput classical molecular dynamics (MD) simulations of organic polymer systems. Polymer design is central to problems in many active research areas including plastic upcycling in sustainability research, polymer-biopolymer compatibilization in biomedical engineering, and self-healing of dynamically covalent polymer networks in functional material design.  Systematic exploration of chemical and morphological design spaces through experiment alone is practically infeasible and requires assistance from computational structure-function models. Machine learning models are a popular choice for such models but are similarly constrained by lack of comprehensive experimental property datasets. However, both limitations can be overcome by physics-based molecular dynamics simulations, which are significantly cheaper to run than an experiment and more interpretable than a machine learning model. 

That said, no cohesive software ecosystem currently exists for representing and preparing arbitrary covalently-bound polymer systems for MD simulations. Tim’s research addresses this deficiency by designing systematic methods for constructing polymer systems and by developing software that makes these methods accessible to researchers in polymer-adjacent disciplines. His efforts have materialized into two collaborative polymer-related projects. 

The first, Polymer Inverse Design (PolyID), is a sustainability-focused collaboration with the National Renewable Energy Laboratory (NREL) aimed at discovering biomass-derived replacements for petroleum-based plastics. For this project, he has developed high-throughput workflows that generate polymer structure and MD simulation files for thousands of unique polymer preparations, requiring only monomer chemistry and polymerization mechanism information from a chemical database as input. These files are then used to run simulations, producing complete physical property datasets for training graph neural networks for thermophysical property inference. 

The second project, Multiscale Polymer Toolkit (MuPT), is an infrastructure-driven, multi-research group collaboration focused on developing software for representing polymers and co-molecules at the atomic, molecular, and nanoscale levels. The toolkit will provide APIs that interface with existing molecule-building, parameterization, and simulation tools. Tim’s contributions have centered on the development of the polymerist Python library, which, among many other packages developed and maintained by collaborators, will eventually be integrated into the MuPT ecosystem. 

Looking ahead, Tim plans to continue developing application-driven, open-source molecular software, either in a national research laboratory or in an industry setting where creativity and knowledge-sharing are valued. His MolSSI fellowship project, focused on distributing a multiscale polymer modeling software, is a step toward this goal. He hopes the software will become widely adopted in the field, fostering collaboration and innovation. 

Beyond research and software development, Tim finds pleasure in music. A lifelong musician, he has played the saxophone, bassoon, and piano, embracing both jazz and classical styles. He also enjoys unwinding with a good book, a pursuit that complements his scientific explorations. 

With a unique blend of chemistry, coding, and creativity, Tim is set to make meaningful contributions to both scientific software and the broader research community. If you are interested in more of Tim’s work, check out his GitHub page

The MolSSI is excited to announce our first-ever Software Fellow Alumni Career Panel, featuring former fellows who now work in industry positions across computational molecular science. Panelists from Nurix Therapeutics, SandboxAQ, QSimulate, and Schrödinger will discuss their career paths after completing their fellowships and share practical insights about working in the field. 

This virtual event is aimed at students and researchers interested in computational chemistry and molecular simulation careers outside academia. We also welcome any current professionals or former fellows who would like to attend and check-in! Panelists will address how undergraduates, graduate students, and post docs can prepare for careers in the chemical industry, discussing relevant skills, experiences, and educational paths that lead to success in computational molecular science careers and the day-to-day duties of their current role. 

Date and Time: April 1, 2025, 3:00 -4:00 PM ET (12:00 – 1:00 PM PT)

Location: Virtual Event

Registration Link:

Panelist Information 

dr-heta-gandhi
Dr. Heta Gandhi
Machine Learning Scientist
Nurix Therapeutics

2020 MolSSI Software Fellow

Dr. Heta Gandhi is a Machine Learning Scientist at Nurix Therapeutics, where she develops and integrates computational methods and machine learning techniques for small molecule drug discovery. Dr. Gandhi earned her PhD in Chemical Engineering from the University of Rochester under the mentorship of Dr. Andrew White. Her doctoral research focused on creating explainable artificial intelligence methods for interpreting molecular property prediction models and developing deep learning applications for cheminformatics and chemical engineering. Earlier in her graduate career, Dr. Gandhi worked on computational chemistry modeling and mixed reality technology for chemical education. In 2020, she received the MolSSI Software Investment Fellowship for extending machine learning techniques to coarse-grained molecular dynamics simulations. In addition to her research contributions, Dr. Gandhi is also an active peer reviewer for multiple prestigious journals and conferences. 


Dr. Mary Pitman
Staff Scientist
Sandbox AQ

2022 MolSSI Software Fellow

Mary Pitman, PhD, is a Staff Research Scientist at SandboxAQ, where she leads technological innovation in drug discovery and biologics. Dr. Pitman’s group develops new computational methods in antibody design, cell-scale AI models, and affinity prediction techniques for small molecules and biologics. Her background includes scientific software engineering for pharmaceutical and AI companies, academia, and the NIH. As a postdoctoral MolSSI software fellow, Dr. Pitman worked with David Mobley and discovered new AI and graph theoretic methods for optimal drug design. During her doctorate, Dr. Pitman studied under Garegin Papoian as an Integrative Cancer Research fellow at the NCI/NIH. There, she derived new theoretical frameworks to model how epigenetics drive cancer.  


Dr. Justin Provazza
Senior Scientist
QSimulate

2018 MolSSI Software Fellow

Dr. Justin Provazza obtained his PhD from Boston University under Professor David Coker in May 2020. His graduate research focused on developing and applying approximate quantum dynamics methods to simulate exciton transport and nonlinear spectroscopy in open quantum systems. During his PhD, Dr. Provazza was a MolSSI Software Development Fellow, working under the guidance of Dr. Benjamin Pritchard to develop high-performance computing libraries for molecular quantum dynamics and spectroscopy simulations. After graduation, he joined Professor Roel Tempelaar’s group as a postdoctoral fellow, researching molecular quantum transduction processes. Dr. Provazza joined QSimulate in June 2022.   


Dr. João Rodrigues
Principal Scientist
Schrödinger Inc.

2020 MolSSI Software Fellow

Dr. João Rodrigues is a Principal Scientist at Schrödinger Inc., where he leads the structure refinement team. He obtained his undergraduate degree in Biochemistry from the University of Coimbra in Portugal, where he first focused on bioinformatics and computational biology. He then trained with Alexandre Bonvin at Utrecht University and Michael Levitt at Stanford University, specializing in structural biology and computational methods for predicting the structure and dynamics of proteins and protein complexes. Since joining Schrödinger in 2021, Dr. Rodrigues has focused on improving small-molecule docking methods and, since 2024, has been leading efforts to blend experimental data with computational techniques to enhance 3D structural model quality.   

The journey into molecular science for Shuhang Li, a current PhD candidate at Emory University, began in high school where he was inspired by two extraordinary teachers: Mrs. Geng, a Chemistry teacher, and Mr. Du, a Physics teacher. Mrs. Geng had a unique talent for transforming abstract chemical concepts into relatable stories that made molecular interactions feel tangible. At the same time, Mr. Du’s lessons in physics revealed the fundamental forces that govern matter at every scale, from quantum interactions to celestial mechanics. Their passion and guidance made him appreciate the potential of natural science as a pursuit, and this curiosity led Shuhang to pursue deeper study at the College of Chemistry at Nankai University.

Bridging Mathematics, Physics, and Computing

During his tenure as a MolSSI Software Fellow, Shuhang has had the privilege of being mentored by Dr. Jonathan Moussa, a MolSSI Software Scientist with expertise spanning mathematics, physics, and computer science. His enthusiasm for problem-solving was both inspiring and transformative.  One pivotal moment in research came when Shuhang was struggling with optimizing software performance due to computational bottlenecks. Dr. Moussa introduced the concept of runtime code generation—an elegant solution where code is dynamically generated during execution rather than explicitly written and stored. Additionally, he suggested an approach to optimize tensor operations by strategically generating intermediate tensors, significantly enhancing computational efficiency. These insights revolutionized the approach to software development, demonstrating the power of mathematical intuition in crafting high-performance computational tools.

Current Research

As a MolSSI Software Fellow, Shuhang focuses on designing robust and efficient multireference electronic structure algorithms to simulate electronic excitation, ionization, and attachment processes in open-shell systems. These species, such as radicals and biradicals, play a crucial role in photochemical reactions and electron transfer, making their electronic structures essential for understanding chemical mechanisms.

A key aspect of this work involves developing NiuPy (GitHub link), a software that integrates wicked (GitHub link), an automated algebraic derivation tool, with a runtime code generator that streamlines tensor contractions. By automating labor-intensive steps, this framework eliminates manual implementation bottlenecks, enabling rapid prototyping for researchers working on multireference equation-of-motion (EOM) theories and quantum computing approaches.

As a Ph.D. student in the Evangelista Lab at Emory, Shuhang is also developing low-scaling multireference electronic structure algorithms to overcome computational bottlenecks when modeling large systems or active spaces. These efforts aim to extend the applicability of existing multireference renormalization group methods, making them viable for previously intractable problems.

The Future: Open Science and Academic Pursuits

Through the MolSSI Fellowship, Shuhang has gained transformative skills that bridge computational theory with real-world software development. Beyond coding, this experience has underscored the power of open-source software in accelerating discoveries across physics, chemistry, and materials science. The long-term goal is to remain in academia, contributing to the development of powerful computational tools that bridge disciplines and empower researchers worldwide.

Beyond Research: Cooking, Hiking, and a Dream of Tibet

While science is a driving force, Shuhang also enjoys exploring the hiking trails of North Georgia (and beyond!) whenever time permits. Cooking, particularly Cantonese cuisine, has also become a favorite pastime pursuit. 

A significant personal milestone for Shuhang is becoming the first Ph.D. in his family. Though the journey has been rigorous, it has also been deeply rewarding. Looking ahead, one personal dream remains—to travel to Tibet, a place of breathtaking landscapes, rich culture, and deep history. For now, however, the focus remains on pushing the boundaries of computational science. But beyond the equations, algorithms, and simulations, Shuhang Li remains committed to a broader vision—one where science is open, collaborative, and deeply connected to the world.

You can find more about him at LinkedIn | Github | X (former Twitter) @Li_Shuhang

Levi Petix’s path to molecular science was not initially planned. When he began graduate school, he was open to exploring various research avenues within chemical engineering. However, during the first semester, faculty members presented their research to recruit new students, and it was during this time that Levi discovered his passion for molecular science. Prof. Michael Howard’s presentation stood out, showcasing the importance of computational research in soft materials beyond just scientific curiosity. Levi was intrigued by how molecular science enables the rapid identification and screening of solutions to complex research challenges while minimizing time and costs associated with extensive experiments. Despite his deep dive into this field, he maintains an engineering mindset, always focused on practicality and application. He finds immense satisfaction in leveraging fundamental physics and computation to tackle difficult yet meaningful problems. 

Current Research 

Levi’s current research as a MolSSI Software Fellow focuses on multiscale inverse design, with an increasing emphasis on methods development. He’s particularly interested in leveraging computational techniques to bridge the gap between simulation and experiment. As he notes, material discovery has traditionally relied on experimental forward design, a process that involves selecting chemicals, conducting experiments, and characterizing the resulting structures. If the desired material properties are not achieved, researchers adjust experimental conditions and repeat the process—a costly, time-consuming, and labor-intensive cycle. However, advances in computational techniques are now enabling a more efficient approach: multiscale inverse design. 

As a Ph.D. candidate at Auburn University, Levi is at the forefront of this field, focusing on relative entropy minimization as a key technique for inverse design. Unlike black-box machine learning or iterative Boltzmann inversion, this approach allows for the direct incorporation of physically meaningful constraints, making experimental validation more feasible. Originally pioneered by Prof. Scott Shell (UCSB) and later by Prof. Tom Truskett (UT Austin), relative entropy minimization has been highly effective in designing coarse-grained models. However, past implementations primarily focused on unconstrained pair potentials, limiting direct experimental applicability. 

Through his MolSSI fellowship, Levi is expanding relentless, a computational framework designed to model and optimize materials. This expansion enables the application of relative entropy minimization to materials with bonds, such as polymers and metal-organic frameworks, making it a powerful tool for linking simulations with experimental synthesis. The overarching goal is to leverage high-performance computing (HPC) to rapidly screen material designs, eventually providing experimentalists with precise recommendations for synthesis. 

Advancements in Methodology and Software 

Levi has integrated surrogate modeling into the relative entropy framework, significantly accelerating the optimization of interaction parameters. In collaboration with Prof. Chris Kieslich (Georgia Tech), this approach has reduced the sequential nature of parameter optimization, enabling parallel training of surrogate models and shortening the time to solution. Their work was recently published in the Journal of Chemical Theory and Computation (doi.org/10.1021/acs.jctc.3c00651). 

In addition to surrogate modeling, Levi collaborates with Prof. Jeetain Mittal’s group (Texas A&M) on physics-informed inverse design, incorporating physical constraints directly into parameter optimization in relentless. This approach enhances experimental relevance and improves simulation efficiency. 

Additionally, a collaboration with Prof. Tom Truskett’s group (UT Austin) focuses on applying relentless to coarse-grain polymer systems, dramatically accelerating calculations. 

On the software development side, Levi is the lead developer of relentless and a co-developer of lammpsio, a package that simplifies LAMMPS data file creation and conversion to HOOMD-blue’s GSD format. Contributions have also been made to AZplugins, a widely used HOOMD-blue plugin maintained by Prof. Michael Howard and Prof. Antonia Statt (UIUC), and Jupyter notebook tutorials for HOOMD-blue’s Multiparticle Collision Dynamics (MPCD) module, in collaboration with Prof. Jeremy Palmer’s (University of Houston) group. 

Towards an Integrated Multiscale Workflow 

Beyond mesoscale modeling, Levi conducts atomistic simulations to investigate solvent-mediated interactions. With the ongoing expansion of relentless, the framework will soon enable coarse-graining of polymers from atomistic simulations, providing a direct connection between molecular-level interactions and mesoscale material properties. His long-term vision is to integrate relentless into a broader computational pipeline, where atomistic simulations inform mesoscale modeling, ultimately leading to materials that can be experimentally synthesized. By bridging computational predictions with laboratory realization, Levi aims to revolutionize materials design, making it more efficient, predictive, and experimentally relevant. 

The Influence of his Software Sciences Mentor 

Levi credits much of his development to his MolSSI software sciences mentor, Dr. Jing Chen, whose expertise in both polymer science and computer science has been invaluable. Her insights have played a crucial role in shaping relentless, a computational tool designed for efficient and transparent soft material design. With Jing’s guidance, Levi has been able to refine the software’s design and layout, ensuring its effectiveness and usability. As anyone in software development knows, code that runs smoothly on one computer often encounters issues on another, and Jing’s willingness to test and troubleshoot has been instrumental in overcoming these challenges. 

The Impact of the MolSSI Software Fellowship 

Being awarded the MolSSI Software Fellowship has been a transformative experience for Levi. The fellowship has provided him with the opportunity to expand relentless in ways that will greatly benefit the computational soft material research community. Previously, relentless was limited to designing materials that interacted through pairwise potentials without bonds. However, with the support of MolSSI, Levi has been able to incorporate molecular materials with bonds, opening up new research possibilities for addressing long-standing questions in his research group and the broader scientific community. 

Beyond the technical advancements, the fellowship has been a period of tremendous professional growth for Levi. His work with relentless has transitioned from minor bug fixes and small feature additions to a large-scale expansion, requiring careful planning, a well-structured development roadmap, and a strong focus on user interface, documentation, and testing. These experiences have enhanced his skills as a software developer and will be invaluable in his future. 

Future Career Aspirations 

Looking ahead, Levi envisions a career that bridges industry and academia. His immediate goal after graduate school is to work as a computational scientist in the industry, where he hopes to apply his 

expertise to real-world challenges. However, his long-term ambition is to return to academia as a professor of practice. Inspired by Professors of Practice David Carroll and Mike Gill at the University of Mississippi who shaped his development as an engineer and leader, Levi aspires to mentor and educate future generations of engineering students by combining his industrial experience with the teaching skills honed during graduate school. 

Unique Talents and Passions 

Outside of his academic and professional pursuits, Levi has an uncanny knack for securing incredible deals on event tickets. Whether it’s concerts or sporting events, he has managed to see artists like Noah Kahan, Lucy Dacus, Taylor Swift, and Fleetwood Mac up close for minimal cost. His most remarkable ticketing feat occurred during the AIChE 2022 conference in Phoenix, where he found center-stage tickets for an Elton John concert just two hours before the show for only thirty dollars each, allowing him and his lab mate to witness one of Elton’s final performances. 

Proud Accomplishments 

Among his many achievements, Levi considers receiving the MolSSI Software Fellowship his most significant academic accomplishment. The fellowship has provided him with the opportunity to collaborate with an exceptional group of researchers and developers, enhancing his skills and deepening his impact on computational molecular science. It has been an invaluable experience that continues to shape his career. 

Life Beyond Research 

When he’s not working, Levi is an avid sports fan. He dedicates much of his free time to following and attending games, particularly those of his alma mater, Ole Miss, as well as Auburn football and baseball. He also dreams of traveling to London to watch his favorite Premier League team, Tottenham Hotspur, play after completing his Ph.D. His love for sports and travel has led him to explore various stadiums, including Virginia Tech’s Lane Stadium and English Field during his visit to MolSSI, where he even had the unique opportunity to step inside Lane Stadium for the attached photo! 

Immediate Goals 

The upcoming years promise to be busy and exciting for Levi. He is preparing for a major personal milestone—his wedding in March—and is also focused on completing his Ph.D. by the spring of 2026. His immediate goals include publishing research papers using the enhancements made to relentless with MolSSI’s support, wrapping up other ongoing projects, and securing a job in industry. Balancing these ambitious endeavors while maintaining a healthy work-life balance is a priority, and Levi is determined to make the most of this critical phase of his journey. 

Find more about him on GitHub, LinkedIn and X (former Twitter @LeviPetix)

The Molecular Sciences Software Institute (MolSSI) and Intel invite you to attend our hands-on Basics of Accelerated Computing with Intel OpenMP GPU Offload virtual workshop on Mar. 7, 2025 from 13:00 – 17:00 ET.

The main focus of the course will be on the following topics:

  • A brief overview of OpenMP parallelization on CPUs
  • Offloading an OpenMP CPU-parallel code to GPUs
  • Managing data transfer between the host and the device within OpenMP framework
  • OpenMP GPU offload best practices
  • Overview of a real-life example in which Intel OpenMP helped community a code such as NWChem to perform compute- and data-intensive tasks more efficiently

The following prerequisites are recommended but not mandatory for attending the workshop:

  • Experience with OpenMP parallelization on CPUs
  • Basic familiarity with Bash as well as C, C++ or Fortran programming languages
  • Familiarity with Jupyter Lab environment

We have arranged a brief preparation session before the beginning of the workshop. The details of the preparation session can be found here.

Registration details:

You can stay up-to-date about the upcoming workshops by subscribing to our newsletter here or following the updates posted on our Industrial Training Program webpage.

The Molecular Sciences Software Institute (MolSSI) and Intel invite you to attend our hands-on Introduction to Intel® Tiber™ AI Cloud and oneAPI virtual workshop on Mar. 7, 2025 from 12:00 – 13:00 ET.

The main focus of the course will be on the following topics:

  • Intel Certified Instructor program
  • Introduction to Intel oneAPI ecosystem
  • Intel Tiber AI Cloud, registration and basic usage
  • Software, hardware and educational resources available on Tiber AI Cloud
  • Hands-on example: Running a conversational app in Jupyter Lab based on DeepSeek-R1 large language model

The following prerequisites are recommended but not mandatory for attending the workshop:

  • Experience with cloud environments (Oracle OCI, AWS, Microsoft Azure, Intel Tiber AI Cloud etc.)
  • Basic familiarity with Bash as well as Python programming language
  • Familiarity with Jupyter Lab environment

Registration details:

You can stay up-to-date about the upcoming workshops by subscribing to our newsletter here or following the updates posted on our Industrial Training Program webpage.

MolSSI Workshops

The MolSSI’s Software Workshop program is a community-driven effort in which researchers from academia, industry, and national labs propose timely and important topics focused on the software needs of the molecular sciences, and the MolSSI organizes or facilitates the event.