The DPhil in Computational Discovery is a multidisciplinary programme spanning projects in Advanced Molecular Simulations, Machine Learning and Quantum Computing to develop new tools and methodologies for life sciences discovery.
This innovative course has been developed in close partnership between Oxford University and IBM Research. Each research project has been co-developed by Oxford academics working with IBM scientists. Students will have a named IBM supervisor/s and many opportunities for collaboration with IBM throughout the studentship.
The scientific focus of the programme is at the interface between Physical and Life Sciences. By bringing together advances in data and computing science with large complex sets of experimental data more realistic and predictive computational models can be developed. These new tools and methodologies for computational discovery can drive advances in our understanding of fundamental cellular biology and drug discovery. Projects will span the emerging fields of Advanced Molecular Simulations, Machine Learning and Quantum Computing addressing both fundamental questions in each of these fields as well as at their interfaces.
Students will benefit from the interdisciplinary nature of the course cohort as well as the close interactions with IBM Scientists.
Applicants who are offered places will be provided with a funding package that will include fees at the Home rate, a stipend at the standard Research Council rate (currently £17,668 pa) + £2,400 for four years.
There are 16 projects available and you may identify up to three projects to be considered for in your application. The details of Project 4 are listed below.
There is no application fee to apply to this course. For information on how to apply and entry requirements, please see DPhil in Computational Discovery | University of Oxford.
Project 4
Title: Effective Transport Coefficients in Extreme Dynamic Materials
PI: Gianluca Gregori
Characterizing and quantifying mass, momentum, and energy transport in materials under extreme conditions is vital in many areas of research, ranging from inertial confinement fusion to the behavior of matter in the interiors of giant planets and stars. With temperatures of a few electron volts (eV) and densities comparable to solids, warm dense matter (WDM) forms a key constituent of planetary interiors[1] as well as cooler stellar objects such as brown dwarfs[2] and the crust of neutron stars[3]. WDM is also produced during laser processing of solids and is an important transient state in all approaches to inertial confinement fusion[4] (ICF). Transport properties are difficult to model in WDM. Yet the direct measurement of transport properties and disentangling microscopic from macroscopic contributions remains notoriously elusive in these extreme states of matter[5]. Our goal here is to develop an experimental and numerical framework that can be used to measure effective transport in WDM and then use the experimental data to contruct a suitable representation via symbolic regression or a trained neural network[6].
Our proposed work utilizes recent advances in diagnostics and in machine learning. For the experiments, we intend to use X-ray photon correlation spectroscopy[7] (XPCS) in novel ways to extract effective transport coefficients in dynamic laser-driven materials. Recent developments in XPCS have demonstrated it as a powerful diagnostic at X-ray free electron laser facilities, enabling the tracking of atomic scale structure and dynamics with unprecedented spatio-temporal resolution. The experiments proposed here are a necessary first step toward an eventual goal to develop XPCS to measuring transport in dynamic non-equilibrium HED materials at different scales and provide first-of-the-kind estimates for viscosity and diffusivity under different state conditions and in the presence or absence of instabilities and turbulence. Here we want to propose a novel machine learning approach to address the complex micro-physics of material strength properties and to identify their emergent behaviour via closed mathematical expressions. This is done by using a Graph Neural Network[8] (GNN) to represent the discrete description of the underlying continuum system and then applying deep learning techniques to obtain a representation of the material properties as a function of the state variables (density, temperature, etc.) The latent representation learned by the GNN is then extracted with a symbolic regression analysis[9]. Our long-term goal is the development of augmented methods to ultimately improve the design and verification integrated modelling of WDM systems, in the sense that fluid simulations using these effective transport coeffcients may now be able to capture the relevant physical processes at all scales.
One of the student’s task will consist in participating in experiments at Free Electron Laser facilities and develop an experimental diagnostics able to extract transport coefficients such as mass diffusion, thermal conduction or viscosity. Once a sufficiently large database has been obtained, the student will then train the GNN and apply symbolic regression techniques in order to extract an effective representation of those transport coefficients. We expect this project to produce important results that are of interest not only to the fusion community, but the broader community of researchers working in high energy density physics, planetary science and extreme materials.
[1] Guillot, T., 1999: Science, 286 (5437), 72–77.
[2] Brown, C. R. D. et al., 2014: Sci. Rep., 4 (1), 521.
[3] Ichimaru, S., 1982: Rev. Mod. Phys., 54 (4), 1017–1059.
[4] Hurricane, O. A. et al., 2016: Nat. Phys., 12 (8), 800–806.
[5] Grabowski, P. et al., 2020: High Energy Density Physics, 37, 100 905.
[6] Miniati F. and Gregori, G.. 2022: Sci. Rep., 12, 11709.
[7] Sutton, M., 2008: Comptes Rendus Physique, 9 (5-6), 657–66.
[8] Battaglia P. W. et al., 2018: Arxiv:1806.01261v3 (2018).
[9] Udrescu, S.-M. and Tegmark, M., 2020: Sci. Adv. 6, eaay2631.
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