Materials designed to degrade: structure, properties, processing, and performance relationships in polyhydroxyalkanoate biopolymers.

Conventional plastics pose significant environmental and health risks across their life cycle, driving intense interest in sustainable alternatives. Among these, polyhydroxyalkanoates (PHAs) stand out for their biocompatibility, degradation characteristics, and diverse applications. Yet, challenges like production cost, scalability, and limited chemical variety hinder their widespread adoption, impacting material selection and design.

Toward a Predictive Understanding of Cyanobacterial Harmful Algal Blooms through AI Integration of Physical, Chemical, and Biological Data.

Freshwater cyanobacterial harmful algal blooms (cyanoHABs) are a worldwide problem resulting in substantial economic losses, due to harm to drinking water supplies, commercial fishing, wildlife, property values, recreation, and tourism. Moreover, toxins produced from some cyanoHABs threaten human and animal health. Climate warming can affect the distribution of cyanoHABs, where rising temperatures facilitate more intense blooms and a greater distribution of cyanoHABs in inland freshwater.

How inversion relates to disordering tendencies in complex oxides.

Complex oxides exhibit great functionality due to their varied chemistry and structures. They are quite flexible in terms of the ordering of cations, which can also impact their functional properties to a large extent. Thus, the propensity for a complex oxide to disorder is a key factor in optimizing and discovering new materials.

Biodegradation Studies of Polyhydroxybutyrate and Polyhydroxybutyrate--Polyhydroxyvalerate Films in Soil.

Due to increased environmental pressures, significant research has focused on finding suitable biodegradable plastics to replace ubiquitous petrochemical-derived polymers. Polyhydroxyalkanoates (PHAs) are a class of polymers that can be synthesized by microorganisms and are biodegradable, making them suitable candidates. The present study looks at the degradation properties of two PHA polymers: polyhydroxybutyrate (PHB) and polyhydroxybutyrate--polyhydroxyvalerate (PHBV; 8 wt.

Machine Learning for Melting Temperature Predictions and Design in Polyhydroxyalkanoate-Based Biopolymers.

Diminishing fossil fuel-based resources and ever-growing environmental concerns related to plastic pollution demand for the development of sustainable and biodegradable polymeric material alternatives. Polyhydroxyalkanoates (PHAs) represent an eco-friendly and economically viable class of polymers with a wide range of applications. However, the chemical diversity combined with tunable physical properties available within PHAs poses discovery and optimization challenges with respect to identifying optimal application-specific chemical compositions.

Predicting the Mechanical Response of Polyhydroxyalkanoate Biopolymers Using Molecular Dynamics Simulations.

Polyhydroxyalkanoates (PHAs) have emerged as a promising class of biosynthesizable, biocompatible, and biodegradable polymers to replace petroleum-based plastics for addressing the global plastic pollution problem. Although PHAs offer a wide range of chemical diversity, the structure-property relationships in this class of polymers remain poorly established. In particular, the available experimental data on the mechanical properties is scarce.

Strong Purcell enhancement at telecom wavelengths afforded by spinel FeO nanocrystals with size-tunable plasmonic properties.

Developments in the field of nanoplasmonics have the potential to advance applications from information processing and telecommunications to light-based sensing. Traditionally, nanoscale noble metals such as gold and silver have been used to achieve the targeted enhancements in light-matter interactions that result from the presence of localized surface plasmons (LSPs). However, interest has recently shifted to intrinsically doped semiconductor nanocrystals (NCs) for their ability to display LSP resonances (LSPRs) over a much broader spectral range, including the infrared (IR).

A Deep Neural Network for Accurate and Robust Prediction of the Glass Transition Temperature of Polyhydroxyalkanoate Homo- and Copolymers.

The purpose of this study was to develop a data-driven machine learning model to predict the performance properties of polyhydroxyalkanoates (PHAs), a group of biosourced polyesters featuring excellent performance, to guide future design and synthesis experiments. A deep neural network (DNN) machine learning model was built for predicting the glass transition temperature, , of PHA homo- and copolymers. Molecular fingerprints were used to capture the structural and atomic information of PHA monomers.

Band-Edge Engineering To Eliminate Radiation-Induced Defect States in Perovskite Scintillators.

Under radiative environments such as extended hard X- or γ-rays, degradation of scintillation performance is often due to irradiation-induced defects. To overcome the effect of deleterious defects, novel design mitigation strategies are needed to identify and design more resilient materials. The potential for band-edge engineering to eliminate the effect of radiation-induced defect states in rare-earth-doped perovskite scintillators is explored, taking Ce-doped LuAlO as a model material system, using density functional theory (DFT)-based DFT + and hybrid Heyd-Scuseria-Ernzerhof (HSE) calculations.

Molecular dynamics simulations for glass transition temperature predictions of polyhydroxyalkanoate biopolymers.

Polyhydroxyalkanoates (PHAs) represent an emerging class of biosynthetic and biodegradable polyesters that exhibit considerable potential to replace petroleum-based plastics towards a sustainable future. Despite the promise, general structure-property mappings within this class of polymers remain largely unexplored. An efficient exploration of this vast chemical space calls for the development and validation of predictive methods for accurate estimation of a diverse range of properties for PHA-based polymers.

Machine-Learning-Based Predictive Modeling of Glass Transition Temperatures: A Case of Polyhydroxyalkanoate Homopolymers and Copolymers.

Polyhydroxyalkanoate-based polymers-being ecofriendly, biosynthesizable, and economically viable and possessing a broad range of tunable properties-are currently being actively pursued as promising alternatives for petroleum-based plastics. The vast chemical complexity accessible within this class of polymers gives rise to challenges in the rational discovery of novel polymer chemistries for specific applications. The burgeoning field of polymer informatics addresses this challenge via providing tools and strategies for accelerated property prediction and materials design via surrogate machine-learning models built on reliable past data.

Multifidelity Information Fusion with Machine Learning: A Case Study of Dopant Formation Energies in Hafnia.

Cost versus accuracy trade-offs are frequently encountered in materials science and engineering, where a particular property of interest can be measured/computed at different levels of accuracy or fidelity. Naturally, the most accurate measurement is also the most resource and time intensive, while the inexpensive quicker alternatives tend to be noisy. In such situations, a number of machine learning (ML) based multifidelity information fusion (MFIF) strategies can be employed to fuse information accessible from varying sources of fidelity and make predictions at the highest level of accuracy.

The impact of chemical order on defect transport in mixed pyrochlores.

Using temperature accelerated dynamics, an accelerated molecular dynamics method, we examine the relationship between composition and cation ordering and defect transport in the mixed pyrochlore Gd2(Ti1-xZrx)2O7, using the oxygen vacancy as a representative defect structure. We find that the nature of transport is very sensitive to the cation structure, transitioning, as a function of composition, from three-dimensional migration to two-dimensional to pseudo-one-dimensional to becoming essentially immobile before reverting back to three-dimensional as the Zr content is increased. The rates of migration are also affected by the cation structure in the various compositions.

Computational screening of organic polymer dielectrics for novel accelerator technologies.

The use of infrared lasers to power accelerating dielectric structures is a developing area of research. Within this technology, the choice of the dielectric material forming the accelerating structures, such as the photonic band gap (PBG) structures, is dictated by a range of interrelated factors including their dielectric and optical properties, amenability to photo-polymerization, thermochemical stability and other target performance metrics of the particle accelerator. In this direction, electronic structure theory aided computational screening and design of dielectric materials can play a key role in identifying potential candidate materials with the targeted functionalities to guide experimental synthetic efforts.

A polymer dataset for accelerated property prediction and design.

Emerging computation- and data-driven approaches are particularly useful for rationally designing materials with targeted properties. Generally, these approaches rely on identifying structure-property relationships by learning from a dataset of sufficiently large number of relevant materials. The learned information can then be used to predict the properties of materials not already in the dataset, thus accelerating the materials design.

Machine Learning Strategy for Accelerated Design of Polymer Dielectrics.

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.

Termination chemistry-driven dislocation structure at SrTiO3/MgO heterointerfaces.

Exploiting the promise of nanocomposite oxides necessitates a detailed understanding of the dislocation structure at the interfaces, which governs diverse and technologically relevant properties. Here we report atomistic simulations demonstrating a strong dependence of the dislocation structure on the termination chemistry at the SrTiO3/MgO heterointerface. The SrO- and TiO2-terminated interfaces exhibit distinct nearest neighbour arrangements between cations and anions, leading to variations in local electrostatic interactions across the interface that ultimately dictate the dislocation structure.

Rational design of all organic polymer dielectrics.

To date, trial and error strategies guided by intuition have dominated the identification of materials suitable for a specific application. We are entering a data-rich, modelling-driven era where such Edisonian approaches are gradually being replaced by rational strategies, which couple predictions from advanced computational screening with targeted experimental synthesis and validation. Here, consistent with this emerging paradigm, we propose a strategy of hierarchical modelling with successive downselection stages to accelerate the identification of polymer dielectrics that have the potential to surpass 'standard' materials for a given application.

Revisiting the Al/Al₂O₃ interface: coherent interfaces and misfit accommodation.

We study the coherent and semi-coherent Al/α-Al2O3 interfaces using molecular dynamics simulations with a mixed, metallic-ionic atomistic model. For the coherent interfaces, both Al-terminated and O-terminated nonstoichiometric interfaces have been studied and their relative stability has been established. To understand the misfit accommodation at the semi-coherent interface, a 1-dimensional (1D) misfit dislocation model and a 2-dimensional (2D) dislocation network model have been studied.

Accelerating materials property predictions using machine learning.

The materials discovery process can be significantly expedited and simplified if we can learn effectively from available knowledge and data. In the present contribution, we show that efficient and accurate prediction of a diverse set of properties of material systems is possible by employing machine (or statistical) learning methods trained on quantum mechanical computations in combination with the notions of chemical similarity. Using a family of one-dimensional chain systems, we present a general formalism that allows us to discover decision rules that establish a mapping between easily accessible attributes of a system and its properties.

How critical are the van der Waals interactions in polymer crystals?

van der Waals (vdW) interactions play a prominent role in polymer crystallization. However, density functional theory (DFT) computations that utilize conventional (semi)local exchange-correlation functionals are unable to account for vdW interactions adequately and hence lead to poor predictions of equilibrium structures, densities, cohesive energies, and bulk moduli of polymeric crystals. This study therefore applies two forms of dispersion corrections to DFT, using either the Grimme (DFT-D3/D2) or the Tkatchenko and Scheffler (DFT-TS) approaches.

Oxygen-assisted unidirectional growth of CdSe nanorods using a low-temperature redox process.

The role of oxygen in directing the low temperature (125 degrees C), redox-assisted, unidimensional and unidirectional growth of CdSe nanocrystals (NCs) was investigated. In the presence of oxygen, CdSe quantum dots grow selectively along their c axis with little to no change in their width. Reduction of oxygen in the growth medium results in three-dimensional growth.