PhD Studentship: Machine-learning Quantum Surrogate Models to Simulate Energy Transport Across Interfaces

Supervisors:

Reinhard Maurer (Chemistry), James Kermode (Engineering)

Summary:

Modern technologies such as photocatalysis or laser nanolithography involve energy transfer across interfaces. Many critical societal challenges require that we transfer light or electronic energy more efficiently into chemical energy, e.g., to utilize CO2 as renewable fuel. To achieve this, we need to understand the mechanisms behind the intricate dynamics that unfold at interfaces. Quantum mechanical simulations provide electronic-structure insights but are computationally intractable for relevant systems. The aim of this project is to create and apply machine learning models that emulate the quantum mechanical interaction of light, electrons, and atoms for many thousands of atoms at realistic interfaces.

Background:

Experimental evidence on ultrafast dynamics and transport at surfaces is notoriously hard to interpret on its own without knowledge of the nanoscale structure and electronic properties that unfold during the dynamics. First principles electronic structure methods are unable to address complex non-equilibrium dynamics at the scale of thousands to tens of thousands of atoms.

Machine learning (ML) methods are revolutionising the physical sciences, for example interatomic potential representations are becoming a common approach to accelerate molecular dynamics simulations. It was recently shown that ML methods can even reconstruct quantum mechanical Hamiltonians of molecules [1] and provide models of multiple electronic states [2]. ML-based surrogate models need to be able to replicate electronic structure results with a precision of a few meV to be reliable.

Research Questions for the project:

• Is the ACEhamiltonians representation [3] able to capture the electronic structure and energy landscape of multicomponent interfaces and nanostructures?

• How much training data and of which kind is required to generate a faithful and transferable representation of electronic structure of a material interface?

• Can we use surrogate Hamiltonians to predict measurable transport coefficients and reaction rates driven by light and temperature gradients?

You will develop a new machine learning representation of quantum mechanical Hamiltonians based on the recently proposed ACEhamiltonians approach [3]. You will expand this approach to multicomponent systems with the aim to perform large-scale non-equilibrium time-dependent simulations of ultrafast energy and charge transport at interfaces. You will generate training data based on Density Functional Theory and learn how to build such models based on the Atomic Cluster Expansion (ACE) formalism [4]. Once the models are constructed and validated, you will use a recently developed molecular dynamics simulation code [5] to apply them to simulate different dynamical processes such as light-driven defect propagation and light-driven ultrafast dynamics of molecules at surfaces.

References:

[1] Schütt et al. „Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions“, Nature Commun. 10, 5024 (2019), https://www.nature.com/articles/s41467-019-12875-2

[2] Westermayr, Maurer, „Physically inspired deep learning of molecular excitations and photoemission spectra”, Chem. Sci. 12, 10755-10764 (2021), https://pubs.rsc.org/en/content/articlelanding/2021/sc/d1sc01542g

[3] Zhang et al. „ Equivariant analytical mapping of first principles Hamiltonians to accurate and transferable materials models”, npj Computational Materials 8, 158 (2022) https://www.nature.com/articles/s41524-022-00843-2. Implemented in ACEhamiltonians.jl package.

[4] Dusson et al. “Atomic Cluster Expansion: Completeness, Efficiency, and Stability”, J. Comput. Phys. 454, 110946 (2022), https://doi.org/10.1016/j.jcp.2022.110946

[5] Gardner et al. “NQCDynamics.jl: A Julia package for nonadiabatic quantum classical molecular dynamics in the condensed phase”, J. Chem. Phys. 156, 174801 (2022), https://pubs.acs.org/doi/abs/10.1021/acs.jctc.9b01217

For further details about the project and how it links to the training included in the HetSys PhD programme, please click here

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