Tran Doan Huan

Pathways towards ferroelectricity in hafnia

The question of whether one can systematically identify (previously unknown) ferroelectric phases of a given material is addressed, taking hafnia

Machine learning strategy for accelerated design of polymer dielectrics

({\mathrm{HfO}}_{2})

Accelerated materials property predictions and design using motif-based fingerprints

as an example. Low free energy phases at various pressures and temperatures are identified using a first-principles based structure search algorithm. Ferroelectric phases are then recognized by exploiting group theoretical principles for the symmetry-allowed displacive transitions between nonpolar and polar phases. Two orthorhombic polar phases occurring in space groups

Poly(dimethyltin glutarate) as a Prospective Material for High Dielectric Applications

Pca{2}_{1}

A polymer dataset for accelerated property prediction and design

and

Low-Energy Polymeric Phases of Alanates

Pmn{2}_{1}

Interlayer Interactions in van der Waals Heterostructures: Electron and Phonon Properties

are singled out as the most viable ferroelectric phases of hafnia, as they display low free energies (relative to known nonpolar phases), and substantial switchable spontaneous electric polarization. These results provide an explanation for the recently observed surprising ferroelectric behavior of hafnia, and reveal pathways for stabilizing ferroelectric phases of hafnia as well as other compounds.

A universal strategy for the creation of machine learning-based atomistic force fields

The ability to efficiently design new and advanced dielectric polymers is hampered by the lack of sufficient, reliable data on wide polymer chemical spaces, and the difficulty of generating such data given time and computational/experimental constraints. Here, we address the issue of accelerating polymer dielectrics design by extracting learning models from data generated by accurate state-of-the-art first principles computations for polymers occupying an important part of the chemical subspace. The polymers are ‘fingerprinted’ as simple, easily attainable numerical representations, which are mapped to the properties of interest using a machine learning algorithm to develop an on-demand property prediction model. Further, a genetic algorithm is utilised to optimise polymer constituent blocks in an evolutionary manner, thus directly leading to the design of polymers with given target properties. While this philosophy of learning to make instant predictions and design is demonstrated here for the example of polymer dielectrics, it is equally applicable to other classes of materials as well.

Layered structures of organic/inorganic hybrid halide perovskites

Data-driven approaches are particularly useful for computational materials discovery and design as they can be used for rapidly screening over a very large number of materials, thus suggesting lead candidates for further in-depth investigations. A central challenge of such approaches is to develop a numerical representation, often referred to as a fingerprint, of the materials. Inspired by recent developments in chem-informatics, we propose a class of hierarchical motif-based topological fingerprints for materials composed of elements such as C, O, H, N, F, etc., whose coordination preferences are well understood. We show that these fingerprints, when representing either molecules or crystals, may be effectively mapped onto a variety of properties using a similarity-based learning model and hence can be used to predict relevant properties of a material, given that its fingerprint can be defined. Two simple procedures are introduced to demonstrate that the learning model can be inverted to identify the desired fingerprints and then, to reconstruct molecules which possess a set of targeted properties.

Effect of Incorporating Aromatic and Chiral Groups on the Dielectric Properties of Poly(dimethyltin esters)

Poly(dimethyltin glutarate) is presented as the first organometallic polymer, a high dielectric constant, and low dielectric loss material. Theoretical results correspond well in terms of the dielectric constant. More importantly, the dielectric constant can be tuned depending on the solvent a film of the polymer is cast from. The breakdown strength is increased through blending with a second organometallic polymer.