Early Steps in Computational Science

Dr. Carlos Bueno’s journey into computational biophysics started by helping his father to automate scheduling tasks in Excel. This early exposure to programming sparked a lasting interest, evolving into writing simple programs in Visual Basic, such as a titration calculator in Visual Basic, where he found that “even simple chemical models hide multiple complexities”. His academic path then took him to Dr. Mirko Zimic’s bioinformatics lab in Peru, where he navigated at the interface between biology, chemistry, physics, and programming. Whether setting up computing clusters or diving into biophysical modeling for tuberculosis research and poultry vaccine development, Carlos’s work was deeply informed by the proximity and insights gained from Dr. Patricia Sheen’s experimental lab, whose lab benches were next to their computers. This closeness between the labs allowed him to bridge computational and experimental perspectives.

Building Open-Source Tools and Leadership

At Rice University, Carlos joined Professor Peter Wolynes’ group at the Center for Theoretical Biological Physics, focusing on mesoscale biophysical models of complex protein interactions. His research evolved through close collaborations with faculty across disciplines, including Dr. Margaret Cheung, Dr. M. Neal Waxham, Dr. José Onuchic, which enriched his perspective on both the biological relevance and computational demands of his work. Within this collaborative environment he faced the challenge of managing and developing software that organically developed in a fast-paced academic environment, such as multiple versions of the code coexisting without version control or insufficient documentation. This led him to the MolSSI best practices, a turning point in his approach to scientific software.

“The MolSSI best practices completely reshaped how I approached scientific software,” Carlos reflects. He began implementing version control, unit testing, continuous integration, and thorough documentation, contributing to the development of OpenAWSEM in collaboration with Dr. Nick Schafer and Dr. Wei Lu for protein modeling and Open3SPN2 for DNA simulations in OpenMM.

The MolSSI Software Fellowship, alongside the mentorship of Dr. Jessica Nash, proved instrumental in Carlos’s growth as a software leader. He states, “Working with Jessica has helped me tackle problems leading open-source collaborative software projects with large teams, collaborating effectively with the bigger scientific computing ecosystem, and adopting more effective coding styles and design patterns to ensure our software tools can be easily adapted to tackle a vast range of problems.” This guidance, combined with insights from other mentors like Levi, who emphasized long-term software design, solidified his confidence in leading projects, particularly in prioritizing requirements and refining communication.

Carlos highlights specific areas where Jessica’s mentorship was invaluable: “Her feedback has helped me think more deliberately about design patterns and how to structure codebases, so they are easier to extend and maintain. She has also introduced me to valuable resources and best practices that I now apply regularly in my projects for testing and benchmarking. We have also had discussions about licensing, community contributions, and interoperability with other tools like MDTraj, and MDAnalysis. She has help us to develop new methods and interfaces for the Frustratometer project, making it more flexible and accessible.”

A Vision for Science and Mentorship

Carlos’s vision extends beyond immediate projects. He aims to remain at the intersection of theory and experiment, bridging biology, chemistry, and physics through computational modeling. His immediate goal is to expand the AWSEM ecosystem into a robust, well-documented platform for future collaborators.

Beyond his research, Carlos is passionate about mentorship and science education, contributing to programs like Science Clubs Peru, iGEM, the Serendipity Program, and the Frontiers in Science Program. He is particularly proud of his open-source contributions, OpenAWSEM and Open3SPN2, which are now being used and extended by others.

Above all, Carlos finds joy in spending time with his son, seeing the world through his eyes and reminding himself of the constant learning and growth that defines human experience.

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

2024-25 MolSSI Software Fellow Yiwen Wang did not initially envision a PhD in chemistry, having started her academic journey in the realm of physics. However, being surrounded by friends immersed in chemistry—many of whom later became computational chemists—exposed her to a world where physics, chemistry, biology, and computer science seamlessly intersect in fascinating ways. Daily discussions ranging from quantum chemistry to molecular dynamics ignited her interest in molecular science, ultimately leading her to pursue a Ph.D. in computational and theoretical chemistry. 

Her current research focuses on advancing constrained nuclear electronic orbital (CNEO) theory, a framework that incorporates nuclear quantum effects (NQEs) into electronic structure methods. As part of the Prof. Y. Yang group, Yiwen is developing constrained multicomponent time-dependent density functional theory (CNEO-TDDFT) to extend CNEO methodologies from ground-state to excited-state calculations. By integrating NQEs—particularly quantum nuclear delocalization effects—into an effective potential energy surface (PES), her work aims to provide a more accurate description of excited-state dynamics, an area where conventional electronic structure methods often fall short. 

The Challenge of Scientific Software Development 

Implementing these advanced methodologies in real-world software is no small feat. Under mentorship of MolSSI Software Scientist Dr. Sina Mostafanejad, Yiwen has been working on incorporating the CNEO framework into NWChem and PySCF, two open-source computational chemistry packages written in Fortran and Python, respectively. Despite having no prior experience with Fortran, she embraced the challenge head-on and made substantial contributions to NWChem. She has successfully implemented CNEO ground-state energy and its analytic gradients, as well as CNEO excited-state energy and its analytic gradients. While further refinements remain—such as integrating the framework into NWChem’s workflow, optimizing code structure, and finalizing the compilation process—she anticipates that by the end of her MolSSI Fellowship, the full CNEO functionalities will be implemented and ready for broader use. This will enable researchers studying systems with significant NQEs to leverage CNEO-based methods in their calculations. 

A Defining Accomplishment: Turning 40 Pages of Theory into Code 

Among her many achievements, one stands out as a particularly defining moment. The successful execution of her code for calculating analytic gradients was the culmination of months of work and theoretical derivations spanning over 40 pages. Seeing those equations come to life in functional computational code was an incredibly rewarding experience—one that still brings her a deep sense of satisfaction. 

Beyond Research: A Love for Cooking, Baking, and Nature 

While Yiwen’s research keeps her engaged in the world of molecular science, she finds balance in everyday joys. She has a hidden talent for cooking and baking, with a particular fondness for making chocolate or blueberry bagels on weekends. Long walks along the lakeshore of the University of Wisconsin campus provide moments of reflection and connection, often accompanied by phone calls with family and friends. 

Looking Ahead: Future Goals and Aspirations 

As she continues her Ph.D. journey, Yiwen remains focused on exploring new chemistry problems, conducting impactful research, and ultimately completing her doctoral studies. Whether in academia or industry, her passion lies in developing computational tools that enhance our understanding of molecular systems, with potential applications in drug design, materials science, and beyond. 

Through a unique blend of theoretical insight, software development, and interdisciplinary collaboration, Yiwen Wang is making strides in computational chemistry—bringing molecular science and computing together to push the boundaries of what’s possible. 

For more about her work, visit her GitHub or LinkedIn

Ruby Manderna, a Ph.D. student in the Department of Chemistry and Nanoscale Science at UNC Charlotte, is working at the forefront of computational quantum chemistry. Under the guidance of Dr. Jay Foley, her research focuses on developing ab initio methods for polaritonic chemistry—a cutting-edge field that explores light-matter interactions at the quantum level. By refining computational approaches, Ruby aims to create more accurate and efficient tools to study these complex systems, pushing the boundaries of what is possible in molecular modeling.

A Passion Ignited in Nanoscience

Ruby’s journey into molecular sciences began during her master’s thesis, where she explored the green synthesis of silver nanoparticles and their diverse applications. What started as a routine lab task quickly turned into a captivating journey of discovery. She found herself experimenting with various parameters—tweaking concentrations, adjusting reaction times, and refining techniques—all in pursuit of optimizing the nanoparticle properties. By the end of the project, Ruby was not only proud of the results she had achieved but also deeply inspired to pursue a career dedicated to unraveling the mysteries of molecular sciences. This experience ignited a passion within her for research, leading to an eagerness to explore further innovations in nanotechnology and its potential to address pressing global challenges.

The Impact of Mentorship and the MolSSI Fellowship

Developing advanced computational methods requires strong coding skills, and MolSSI Software Scientist Dr. Taylor Barnes has played a crucial role in helping Ruby refine her programming expertise. Through clear explanations of complex concepts, thoughtful resource suggestions, detailed code reviews, and invaluable feedback, Dr. Barnes has significantly strengthened her confidence in tackling complex computational problems.

Ruby’s growth as a researcher has also been greatly supported by her MolSSI Software Fellowship, an opportunity that has enhanced her technical skills, expanded opportunities for engaging in meaningful discussions and network with people from different fields, and provided the resources to advance her work in polaritonic chemistry. This experience has fueled her drive to develop robust software tools that will contribute meaningfully to the field by building robust and impactful tools for polaritonic Chemistry. This transformative journey has not only deepened Ruby’s understanding of complex concepts but also instilled in her a sense of confidence to tackle challenges and innovate within her discipline. As she continues to embrace these opportunities, she is excited to explore new collaborations and push the boundaries of what is possible in her research endeavors.

Looking to the Future

With a deep passion for research, Ruby envisions a long-term career as a research scientist in a national lab or industry, working on innovative challenges in quantum chemistry and quantum computing. In the immediate future, she is dedicated to developing a software tool for ab initio molecular dynamics, specifically tailored for polaritonic systems. This project, which blends her interests in physics, computational chemistry, and software development, aims to provide researchers with a more effective way to simulate and analyze complex light-matter interactions. By successfully completing her current software development project, Ruby plans to make a significant contribution to both the scientific community and the field of polaritonic chemistry. She’s particularly excited about the potential of this tool to deepen our understanding of light-matter interactions at the quantum level, unlocking new possibilities for exploring and manipulating these complex systems.

Beyond the Lab

Despite her dedication to science, Ruby firmly believes in maintaining a healthy work-life balance. She’s happiest going on walks, enjoying the outdoors, spending time with friends and family, and unwinding with a good science fiction or comedy series. Exploring new trails and immersing herself in captivating stories allows her to recharge and find inspiration for her creative pursuits. Whether it’s the thrill of discovering a hidden path or laughing at clever dialogue, these moments provide a perfect balance to her routine and fuels her imagination.

Celebrating Achievements and Embracing the Future

Among Ruby’s proudest accomplishments are earning admission to her master’s program after excelling in a highly competitive exam and being awarded the MolSSI Software Fellowship. These milestones have reinforced her belief in hard work and perseverance, motivating her to continue striving for excellence in her field.

As she looks ahead to the next few years, Ruby is excited about the opportunities that lie ahead—whether in academia, industry, or research institutions. With her expertise, passion, and drive to innovate, she is well on her way to making a lasting impact in the world of computational quantum chemistry.

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

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 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. 

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 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 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