Congratulations to our students who have had research papers published this academic year! Read about their publications and where they be accessed below:

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Connor AllenLink opens in a new window

Connor Allen and Albert P Bartók, ‘Optimal data generation for machine learned interatomic potentials’, Learn.: Sci. Technol.3 045031 (2022), https://doi.org/10.1088/2632-2153/ac9ae7Link opens in a new window 

Abstract

Machine learning interatomic potentials (MLIPs) are routinely used atomic simulations, but generating databases of atomic configurations used in fitting these models is a laborious process, requiring significant computational and human effort. A computationally efficient method is presented to generate databases of atomic configurations that contain optimal information on the small-displacement regime of the potential energy surface of bulk crystalline matter. Utilising non-diagonal supercell (Lloyd-Williams and Monserrat 2015 Phys. Rev. B 92 184301), an automatic process is suggested for ab initio data generation. MLIPs were fitted for Al, W, Mg and Si, which very closely reproduce the ab initio phonon and elastic properties. The protocol can be easily adapted to other materials and can be inserted in the workflow of any flavour of MLIP generation.

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Christopher WoodgateLink opens in a new window

Christopher D. Woodgate, Julie B. Staunton, "Short-range order and compositional phase stability in refractory high-entropy alloys via first principles theory and atomistic modelling: NbMoTa, NbMoTaW and VNbMoTaW", Phys. Rev. Mater. 7 013801 (2023), https://doi.org/10.1103/PhysRevMaterials.7.013801Link opens in a new window

Abstract

Using an all-electron, first-principles, Landau-type theory, we study the nature of short-range order and compositional phase stability in equiatomic refractory high-entropy alloys, NbMoTa, NbMoTaW, and VNbMoTaW. We also investigate selected binary subsystems to provide insight into the physical mechanisms driving order. Our approach examines the short-range order of the solid solutions directly, infers disorder/order transitions, and also extracts parameters suitable for atomistic modeling of diffusional phase transformations. We find a hierarchy of relationships between the chemical species in these materials which promote ordering tendencies. The most dominant is a relative atomic size difference between the 3d element, V, and the other 4d and 5d elements which drives a B32-like order. For systems where V is not present, ordering is dominated by the difference in filling of valence states; pairs of elements that are isoelectronic remain weakly correlated to low temperatures, while pairs with a valence difference present B2-like order. Our estimated order-disorder transition temperature in VNbMoTaW is sufficiently high for us to suggest that SRO in this material may be experimentally observable.

Christopher D. Woodgate, Daniel Hedlund, L. H. Lewis, Julie B. Staunton, Interplay between magnetism and short-range order in Ni-based high-entropy alloys: CrCoNi, CrFeCoNi, and CrMnFeCoNi, arXiv:2303.00641 (2023), https://doi.org/10.48550/arXiv.2303.00641Link opens in a new window

Abstract

The impact of magnetism on predicted atomic short-range order in Ni-based high-entropy alloys is studied using a first-principles, all-electron, Landau-type linear response theory, coupled with lattice-based atomistic modelling. We perform two sets of linear-response calculations: one in which the paramagnetic state is modelled within the disordered local moment picture, and one in which systems are modelled in a magnetically ordered state. We show that the treatment of magnetism can have significant impact both on the predicted temperature of atomic ordering and also the nature of atomic order itself. In CrCoNi, we find that the nature of atomic order changes from being L12-like when modelled in the paramagnetic state to MoPt2-like when modelled assuming the system has magnetically ordered. In CrFeCoNi, atomic correlations between Fe and the other elements present are dramatically strengthened when we switch from treating the system as magnetically disordered to magnetically ordered. Our results show it is necessary to consider the magnetic state when modelling multicomponent alloys containing mid- to late-3d elements. Further, we suggest that there may be high-entropy alloy compositions containing 3d transition metals that will exhibit specific atomic short-range order when thermally treated in an applied magnetic field.

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Aravinthen RajkumarLink opens in a new window

Aravinthen Rajkumar, Peter Brommer and Łukasz Figiel, ‘An extensible density-biasing approach for molecular simulations of multicomponent block copolymers’, Soft Matter, 2023,19, 1569-1585, https://doi.org/10.1039/D2SM01516ALink opens in a new window

Abstract 

A node-density biased Monte Carlo methodology is proposed for the molecular structure generation of complex block copolymers. Within this methodology, the block copolymer is represented as bead-spring model. Using self-consistent field theory, a density field for all monomer species within the system is calculated. Block copolymers are generated by random walk configuration biased by the density fields. The proposed algorithm then modifies the generation process by taking the global structure of the polymer into account. It is then demonstrated that these global considerations can be built into the sampling procedure, specifically through functions that assign a permissible difference in density field value between relevant monomer species to each step of the random walk. In this way, the random walk may be naturally controlled to provide the most appropriate conformations. The overall viability of this approach has been demonstrated by using the resulting configurations in molecular dynamics simulations. This new methodology is demonstrated to be powerful enough to generate molecular configurations for a much wider variety of materials than the original approach. Two key examples of the new capabilities of the method are viable configurations for ABABA pentablock copolymers and ABC triblock terpolymers.

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Joe GilkesLink opens in a new window

Julia Westermayr, Joe Gilkes, Rhyan Barrett & Reinhard J. Maurer, ‘High-throughput property-driven generative design of functional organic molecules’, Nature Computational Science volume 3, 139–148 (2023), https://doi.org/10.1038/s43588-022-00391-1Link opens in a new window

Abstract

The design of molecules and materials with tailored properties is challenging, as candidate molecules must satisfy multiple competing requirements that are often difficult to measure or compute. While molecular structures produced through generative deep learning will satisfy these patterns, they often only possess specific target properties by chance and not by design, which makes molecular discovery via this route inefficient. In this work, we predict molecules with (Pareto-)optimal properties by combining a generative deep learning model that predicts three-dimensional conformations of molecules with a supervised deep learning model that takes these as inputs and predicts their electronic structure. Optimization of (multiple) molecular properties is achieved by screening newly generated molecules for desirable electronic properties and reusing hit molecules to retrain the generative model with a bias. The approach is demonstrated to find optimal molecules for organic electronics applications. Our method is generally applicable and eliminates the need for quantum chemical calculations during predictions, making it suitable for high-throughput screening in materials and catalyst design.

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Jingbang LiuLink opens in a new window

Jingbang Liu, Chengxi Zhao, Duncan A. Lockerby, and James E. Sprittles, Thermal capillary waves on bounded nanoscale thin films, Phys. Rev. E 107, 015105 (2023), https://doi.org/1103/PhysRevFluids.7.024203Link opens in a new window

Abstract

The effects of thermal fluctuations on nanoscale flows are captured by a numerical scheme that is underpinned by fluctuating hydrodynamics. A stochastic lubrication equation (SLE) is solved on nonuniform adaptive grids to study a series of nanoscale thin-film flows. The Fornberg scheme is used for high-resolution spatial discretization and a fully implicit time-marching scheme is designed for numerical stability. The accuracy of the numerical method is verified against theoretical results for thermal capillary waves during the linear stage of their development. The framework is then used to study the nonlinear behavior of three bounded thin-film flows: (1) droplet spreading, where power laws are derived; (2) droplet coalescence, where molecular dynamics results are reproduced by the SLE at a fraction of the computational cost and it is discovered that thermal fluctuations decelerate the process, in contrast to previously investigated phenomena; and (3) thin-film rupture, where, in the regime considered, disjoining pressure dominates the final stages of rupture.