TYC Symposia: Disordered and amorphous functional materials

Professor David Drabold  - Ohio University

Professor Volker Deringer  - University of Oxford

Professor Alex Shluger - UCL

Professor Andrew Goodwin - University of Oxford

Thursday 30th April 2020
Time: 2pm
Venue: Lecture room G20, Royal School of Mines, Imperial College London
Contact: Ms Hafiza Bibi
Tel: 020 7594 7252

To attend the event please register here

Teaching Reverse Monte Carlo to respect Chemistry: a new method

Professor David Drabold, Ohio University

Perhaps the oldest problem in the science of glassy and amorphous materials is determining the atomistic structure. The question is fundamental, since the structure determines physical observables. An ideal model should, of course, coincide with experimental information and be a suitable minimum of an appropriate energy functional. Reverse Monte Carlo readily produces models reproducing diffraction data, but can yield poor models since it includes no chemical information, and Molecular Dynamics (MD), especially ab initio MD is limited to small systems and often, overly fast quenches. Almost all current simulations are of one type or the other.

"Force Enhanced Atomic Refinement" (FEAR) is a method to determine model coordinates jointly optimizing agreement with experiments and minimizing a realistic potential energy function. Robust convergence to a joint solution accrues if one iterates the two-step process: (1) Invoke RMC, stopping after N accepted moves, (2) move the atoms incrementally along the atomic forces for M moves to reduce the potential energy. In practice, a few to several thousand such iterations produce the desired models. In practice N>>M (with N~100, M~1), which is computationally convenient as the RMC steps are very inexpensive, at least for diffraction data. In practice the method is faster than comparable quench methods and often yields better cohesive energies than MD.

We have applied this scheme to many systems: amorphous silicon[1], amorphous silica[2], amorphous carbon[3], silver-alloyed chalcogenide solid electrolyte materials[4], Zr-doped tantala for Laser Interferometer Gravitational-Wave Observatory applications[5], and a metallic glass[6]. Others have shown that it is highly successful with sodium silicate glasses[7], where it has revealed new information about medium-range order. Ongoing work on analysis and extensions will be discussed.


[1] D. Igram, B. Bhattarai, P. Biswas and D. A. Drabold, J. Non-Cryst. Sol 492 27 (2018); A. Pandey, P. Biswas, B. Bhattarai, D. A. Drabold Phys. Rev B 94 235208 (2016).

[2] A. Pandey, P. Biswas and D. A. Drabold,  Phys. Rev. B 92 155205 (2015).

[3] B. Bhattarai, A. Pandey and D. A. Drabold,  Carbon 131 168 (2018). B. Bhattarai, P. Biswas, R. Atta-Fynn and D. A. Drabold,  Phys. Chem. Chem. Phys. 20 19546 (2018).

[4] A. Pandey, P. Biswas and D. A. Drabold, Scientific Reports, 6 33731 (2016).

[5] R. Thapa, K. Prasai and D. A. Drabold (unpublished).

[6] B. Bhattarai, R. Thapa and D. A. Drabold MSMSE 27 075002 (2019).

[7] Revealing the Atomic Structure of Silicate Glasses by Force-Enhanced Atomic Refinement

Q. Zhou, T. Du, L. Guo, M. M. Smedskjaer, M. Bauchy,


Machine Learning Approaches for Modelling and Understanding Amorphous Functional Materials

Professor Volker Deringer  - University of Oxford

Understanding the links between atomic structure, bonding, and properties in materials is a formidable task. Density-functional theory (DFT) based simulations have played important roles in this – but they are computationally expensive and can describe complex materials only in small model systems. Novel interatomic potentials based on machine learning (ML) have recently garnered a lot of attention in the computational materials science community: they achieve similar levels of accuracy but are orders of magnitude faster.
In this talk, I will argue that ML-based interatomic potentials are particularly useful for studying materials with complex structures, such as amorphous (non-crystalline) solids. I will first describe an ML potential for amorphous carbon [1], with a special view on what is needed to generate and validate ML potentials for the amorphous state. I will then present an application to porous and partly "graphitised" carbon structures, which are relevant for applications in energy storage, including Na-ion battery anodes [2]. The second part of the talk will focus on amorphous silicon (a-Si), another prototypical non-crystalline material – where ML-driven simulations allowed us to unlock long simulation times and accurate atomistic structures [3], and “machine-learned” atomic energies were shown to permit a chemical interpretation, suggesting a more general approach to modelling and understanding the intricate liquid and amorphous phases of silicon [4].

[1] V. L. Deringer, G. Csányi, Phys. Rev. B 95, 094203 (2017).
[2] J.-X. Huang, G. Csányi, J.-B. Zhao, J. Cheng, V. L. Deringer, J. Mater. Chem. A 7, 19070 (2019).
[3] V. L. Deringer, N. Bernstein, A. P. Bartók, R. N. Kerber, L. E. Marbella, C. P. Grey, S. R. Elliott, G. Csányi, J. Phys. Chem. Lett. 9, 2879 (2018).
[4] N. Bernstein, B. Bhattarai, G. Csányi, D. A. Drabold, S. R. Elliott, V. L. Deringer, Angew. Chem. Int. Ed., 54, 7057 (2019).


Role of Electron and Hole Localization in Degradation of Amorphous Oxide Films

Professor Alexander Shluger

Most current electronic and electrochemical devices are stacks of thin films and interfaces operating under electrical stress. Nanometre-thick oxide films play crucial role in performance of these devices. Injection of excess electrons and holes into oxide films is responsible for the mechanisms that govern the formation of conductive filaments in resistance switching memory devices, the dielectric breakdown in microelectronic devices, and the performance of photo-electrochemical and oxide fuel cells. Our theoretical modelling combined with experimental observations demonstrates that structural disorder in amorphous SiO2, Al2O3 TiO2, ZnO and HfO2 films creates precursor sites which can spontaneously trap up to two electrons or holes in deep states in the band gap. The results demonstrate that single- and bi-polaron electron and hole states can form in a- SiO2 [1], a-TiO2 and a- HfO2 [2], where the effect of local disorder is amplified by polaronic relaxation of amorphous network. Only hole trapping is found in a-Al2O3 [3] and ZnO. The electron localization weakens Me–O bonds, which can be broken upon thermal activation, creating an O2- interstitial ion and a neutral O vacancy [4]. O2- interstitial ions can easily diffuse through the oxide and in devices are guided to the positive electrode by the electric field [5]. Multi-scale modelling including electron injection rates, defect creation and electron hopping through created defects is used to describe the structural and electrical degradation and dielectric breakdown in oxide films.

[1] A.-M. El-Sayed, M. B. Watkins, V. V. Afanas’ev, A. L. Shluger, Phys. Rev. B, 89, 125201 (2014).

[2] M. Kaviani, J. Strand, V. V. Afanas’ev, and A. L. Shluger, Phys. Rev. B, 94, 020103 (2016).

[3] O. Dicks and A. L. Shluger, J. Phys.: Condens. Matter, 29, 314005 (2017)

[4] D. Z. Gao, A.-M. El-Sayed, A. L. Shluger, Nanotechnology, 27, 505207 (2016)

[5] A. Mehonic, M. Buckwell, L. Montesi et al. Adv. Mater. 28, 7486 (2016)


Hidden vacancy-network polymorphism of Prussian Blue analogues

Professor Andrew Goodwin

Prussian Blue analogues (PBAs) are an important and broad family of materials, with applications in e.g. catalysis, energy, proton conduction, and gas storage. The vast majority of PBAs are defective materials that contain a very large fraction of transition-metal vacancies; these vacancies in turn connect to form extended micropore networks. We have used 3D total scattering measurements to characterise the nature of these disordered pore networks in a variety of PBAs. This talk will present our results, and demonstrate how PBA composition and synthesis approach allow for correlated defect engineering in PBAs as a means of controlling storage capacity, anisotropy, and transport efficiency.




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