Aims

Contemporary physical chemistry makes important contributions to all areas of science. This module draws on topics around a theme of chemistry far from equilibrium, influenced by current research within SNES (Chemistry). We will demonstrate and contextualise the relevance of physical chemistry towards climate change, the environment, healthcare, stars and interstellar space, and in furthering our understanding of chemical structure and processes.

This module, which sets out to avoid mathematics, will highlight recent advances in four separate topics that each connect with important contemporary questions about chemical structure and dynamics; (A) Time-resolved Spectroscopy, (B) Astrochemistry, (C) Advanced Crystallography and (D) Machine Learning Interatomic Potentials. Attention is given to modern aspects of the field and to the applications of research in these topics. Emphasis will be given to practical techniques without recourse to detailed mathematical approaches.

Time-resolved Spectroscopy (Section A) will describe the experimental methods available for probing ultra-fast (femtosecond) chemical reactions and the motivations of these experiments. It will describe how researchers construct “molecular movies” that precisely follow the re-arrangement of molecular structures during chemical change. Such changes are understood and interpreted through quantum mechanics.

Astrochemistry (Section B) will explore the chemistry of the interstellar medium, interstellar clouds and circumstellar shells. The fundamental role of chemistry in the evolution of the universe will be discussed. The course examines both the physical chemistry that shapes the chemistry of the universe and the observational methods that allow its observation.

Advanced Crystallography (Section C) will discuss processes of crystal growth and the different methods used to obtain crystalline materials. This leads naturally to an advanced knowledge of the various analytical methods available to characterise crystalline materials. Attention will then be given to understanding some important relationships between the crystal structure of a solid and its physical or chemical properties. Examples of advanced materials will be used where the relationship between structure and function becomes evident.

Machine learning interatomic potentials (Section D) will delve into the novel and rapidly growing field of molecular modelling using machine learning methods. Specifically, the course will give you an overview of the state-of-the-art approaches for creating machine learning force fields which mimic Ab Initio accuracy at a fraction of the cost. In doing so machine learning interatomic potentials are bridging the gap between theoretical predictions and experimental measurements across multiple length and time scales. These methods are increasingly indispensable from any molecular modeler’s toolbox and are driving discovery in Physical Chemistry and Material Science.

Outline Of Syllabus

Time-resolved Spectroscopy
Introduction to ultrafast time-resolved spectroscopy for probing fundamental chemical dynamics; the pump-probe technique, time-resolved vibrational spectroscopy, time-resolved X-ray techniques, including X-ray free electron lasers and new advances in attosecond spectroscopy for probing electron dynamics.

Astrochemistry
The chemistry of the early universe; observational techniques; laboratory experiments; detection and characterisation of interstellar and circumstellar molecules; molecular dynamics in the interstellar medium; cosmic dust and interstellar chemistry

Advanced Crystallography
Review of analytical and synthetic methods in solid state sciences; the crystalline state; crystal engineering; powder diffraction and small angle X-ray scattering; in situ microscopy of crystalline matter

Machine Learning Interatomic Potentials
General introduction to machine learning methods; methods for constructing atomic representations or descriptors; linear models and kernel models; neural network potentials; beyond root-mean-square errors and molecular dynamics stability; training workflows, error quantification and active learning

Teaching Rationale And Relationship

Students acquire knowledge and understanding through taught material and small group tutorials and by the way of research articles available through literature searches on the web. Students will have opportunity to talk to specialists about the experimental protocols central to the module. Research articles will be discussed both formally and informally and used to illustrate the formal lectures. Students will be directed to key websites where additional information can be found in a more user-friendly manner than advanced textbooks.

Student will independently research their essay topic for the assessment and will have prepared for online or in person discussion with lecturers.

Tutorials will be online discussion delivered through a combination of online chats, small group discussion of 3-4 students, and discussion boards.

Assessment Rationale And Relationship

The student will complete a presentation consisting of one topic relevant to the course. The topic will be informed by student selection and assigned by the module leader. This task will require the student to read broadly around the topic explored during the course, draw connections to related themes and report on the outcomes of modern research. The student will be marked on the depth of their knowledge and critical understanding of the subject explored.

Formative assessment will aid students' progression in the module.

Study Abroad students may request to take their assessment before the semester 1 exam period, in which case the format of the assessment may differ from that shown in the MOF. Study Abroad students should contact the School to discuss this.