Property Prediction for Complex Compounds Using Structure-Free Mendeleev Encoding and Machine Learning.

Predicting the properties for unseen materials exclusively on the basis of the chemical formula before synthesis and characterization has advantages for research and resource planning. This can be achieved using suitable structure-free encoding and machine learning methods, but additional processing decisions are required. In this study, we compare a variety of structure-free materials encodings and machine learning algorithms to predict the structure/property relationships of battery materials.

Classification of battery compounds using structure-free Mendeleev encodings.

Machine learning is a valuable tool that can accelerate the discovery and design of materials occupying combinatorial chemical spaces. However, the prerequisite need for vast amounts of training data can be prohibitive when significant resources are needed to characterize or simulate candidate structures. Recent results have shown that structure-free encoding of complex materials, based entirely on chemical compositions, can overcome this impediment and perform well in unsupervised learning tasks.

Complex Dispersion of Detonation Nanodiamond Revealed by Machine Learning Assisted Cryo-TEM and Coarse-Grained Molecular Dynamics Simulations.

Understanding the polydispersity of nanoparticles is crucial for establishing the efficacy and safety of their role as drug delivery carriers in biomedical applications. Detonation nanodiamonds (DNDs), 3-5 nm diamond nanoparticles synthesized through detonation process, have attracted great interest for drug delivery due to their colloidal stability in water and their biocompatibility. More recent studies have challenged the consensus that DNDs are monodispersed after their fabrication, with their aggregate formation poorly understood.

Explainable discovery of disease biomarkers: The case of ovarian cancer to illustrate the best practice in machine learning and Shapley analysis.

Objective: Ovarian cancer is a significant health issue with lasting impacts on the community. Despite recent advances in surgical, chemotherapeutic and radiotherapeutic interventions, they have had only marginal impacts due to an inability to identify biomarkers at an early stage. Biomarker discovery is challenging, yet essential for improving drug discovery and clinical care.

Unsupervised machine learning discovers classes in aluminium alloys.

Aluminium (Al) alloys are critical to many applications. Although Al alloys have been commercially widespread for over a century, their development has predominantly taken a trial-and-error approach. Furthermore, many discrete studies regarding Al alloys, often application specific, have precluded a broader consolidation of Al alloy classification.

Liquid metal synthesis solvents for metallic crystals.

In nature, snowflake ice crystals arrange themselves into diverse symmetrical six-sided structures. We show an analogy of this when zinc (Zn) dissolves and crystallizes in liquid gallium (Ga). The low-melting-temperature Ga is used as a "metallic solvent" to synthesize a range of flake-like Zn crystals.

Safety-by-design using forward and inverse multi-target machine learning.

The economic and social future of nanotechnology depends on our ability and manufacture nanomaterials that avoid potential toxicity, by identifying them before they are made, used and released into the environment. Safety-by-design is a framework for including these issues at an early stage of the development process, but balancing multiple nanoparticle properties and selection criteria remains challenging. Based on a synthetic data set of over 19,000 possible sunscreen product specifications, we have used multi-target machine learning to predict the corresponding size, shape, concentration and polytype of titania nanoparticle additives.

Impact of atomistic or crystallographic descriptors for classification of gold nanoparticles.

Machine learning models are known to be sensitive to the features used to train them, but there is currently no way to predict the impact of using different features prior to feature extraction. This is particularly important to fields such as nanotechnology that are highly multi-disciplinary, and samples can be characterised many different ways depending on the preferences of individual researchers. Does it matter if nanomaterials are described using the interatomic coordinations or more complex order parameters? In this study we compare results of supervised and unsupervised learning on a single set of gold nanoparticles that has been characterised by two different descriptors, each with a unique feature space.

Enhancing classical gold nanoparticle simulations with electronic corrections and machine learning.

Classical simulations of materials and nanoparticles have the advantage of speed and scalability but at the cost of precision and electronic properties, while electronic structure simulations have the advantage of accuracy and transferability but are typically limited to small and simple systems due to the increased computational complexity. Machine learning can be used to bridge this gap by providing correction terms that deliver electronic structure results based on classical simulations, to retain the best of both worlds. In this study we train an artificial neural network (ANN) as a general ansatz to predict a correction of the total energy of arbitrary gold nanoparticles based on general (material agnostic) features, and a limited set of structures simulated with an embedded atom potential and the self-consistent charge density functional tight binding method.

Unsupervised structure classes vs. supervised property classes of silicon quantum dots using neural networks.

Machine learning classification is a useful technique to predict structure/property relationships in samples of nanomaterials where distributions of sizes and mixtures of shapes are persistent. The separation of classes, however, can either be supervised based on domain knowledge (human intelligence), or based entirely on unsupervised machine learning (artificial intelligence). This raises the questions as to which approach is more reliable, and how they compare? In this study we combine an ensemble data set of electronic structure simulations of the size, shape and peak wavelength for the optical emission of hydrogen passivated silicon quantum dots with artificial neural networks to explore the utility of different types of classes.

The pure and representative types of disordered platinum nanoparticles from machine learning.

The development of interpretable structure/property relationships is a cornerstone of nanoscience, but can be challenging when the structural diversity and complexity exceeds our ability to characterise it. This is often the case for imperfect, disordered and amorphous nanoparticles, where even the nomenclature can be unspecific. Disordered platinum nanoparticles have exhibited superior performance for some reactions, which makes a systematic way of describing them highly desirable.

Simulating facet-dependent aggregation and assembly of distributions of polyhedral nanoparticles.

Coarse-grained molecular dynamics simulations of diamond nanoparticles were performed to investigate the effects of size polydispersity on three polyhedral shapes chosen to span a diverse space of surface interactions. It was found that the resulting self-assembly was size dependent as the simulations were quenched, with the largest nanoparticles providing a clustered scaffold for subsequent smaller nanoparticle assembly. Additionally, facet-facet interactions were dominated by the {111} surface and the resulting aggregate was dominated by meso-sized porosity for monodisperse systems, broadening to larger diameters for polydisperse systems.

Machine learning reveals multiple classes of diamond nanoparticles.

Generating samples of nanoparticles with specific properties that allow for structural diversity, rather than requiring structural precision, is a more sustainable prospect for industry, where samples need to be both targeted to specific applications and cost effective. This can be better enabled by defining classes of nanoparticles and characterising the properties of the class as a whole. In this study, we use machine learning to predict the different classes of diamond nanoparticles based entirely on the structural features and explore the populations of these classes in terms of the size, shape, speciation and charge transfer properties.

Dynamic self-assembly of detonation nanodiamond in water.

Nanodiamonds are increasingly used in many areas of science and technology, yet, their colloidal properties remain poorly understood. Here we use direct imaging as well as light and X-ray scattering reveal that purified detonation nanodiamond (DND) particles in an aqueous environment exhibit a self-assembled lace-like network, even without additional surface modification. Such behaviour is previously unknown and contradicts the current consensus that DND exists as mono-dispersed single particles.

Predicting archetypal nanoparticle shapes using a combination of thermodynamic theory and machine learning.

Machine learning is a useful way of identifying representative or pure nanoparticle shapes as part of a larger ensemble, but its predictive capabilities can be limited when a large dataset of candidate structures must already exist. Ideally one would like to use machine learning to define the ideal dataset for future, more computationally intensive, studies before a significant amount of resources are consumed. In this work we combine an established analytical phenomenological model and statistical machine learning to predict the archetypes and prototypes of a diverse ensemble of 2380 platinum nanoparticle morphologies developed with less than twenty input electronic structure simulations.

Correlating anisotropy and disorder with the surface structure of platinum nanoparticles.

Due to the competition between numerous physicochemical variables during formation and processing, platinum nanocatalysts typically contain a mixture of shapes, distributions of sizes, and a considerable degree of surface imperfection. Structural imperfection and sample polydispersivity are inevitable at scale, but accepting bulk and surface diversity as legitimate design features provides new opportunities for nanoparticle design. In recent years disorder and anisotropy have been embraced as useful design parameters but predicting the impact of uncontrollable imperfection a priori is challenging.

Size dependent surface reconstruction in detonation nanodiamonds.

Nanometer-sized diamond particles are used in bio-medical applications, where the nature of the nanodiamond surfaces is crucial to achieving correct functionalisation. Herein, using high-resolution transmission electron microscopy and electronic structure calculations, we study the surface reconstructions that occur in detonation-synthesized nanodiamonds. Our results show that particles smaller than 3 nm exhibit size- and shape-dependent surface reconstructions, and that the surfaces can exhibit a higher-than-expected fraction of sp bonding.

Machine Learning for Silver Nanoparticle Electron Transfer Property Prediction.

Nanoparticles exhibit diverse structural and morphological features that are often interconnected, making the correlation of structure/property relationships challenging. In this study a multi-structure/single-property relationship of silver nanoparticles is developed for the energy of Fermi level, which can be tuned to improve the transfer of electrons in a variety of applications. By combining different machine learning analytical algorithms, including k-mean, logistic regression, and random forest with electronic structure simulations, we find that the degree of twinning (characterized by the fraction of hexagonal closed packed atoms) and the population of the {111} facet (characterized by a surface coordination number of nine) are strongly correlated to the Fermi energy of silver nanoparticles.

The impact of size and shape distributions on the electron charge transfer properties of silver nanoparticles.

Many applications of silver nanoparticles are moderated by the electron charge transfer properties, such as the ionization potential, electron affinity and Fermi energy, which may be tuned by controlling the size and shape of individual particles. However, since producing samples of silver nanoparticles that are perfectly monodispersed in terms of both size and shape can be prohibitive, it is important to understand how these properties are impacted by polydispersivity, and ideally be able to predict the tolerance for variation of different geometric features. In this study, we use straightforward statistical methods, together with electronic structure simulations, to predict the electron charge transfer properties of different types of ensembles of silver nanoparticles and how restricting the structural diversity in different ways can improve or retard performance.

Machine learning and genetic algorithm prediction of energy differences between electronic calculations of graphene nanoflakes.

Computational screening is key to understanding structure-function relationships at the nanoscale but the high computational cost of accurate electronic structure calculations remains a bottleneck for the screening of large nanomaterial libraries. In this work we propose a data-driven strategy to predict accuracy differences between different levels of theory. Machine learning (ML) models are trained with structural features of graphene nanoflakes to predict the differences between electronic properties at two levels of approximation.

Bias-Free Chemically Diverse Test Sets from Machine Learning.

Current benchmarking methods in quantum chemistry rely on databases that are built using a chemist's intuition. It is not fully understood how diverse or representative these databases truly are. Multivariate statistical techniques like archetypal analysis and K-means clustering have previously been used to summarize large sets of nanoparticles however molecules are more diverse and not as easily characterized by descriptors.

On reverse Monte Carlo constraints and model reproduction.

Reverse Monte Carlo (RMC) simulations were performed to investigate the effectiveness of any combination of five experimentally motivated constraints on the reproduction of a test case, a ternary ab initio model. It was found that low energy structures fitting a variety of constraints commonly used in the RMC methodology could still provide an incorrect description of the chemical structural unit populations in multi-elemental systems. It is shown that the use of an elemental bond type constraint is an effective way to avoid this.

Predicting the role of seed morphology in the evolution of anisotropic nanocatalysts.

Controlling the structure of nanocrystals is an effective way to tune their properties and improve performance in a wide variety of applications. However, the atomic pathways for achieving this goal are difficult to identify and exercise, due to competing kinetic and thermodynamic influences during formation. In particular, an understanding of how symmetry, and symmetry breaking, determine nanocrystal morphology would significantly advance our ability to produce nanomaterials with prescribed functions.

Impact of distributions on the archetypes and prototypes in heterogeneous nanoparticle ensembles.

The magnitude and complexity of the structural and functional data available on nanomaterials requires data analytics, statistical analysis and information technology to drive discovery. We demonstrate that multivariate statistical analysis can recognise the sets of truly significant nanostructures and their most relevant properties in heterogeneous ensembles with different probability distributions. The prototypical and archetypal nanostructures of five virtual ensembles of Si quantum dots (SiQDs) with Boltzmann, frequency, normal, Poisson and random distributions are identified using clustering and archetypal analysis, where we find that their diversity is defined by size and shape, regardless of the type of distribution.