About MSSE


MSSE is a remotely delivered, 1-year, full-time program that prepares students for careers in computational science, data science, machine learning, and software engineering. The program is designed to train students with backgrounds in chemistry, physics, biology, computer science, or from other physical science disciplines.

While the MSSE degree focuses on the molecular sciences, the skills it provides are suitable for any student pursuing careers both in science and or non-science-based industries that require advanced machine learning, complex mathematical modeling and simulations, software engineering, or high-performance computing. The MSSE program launches with a two-week bootcamp on the foundations of programming and software engineering. This bootcamp kicks off an educational journey that students experience together as a cohort. The two-semesters of online coursework include a capstone project with an industrial or government lab partner.



The program combines the immense intellectual strengths of the College of Chemistry and the College of Engineering to provide courses created by world experts and faculty committed to the training of the next generation of leaders. The MSSE provides students with the tools, software engineering practices, plus leadership, management, and entrepreneurial skills needed to create or lead science- or engineering-based enterprises. 

One of the central missions of MSSE is to develop a workforce that is highly prepared to work in a multidisciplinary environment and build software infrastructures. Another central mission is to develop a diverse workforce in fields that have traditionally had low participation of women, ethnic minorities, and other underrepresented groups.



Computational Science, also known as scientific computing, is a discipline that harnesses computers and mathematical modeling to understand and solve complex problems in science and engineering. The applications can range in size from the interaction of individual atoms to the behavior of weather systems and galaxies.

At its heart Computational Science involves the use of:

• Science
• Software Engineering
• Machine Learning
• Data Science
• Simulation and Modeling
• High-Performance Computing.

Computational Science allows us to model and simulate natural and artificial systems. It is particularly important when those systems are too expensive, complex, or are otherwise inaccessible to actual experiments.

The exponential improvements in the processing capacity of today’s computers, and a greater support of multi-processor and parallel architectures, have greatly enhanced our ability to simulate large and complex systems. When combined with advanced machine learning techniques such as Deep Neural Networks, these simulations can be used for rapid discovery, experimentation, optimization, and prototyping in multiple fields ranging from chemistry, biology, physics, and the material sciences. These same techniques can also be applied in the social sciences and in non-scientific fields that can benefit from the simulation of complex systems such as finance, retail, supply chain, networks, and marketing.


Research into quantum mechanics in the early twentieth century allowed scientists to gain greater insights into the fundamental nature of matter at the atomic level and led to the emergence of the cross-disciplinary field of molecular science. Molecular science unifies the fields of chemistry, physics, biology, and the material sciences.

Molecular science seeks to explore the properties and interactions of molecules at atomic, molecular, supramolecular, and system levels to develop new materials and useful interactions that solve real-world issues such as disease, world hunger, renewable energy, and environmental problems.

Molecular science has become an important discipline across many of the natural sciences including physical sciences, such as chemistry, geology, physics, and astronomy, and biological sciences, such as bio-chemistry, microbiology, and genetics as well as the environmental sciences. Disciplines that apply molecular science include:

• Biology, chemistry, and physics
• Pharmaceuticals
• Biotechnology
• Materials and chemical engineering
• Bioinformatics
• Quantum chemistry
• Nanotechnology
• Bio-molecular engineering
• Environmental sciences
• Medical science
• Neurobiology
• Technology and engineering
• Cosmetics
• Agriculture and forestry
• Forensics



Computational molecular science combines software engineering and theoretical molecular science to model, analyze, and simulate molecular structures, properties, and interactions. The field uses specialized hardware and software, algorithm design, and large-scale data management to perform algorithmically complex, data-intensive modeling and analysis tasks.

Since its beginning in the early 1970s the field has grown rapidly to become an essential tool in industrial and academic research for solving problems that are too complex or are otherwise intractable or inaccessible to traditional methods of experimental methods. In these cases, computational molecular science can produce valuable qualitative and quantitative insights. For example, in the biological sciences computational biochemists today routinely use computational molecular science to study protein folding and biochemical pathways. In the physical sciences it is used for research into nanotechnology and new materials.

As computational power continues to rapidly grow rapidly and as more advanced software tools and techniques become available the importance of computational molecular science to industrial and academic research will continue to grow.