Session List

Climate change is a serious problem for humanity, and the high-performance computing sector has its own part to play in its alleviation[1]. Yet in addition to this challenge, the socio-economic push for hardware growth has its own scientific issues, particularly regarding the nature of the scientific method, Ockham’s Razor and the nature of understanding. I will present three biophysical simulation techniques developed as part of my own research which showcase alternate, bespoke ways of thinking about the physics at work in novel biological systems. Fluctuating Finite Element Analysis (FFEA) models large, globular biological systems as continuum mechanical systems[2,3]. BioNet models hierarchical protein networks using experimentally relevant building block[4,5]. Finally, our mechano-kinetic model utilises bespoke energetic functions to modify Markov state models and couple long- and short- timescale biological processes together[6]. I will discuss why each of these techniques is justified in leaving out specific types of fine-detail, thus saving computational power, and conclude with a perspective on the responsibility of computational scientists to favour the creation of smart methods over a reliance on hardware improvements.

[1] Frost, J.M. (2017). https://jarvist.github.io/post/2017-06-18-carbon-cost-of-high-performance-computing/

[2] Solernou, A., Hanson, B.S. et al. (2018). PLoS Comp. Biol. 14(3), 1-29

[3] Hanson, B.S. et al. (2021). Methods. 185, 39-48

[4] Hanson, B.S. et al. (2019). Soft Matter. 15(43), 8778-8789

[5] Hanson, B.S. et al. (2020). Macromolecules. 53(17), 7335-7345

[6] Hanson, B.S. et al. (2022). bioRxiv. https://doi.org/10.1101/2020.11.17.386524

The main aim of our research is to understand and interpret the vibrational spectra of a large range of molecular crystals. As such, we use a range of solid-state density functional packages including Castep, VASP, Crystal and CP2K along with additional phonon calculation tools such as Phonopy to determine the vibrational dynamics of these materials. In the majority of cases these calculations determine both the phonon frequencies and Born charges which can be used to determine the IR intensity of the vibrational modes. However, comparing these to the experimental spectra, particularly at low frequencies can be non-trivial because the experimental spectra can be influenced by multiple other parameters including the nature of the sample (powder or single crystal), the particle size and shape along with the measurement geometry. As such we have developed the python post-processing tool PDielec [1] which can be used to understand and visualise phonon calculations. As well as acting as a general python parser for a number of density functional packages, this tool provides a QT-5 based GUI that allows the visualisation of structures and vibrational motion, and a number of tools to understand differences and improve correlation between calculated and experimental spectra.

[1] https://doi.org/10.5281/zenodo.5888313

The Leeds Analytics Secure Environment for Research (LASER) is a University of Leeds (UoL) service hosted in the Leeds Institute of Data Analytics (LIDA) on Microsoft Azure.

LASER offers the combination of meeting the highest standards of security for data analytics, ensuring ISO27001 and NHS Data Security and Protection Toolkit compliance with the flexibility to enable constant agility in design and function; alongside scalability depending on the researcher need.

LASER is the platform upon which we can build and host Virtual Research Environments (VREs). In their simplest form, a VRE is a virtualised environment consisting of virtual machines and shared storage where data flow is strictly controlled. Taking a 'walled garden' approach, there is no access to the internet or other networks from inside a VRE.

The LASER Platform has been designed with and for researchers and includes the following capabilities:

• Fully flexible and scalable to enable researchers to align spend to research requirements.
• Agile and quick to provision, to support a range of research user cases.
• Access to the latest tools and capabilities such as machine learning to support researchers.

There is a growing interest in the use of chatbots in Universities, as they can provide efficient and timely services to students and educators; eg see UoL Strategy 2020-2030

The EDUBOTS project, funded by Erasmus+, explored best practices and innovative uses of university chatbots. We implemented case study chatbots using platforms HUBERT for student feedback, and DIFFER for student interaction and fostering a sense of belonging. Our case studies and surveys provided feedback from students and educators regarding the possible uses of chatbots in higher education (HE). We present our key findings:

Educators and students agreed the importance of chatbots for

Responding to FAQs that relate to administration, eg admissions, IT helpdesk, Student Success service;

Conducting tests and quizzes with students to assess their conceptual understanding of a topic;

Feedback from students to the instructor for course evaluation.

Students were much keener than educators on "offering personalised feedback to students on their conceptual understanding of a topic" and "offering tutorials to students related to courses". Social aspects were not very popular with the educators group. On the other hand, more students wanted a chatbot that can facilitate communication with their mentors and establish study groups within their courses.