Date and Time: July 27th and July 28th. from 1-5 pm ET each day.

Registration: Sign up here

The rapid development of LLMs has dramatically transformed the nature of software development work, allowing researchers and software engineers to produce sometimes genuinely high-quality code at an extraordinary rate. It has also introduced a new set of risks. Misbehaving agents have destroyed entire repositories, deleted production databases, and exposed confidential information to the world. Developers have vibe-coded complex software projects in record time, only to realize that that their code is a buggy and unmaintainable nightmare. Students and early career professionals regularly struggle with both the fear that they have been hopelessly superseded by LLMs, and the reality that they have not been trained in the high-level project management skills that are essential for properly guiding AI agents.

The MolSSI is now extending its world-renowned Best Practices Workshops to the domain of AI-assisted software development. In this hands-on event, we’ll offer sober, battle-tested advice for utilizing AI agents in a maximally productive way.

Topics will include:

• containerized agent execution for security,
• requirements-based development workflows for establishing human intent,
• strong static and LLM-based tools for code analysis,
• strategies for resolving the technical debt that will threaten to accumulate when producing code at the rate of an LLM.

Individuals at all levels of education / career are welcome, but basic prior programming experience and familiarity with Git is recommended.  No prior experience with AI or LLMs is required.  Some demonstrations will utilize agentic AI (especially Claude Code).  Attendees with access to Claude Code or similar agentic AI services (such as Codex, OpenCode, etc.) will be able to follow along more effectively, but access to these services is not required for attendance.

Sign up to reserve a spot today!

Shehan Parmar, a Ph.D. candidate in chemistry at Georgia Tech and a 2025–26 MolSSI Software Fellow, builds simulation and software infrastructure for ionic liquid discovery, a class of liquids whose tunable chemistry makes them candidates for spacecraft propellants, battery electrolytes, and energy-efficient desalination. Under the guidance of Prof. Jesse McDaniel, with prior support from the DOE Computational Science Graduate Fellowship, he develops the polarizable force fields, software, and AI/ML pipelines needed to let engineers select across an otherwise impossibly large space of candidate chemistries.

In some ways, Shehan came into molecular science by coincidence. He earned a B.S. in Astronautical Engineering at USC and an M.S. in Aerospace Engineering at UCLA, where his first research project was on how propellants behave under high-magnitude electric fields, an experimental electrospray thruster problem funded by the Air Force Research Lab and NASA JPL. His fascination with space quickly turned into a realization: to understand propellants, one must understand chemistry. That’s when his passion grew for understanding and answering questions through first principles. In 2022 he switched universities and majors mid-PhD to do exactly that, joining Prof. Jesse McDaniel’s group at Georgia Tech.

At Georgia Tech, Shehan’s research has anchored on the molecular behavior of ionic liquid mixtures: their structure, their transport properties, their phase behavior. His published work includes an Editor’s Choice paper on HEHN-based ionic liquid mixtures (J. Phys. Chem. B 2023) and a collaboration with Oak Ridge National Lab on a tetraalkylammonium TFSI ionic liquid (J. Phys. Chem. B 2024).

Through the MolSSI Software Fellowship, Shehan has been bringing two historically separate domains together: ionic liquid science and high-throughput materials discovery. Working with his mentor, Dr. Samuel Ellis, he has been integrating libraries across disciplines and following best software practices to package and deploy reproducible, provenance-aware, and modular high-throughput workflows. One outcome is pympfit, his open-source multipole-based partial-charge fitting library, now refactored as a modular package collaborators can extend. The fellowship complements his previous role as a visiting graduate student in Prof. Kristin Persson‘s group at Lawrence Berkeley National Lab, where he contributed a small feature to atomate2, the Materials Project’s JobFlow- and FireWorks-based workflow framework, helping route classical MD jobs for ionic liquids and electrolytes into dataset-generation pipelines that will eventually feed into machine learning force fields. It is the kind of cross-domain work MolSSI has uniquely supported.

Beyond graduate school, Shehan is eager to push the frontier of materials discovery and to explore how far simulation and AI can go in expanding the chemistries engineers can draw from when designing the next generation of materials. Some of his most fascinating moments in grad school have been the times his molecular dynamics simulations matched independent experimental measurements. Working with talented experimental collaborators to compute properties that turn out to agree, sometimes strikingly well, with what the lab measures has been the clearest signal he could ask for about how promising computational chemistry really is. Whether in industry, a startup, or an academic role, he hopes to keep solving these kinds of problems.

Outside the lab, Shehan teaches yoga in Midtown, Atlanta, trains at his CrossFit gym Team Octopus, and listens to Better by Khalid on repeat for advanced programming focus.

If you’d like to follow Shehan’s work, you can find him on GitHub, LinkedIn, Google Scholar, and his personal website.

From a young age, Ms. Trine K. Quady has been intrigued by the world of chemistry. Her fascination began at just eight years old, when she blew out a candle and was mesmerized by the wispy tendrils of smoke swirling through the air. Curious about the behavior of particles, she asked her big sister if it would be possible to predict their movements if one could manipulate the air in the room. Later learning the complexity of this challenge, it foreshadowed her desire to understand the physical world through the lenses of math and chemistry and life long passion for scientific exploration.

Trine credits much of her intellectual growth to the exceptional teachers and mentors she encountered throughout her educational journey. Their encouragement pushed her to challenge herself both as a student and a researcher. Now, as a graduate student at UC Berkeley and a 2025-26 MolSSI Software Fellow, she has had the opportunity to work alongside her mentor, Dr. Taylor Barnes, who has played a pivotal role in her development as a software engineer. Under his guidance, Trine has learned to dissect large software goals into manageable tasks. Their discussions often start with high-level concepts like parallelization and then dive deep into the intricate details of hardware-level operations. This mentorship has broadened her exposure to various software tools, significantly enhancing her skills and knowledge.

The MolSSI Software Fellowship has proven to be a transformative experience for Trine, enriching her understanding of software sciences. The collaborative environment allows her to engage in meaningful conversations with her fellow software fellows, often starting from familiar ground and evolving into comprehensive discussions about software practices and hardware specifics. This supportive community has made tackling research challenges feel more achievable, providing invaluable guidance along the way.

Trine’s current research focuses on the implementation and acceleration of a local second-order Møller-Plesset Perturbation theory (MP2) code. Unlike traditional implementations, her local-MP2 code is tailored to calculate energy differences between two molecules, specifically targeting energy variations during conformational changes. By optimizing the correlation treatment in similar regions of the geometries, she aims to achieve rapid, difference-matched local MP2 energy calculations. While the theoretical speed-up is promising, the practical implementation poses significant software challenges, underscoring the vital role of the MolSSI fellowship in her journey.

Looking ahead, Trine aspires to become a professor of theoretical chemistry at a research-intensive university. She is passionate about both research and mentoring, believing that academia is the perfect setting for this blend. One of her greatest hopes is to teach a graduate-level quantum mechanics course, a subject that profoundly shaped her own academic path.

When she’s not immersed in the world of chemistry, Trine enjoys indulging her culinary skills, particularly in making “a mean croissant.” She is also an avid runner, having completed her first marathon last October and she plans to tackle the Taco Bell 50k this coming October. Professionally, she’ll complete her PhD and pursue a post-doctoral position.

Follow Trine’s journey and explore her work as she continues to make strides in the field of theoretical chemistry!

Check out her GitHub if you are interested in more of her work

Weiliang Luo’s interest in molecular science started early, sparked by a periodic table tucked into the back of a dictionary. What began as curiosity—wondering how matter is arranged and transformed—quickly deepened through hands-on exposure to simple experiments and vivid chemical reactions that felt almost magical.

Today, that curiosity drives his research at MIT, where he works at the intersection of quantum chemistry, machine learning, and scientific software. His primary focus is Enerzyme, a project centered on developing next-generation neural network potential (NNP) methods for enzymatic catalysis. The goal is to make AI-driven molecular simulations more accessible, robust, and scalable for complex biochemical systems. To do this, he combines modular, physics-inspired model design with large-scale quantum chemistry, transfer learning from biochemical data, and automated active learning workflows with uncertainty quantification.

Building Enerzyme has meant tackling both scientific and engineering challenges. Early versions of the framework were powerful but difficult to maintain, relying on fragile scripts and inefficient GPU usage. Through his MolSSI Software Fellowship, Weiliang worked with mentors—Drs. Benjamin Pritchard, Taylor Barnes, and Jessica Nash—to redesign the system from the ground up. By implementing best practices for multi-GPU PyTorch jobs on Slurm clusters and restructuring key workflows, he significantly improved performance and stability. He also reworked the NEB pipeline to better handle convergence issues, turning it into a more automated and reliable process.

These improvements went beyond speed. Standardizing data structures and configuration handling reduced user error and improved reproducibility, helping transform Enerzyme from a personal research tool into shared infrastructure used across his group.

The longer-term vision for Enerzyme is to become an open, extensible platform—accessible to experimental biochemists, flexible for developers, and aligned with rapid advances in atomistic machine learning. By accelerating simulations of enzyme catalysis, particularly in challenging systems like metalloenzymes, Weiliang hopes to enable new insights into complex biochemical processes. Ultimately, this work could support mechanistic studies, enzyme engineering for sustainable chemistry, and rational drug discovery.

The MolSSI fellowship has also given him the freedom to explore ambitious ideas while connecting with a broader community of computational scientists. These interactions have strengthened his interest in building software that is not only technically sound, but widely useful.

Looking ahead, he is open to paths in academia, industry, or startups, with a consistent goal: advancing molecular science through better computational tools. He is also motivated to mentor others as AI continues to reshape the field.

Outside the lab, Weiliang channels his creativity into the arts. He directs large-scale cultural performances and experiments with incorporating generative AI into storytelling and design. He also enjoys singing and arranging a cappella music, drawn to the same collaborative energy that defines his scientific work.

If you are interested in more of his work, please visit his GitHub and LinkedIn pages

• GitHub
• LinkedIn
• X

The Accelerating Curricular Transformation in the Computational Molecular Sciences (ACT-CMS) program is pleased to announce that the 2026 round of Faculty Fellowship applications is now open.

ACT-CMS is an education and faculty development program from MolSSI. The goal of ACT-CMS is to transform science curricula by accelerating the integration of programming and computation into existing molecular science courses through faculty training and the development of open and reusable curricular modules.

The Faculty Fellows Program is the cornerstone of ACT-CMS. A Faculty Fellowship lasts one years and is awarded to a molecular science educator developing curricula integrating programming and computation into existing science courses. Throughout the program, Faculty Fellows will receive curriculum development and assessment training and will upskill their programming and computational skills. They will produce a curriculum module that uses programming and computation to teach STEM concepts in an existing course that they teach. These modules will be open and reusable, allowing other educators to adopt them in their classrooms.

Applications for the next cohort of Faculty Fellows open in January 2026 and close on February 28, 2026. A Faculty Fellowship has the following benefits:

• $5000 support (stipend + travel funding) stipend each year of the fellowship.
• Mentorship from an ACT-CMS Programming and Computation Mentor and an ACT-CMS Curriculum Mentor.
• Annual meeting at MolSSI headquarters for training and networking.

Application Link: Click Here.

If you are interested in applying and have questions, please register for information session Here. The information session will be held on January 30, 2026, at 1:00 PM ET (10:00 AM PT)

For more information about the ACT-CMS Fellowship Program, please visit the ACT-CMS website.

The Molecular Sciences Software Institute (MolSSI) is proud to announce the release of a new Udemy course: C++ Project Management: CMake, CPack, and Beyond, created by MolSSI Software Scientist Taylor Barnes.

Becoming an expert C++ software engineer requires much more than writing code. Successful developers also need to navigate the complex world of compilers, build systems, package managers, containerization tools, and debuggers that power serious scientific software. This course provides a natural next step for students and researchers who know the basics of C++ and are ready to tackle real-world project management.

🎓 Upon completion, you’ll receive a shareable credential to highlight your training on LinkedIn or your CV.

Special Offer: Just $12.99!

Use coupon code C63D76238F8CA8A16BCF at checkout to get the course for only $12.99
👉 Enroll here: C++ Project Management: CMake, CPack, and Beyond

Offer valid until Oct 4, 2025.

What You’ll Learn

This hands-on course dives into the practical aspects of building, distributing, and maintaining C++ software:

• CMake: Configure builds, compile executables and libraries, and manage dependencies.
• CPack: Create distribution-ready packages for your projects.
• Debian packaging: Understand package structures and run your own test APT server.
• Debugging tools: Use sanitizers and language servers to improve project reliability.
• Container-native development: Explore Docker-based workflows that can streamline and modernize your development environment.

The course also addresses advanced CMake concepts such as variable substitution, scope handling, and creating packages that are easy for others to consume.

Course Format

All lessons are recorded inside a Docker development environment, with support for both Neovim and VSCode. This setup ensures participants can follow along step by step while experiencing the benefits of container-native development.

Who Should Enroll?

• Students with a basic foundation in C++ who want to take the next step.
• Developers interested in contributing to real-world scientific projects.
• Coders looking to create and distribute libraries for others to use.

Prerequisites: Basic familiarity with C++ (conditions, loops, simple classes) and some experience with Git are recommended.