The three primary activities in theoretical and computational chemistry are development of new theory, implementation of methods as reliable software, and application of such methods to a host of challenges in chemical and related sciences. The MSc aims to train new research students to be able to deliver these outcomes.
The MSc consists of a set of training modules and a short project. The compulsory core modules are:
- Quantum Mechanics
- Statistical Mechanics
- Introduction to Programming
- Methods of computer simulation
- Electronic structure theory
- Software Development.
You will also select a number of optional courses (currently five), which may include:
- Applied Computational Chemistry
- Biomolecular Simulation
- Mathematics II
- Quantum Mechanics in Condensed Phases
- Intermolecular Potentials
- Chemical Informatics
- Reaction Dynamics
- Advanced Quantum Mechanics
- Advanced Statistical Mechanics.
Each module consists of several lectures/classes and a piece of assessed coursework.
In addition, you will also be required to undertake one short project with an allocated supervisor. This typically takes a few weeks in either the Easter or Summer vacations. A list of possible supervisors and projects will be provided to select a topic from.
The allocation of graduate supervision for this course is the responsibility of the Department of Chemistry and it is not always possible to accommodate the preferences of incoming graduate students to work with a particular member of staff. Under exceptional circumstances a supervisor may be found outside the Department of Chemistry.
Assessments are spread out over the academic year.
Each module is assessed by a piece of coursework or a test.
The assessment of the short project will be based on a report that you will submit.
The number of students on this course is so small that statistics are not meaningful. Many students go on to further academic study, while others use the skills they have gained in a wide variety of destinations. The department runs a number of activities in close cooperation with the Careers Service, including an annual careers conference, CV workshops and visits from many employers. The course also has strong engagement with industrial partners.
Changes to this course and your supervision
The University will seek to deliver this course in accordance with the description set out in this course page. However, there may be situations in which it is desirable or necessary for the University to make changes in course provision, either before or after registration. The safety of students, staff and visitors is paramount and major changes to delivery or services may have to be made in circumstances of a pandemic, epidemic or local health emergency. In addition, in certain circumstances, for example due to visa difficulties or because the health needs of students cannot be met, it may be necessary to make adjustments to course requirements for international study.
Where possible your academic supervisor will not change for the duration of your course. However, it may be necessary to assign a new academic supervisor during the course of study or before registration for reasons which might include illness, sabbatical leave, parental leave or change in employment.
Computational chemistry is a fascinating and rapidly evolving field that sits at the intersection of chemistry, physics, and computer science. It’s an area of study that focuses on using computer simulations and mathematical models to gain a deeper understanding of chemical processes, molecular structures, and interactions. However, many aspiring students and researchers often wonder, “Is computational chemistry hard?” In this blog, we will delve into the complexities of computational chemistry, discuss the path to becoming a computational chemist, explore the idea of majoring in computational math, and touch upon the pursuit of a Ph.D. in computational chemistry.
Is Computational Chemistry Hard?
The difficulty of computational chemistry depends on one’s background, aptitude, and the level of complexity in the research one undertakes. Like many scientific disciplines, it can be challenging, but it is also immensely rewarding. Here are some factors to consider when evaluating whether computational chemistry is hard:
- Strong Mathematical Foundation: Computational chemistry relies heavily on mathematical principles, including calculus, linear algebra, and differential equations. If you have a strong foundation in math, it can significantly ease the learning curve.
- Proficiency in Programming: To work in computational chemistry, you’ll need to be comfortable with programming languages such as Python, C++, or Fortran. Learning these languages can be challenging, but they are essential for building and running simulations.
- In-Depth Understanding of Chemistry: You’ll need a solid grasp of chemical principles, quantum mechanics, and molecular modeling to be successful in computational chemistry. This understanding can be acquired through coursework and practical experience.
- Problem-Solving Skills: The ability to think critically and solve complex problems is crucial in computational chemistry. You’ll often be tasked with troubleshooting, optimizing algorithms, and interpreting data.
- Continuous Learning: Computational chemistry is a field that requires constant learning and adaptation due to the rapid advancements in technology and methodology.
How to Become a Computational Chemist
Becoming a computational chemist involves a series of steps:
- Educational Foundation: Start with a strong educational foundation by earning a bachelor’s degree in chemistry, physics, or a related field. Alongside core courses, consider taking additional classes in mathematics and computer science to build a well-rounded skill set.
- Master’s or Ph.D. Program: To specialize in computational chemistry, pursue a graduate degree. A master’s degree can provide you with the foundational knowledge, while a Ph.D. will allow you to conduct in-depth research and contribute to the field.
- Gain Practical Experience: Participate in research projects, internships, or laboratory work to gain practical experience. This will help you develop your skills and expand your knowledge.
- Develop Programming Skills: Learn programming languages such as Python, C++, or Fortran. These skills are essential for creating and running computational simulations.
- Stay Informed: Stay up to date with the latest developments in computational chemistry by reading research papers, attending conferences, and joining professional organizations.
Is Computational Math a Good Major?
Computational math, or applied mathematics, can be a valuable major for those interested in computational chemistry. A strong mathematical background is crucial in this field, as it forms the basis for the development and execution of computational simulations. Majoring in computational math can provide you with the mathematical expertise needed to excel in computational chemistry.
Ph.D. in Computational Chemistry
Earning a Ph.D. in computational chemistry is a significant achievement for those who want to make substantial contributions to the field. A Ph.D. program typically involves conducting original research, publishing papers, and defending a dissertation. This level of education can open doors to high-level research positions, academia, and specialized roles in the industry. However, it is an arduous and time-consuming process that demands dedication and a passion for scientific exploration.
Computational chemistry is undoubtedly a challenging field, but it offers a world of opportunities for those who are willing to invest the time and effort to master its complexities. If you have a passion for chemistry, mathematics, and problem-solving, and you’re not afraid of hard work, a career in computational chemistry can be both intellectually stimulating and professionally rewarding. Whether you’re considering it as a major or pursuing a Ph.D., the journey to becoming a computational chemist is filled with exciting discoveries and the potential to impact various industries, from pharmaceuticals to materials science.