Structure-Free Mendeleev Encodings of Material Compounds for Machine Learning

Machine learning is a powerful tool to predict the properties of materials for a variety of applications. However, generating data sets of carefully characterized materials can be time-consuming and costly, particularly when numerous candidate materials are later found to be irrelevant. The problem could be alleviated if machine learning can be used with minimal information to provide guidance at an early stage before significant investment has been made. Since structural characterization is one of the most expensive parts of the process, this study explores structure-free encoding of materials using Mendeleev encoding, a method that does not require information such as lattice constants, lattice positions, or bonding networks. We evaluate Mendeleev encoding using three data sets of continuous, complex material compounds used for battery applications, with four different unsupervised learning methods, inclusive of six algorithms and four evaluation metrics and in addition visualizations of the results. Our results show that Mendeleev encoding is more accurate, stable, and reliable than alternative structure-free encoding, allowing both principle component analysis and archetypal analysis to capture more of the variance during dimensionality reduction and consistently provide superior clustering results. Mendeleev encoding is a simple and scientifically intuitive way of representing material data that is both human and machine-readable and is applicable to any machine-learning task training with tabular data

Identification of Nanoparticle Prototypes and Archetypes

High-throughput (HT) computational characterization of nanomaterials is poised to accelerate novel material breakthroughs. The number of possible nanomaterials is increasing exponentially along with their complexity, and so statistical and information technology will play a fundamental role in rationalizing nanomaterials HT data. We demonstrate that multivariate statistical analysis of heterogeneous ensembles can identify the truly significant nanoparticles and their most relevant properties. Virtual samples of diamond nanoparticles and graphene nanoflakes are characterized using clustering and archetypal analysis, where we find that saturated particles are defined by their geometry, while nonsaturated nanoparticles are defined by their carbon chemistry. At the complex hull of the nanostructure spaces, a combination of complex archetypes can efficiency describe a large number of members of the ensembles, whereas the regular shapes that are typically assumed to be representative can only describe a small set of the most regular morphologies. This approach provides a route toward the characterization of computationally intractable virtual nanomaterial spaces, which can aid nanomaterials discovery in the foreseen big data scenario

Stability of Nanodiamond Surfaces Exposed to N, NH, and NH₂

Knowledge about the surface functionalization of nanodiamonds is of great importance for nanomedical and nanobiological applications. In this paper, we explore the relative stability of the nanodiamond passivated by amidogen (NH2), imidogen (NH), and nitrogen (N) in N2 + H2 and ammonia environments, using density functional tight binding simulations over a range of temperatures. The N-adsorption is found to be energetically unfavorable on the nanodiamond surfaces up to 1500 K. In contrast, adsorption of NH and NH2 is found to be thermodynamically stable at least around room temperature. We also compare the possibility of anisotropic (facet-dependent) adsorption and find that {100} facets of the nanodiamond are more preferred for the NH adsorption in the entire temperature range considered but are only favored at high temperature for the NH2 adsorption. We also show that this chemical functionalization is more stable in the N2 + H2 environment than in the ammonia environment, but its stability depends on the size, shape, and morphology of the nanodiamond

Importance of Structural Features and the Influence of Individual Structures of Graphene Oxide Using Shapley Value Analysis

The application of machine learning (ML) to materials chemistry can accelerate the design process, and when coupled with a detailed explanation, can guide future research. Shapley value analysis is a complementary approach capable of providing a comprehensive analysis of the underlying reasons behind a structure/property relationship. In this study, we have used data sets of graphene oxide nanomaterials generated using electronic structure simulations to train ML models with outstanding accuracy, generalizability, and stability to predict the formation energy and the Fermi energy and applied Shapley value analysis to understand the results. Feature important profiles that rank the value of structural characteristics to each property confirmed that the underlying structure/property relationships are relatively simple and scientifically intuitive, even though the ML models need complex information to achieve high performance. We have also reported instance influence profiles that rank the value of each individual graphene oxide structure to the training process. Feature/instance interactions are also investigated to explain which structural characteristics make particular structures influential, revealing that the most influential structures typically have very high or very low concentrations of H or O. Since the range of concentrations is typically chosen by researchers based on domain knowledge at the outset, this highlights that extreme care should be taken when gathering training data as these decisions will have a very big impact on the final model once trained. In general, the reproducible workflow demonstrated here can be applied to any similar materials data set to make reliable model-agnostic predictions of how the structural characteristics and individual structures contribute to the prediction of functional properties

Inverse Design of MXenes for High-Capacity Energy Storage Materials Using Multi-Target Machine Learning

There is significant interest in discovering high-capacity battery materials, prompting the investigation of the electrochemical energy storage potential of the two-dimensional early transition metal carbides known as MXenes. Predicting the relationship between the composition of a MXene and electrochemical properties is a focus of considerable research. In this paper we classify the specific MXene chemical formula using a new categorical descriptor and simultaneously predict multiple target electrochemical properties. We then invert the design challenge and predict the formula for MXenes based on a set of battery performance criteria. This approach involves a workflow that includes multi-target regression and multi-target classification, focusing on the physicochemical features most pertinent to battery design. The final inverse model recommends Li2M2C and Mg2M2C (M = Sc, Ti, Cr) as candidates for more focused research, based on desirable ranges of gravimetric capacity, voltage, and induced charge

Surface Structure and Environment-Dependent Hydroxylation of the Nonpolar Hematite (100) from Density Functional Theory Modeling

Hematite (α-Fe2O3) nanoparticles are typically synthesized, stored, or used in hydrous environments, and the mineral/water interfaces are important for the surface stability and reactivity of these nanoparticles. Under such conditions the exposed facets are often passivated by hydroxyl groups. The configurations of surface hydroxylation vary with environmental conditions and affect the morphology and surface chemistry. Among the low-index hematite surfaces, the {100} are the only nonpolar surfaces and are often present on nanorods or nanotubes elongated along the [001] direction. In this paper we explore the relaxation and hydroxylation of this surface using first principles thermodynamics. Our results reveal that depending on the supersaturation of water and oxygen, various extents of hydroxylation may appear. In humid or hydrous environments, undercoordinated subsurface oxygen atoms are hydrogenated. In water singly and doubly coordinated hydroxyl groups coexist with chemisorbed water molecules at the surfaces. In environments where the humidity is reduced, the surface is terminated exclusively by doubly coordinated hydroxyl groups. The clean surface occurs when the humidity is further reduced or when temperature is elevated. On the basis of these findings, we have constructed the surface phase diagrams to describe the thermodynamic stability for two different temperatures. The phase diagrams enable us to predict the density and type of hydroxylation, which is relevant to surface stability, reactivity, and catalytic properties in hydrous or humid environments

Geometrical Properties Can Predict CO₂ and N₂ Adsorption Performance of Metal–Organic Frameworks (MOFs) at Low Pressure

Metal–organic frameworks (MOFs) are nanoporous materials with exceptional host–guest properties poised for groundbreaking innovations in gas separation applications according to high-throughput (HT) screening data. However, MOF structural libraries are nearly infinite in practice and so statistical and information technology will play a fundamental role in implementing and rationalizing MOF virtual screening. In this work, we apply <i>k</i>-means clustering and archetypal analysis (AA) to identify the truly significant nanoporous structures in a large library of ∼82 000 virtual MOFs. Quantitative structure–property relationship (QSPR) models of the theoretical CO₂ and N₂ uptake capacities were also developed using a calibration set of ∼16 000 hypothetical MOF structures derived from the prototypes and archetype frameworks. Since uptake capacities correlated poorly to the void fraction, surface area and pore size but these properties were used to build binary classifier predictors that successfully identify “high-performing” nanoporous materials in an external test set of ∼65 000 MOFs with accuracy higher than 94%. The accuracy of the classification decreased for MOFs with fluorine substituents. The classification models can serve as efficient filtering tools to detecting promising high-performing candidates at the early stage of virtual high-throughput screening of novel porous materials

Thermodynamic Cartography and Structure/Property Mapping of Commercial Platinum Catalysts

The development of the next generation of nanosized heterogeneous catalysts requires precise control of the size, shape, and structure of individual components in a variety of chemical environments. Recent reports show that the density of catalytically active defects on Pt nanoparticles is intrinsically linked to performance, such as edges, corners, steps, and kinks, which may be introduced postsynthesis. To optimize the synthesis of nanoparticles decorated by these defects and to understand the structural stability of the final product, multiscale thermodynamic modeling has been used to predict the size and temperature dependence of these steps and to show how this directly relates to catalytic reactivity. The results show that relatively modest annealing can promote the formations of surface steps and kinks and can more than double the reactivity of particles at industrially relevant sizes

Thermodynamic Control of Halogen-Terminated Silicon Nanoparticle Morphology

The fabrication of silicon nanoparticles terminated with halogen species provides a convenient route to create readily functionalizable nanostructures; however, the relationship between formation conditions and the thermodynamic ground-state morphology of the nanoparticles thus formed remains poorly understood. In this work, we use density functional theory calculations to compute surface energies of silicon surfaces terminated with fluorine, chlorine, bromine, and iodine as a function of halogen chemical potential and hence we compute, via a nanomorphology model, the thermodynamically optimal morphology of halogen-terminated silicon nanoparticles. We predict a variety of optimal nanoparticle shapes consisting primarily of Si(100), Si(113), and Si(111) facets with varying terminations, and we demonstrate how control over morphology may be attained by controlling the chemical environment. Implications for the fabrication of nanoparticles with facet-selective reactivity are also discussed

Simultaneous Prediction and Optimization of Charge Transfer Properties of Graphene and Graphene Oxide Nanoflakes from Multitarget Machine Learning

Considerable effort is directed toward controlling the physicochemical structure of graphene and graphene oxide, but complex structure/property relationships are difficult to identify and utilize when the materials are multifunctional and the properties are correlated. In this study, we propose and demonstrate a workflow for predicting which structural features to use to tune correlated properties simultaneously. Highly accurate multitarget regressors predict the ionization potential and electron affinity of graphene and graphene oxide nanoflakes and report the most important structural features as a basis for ensemble filtering that reflects design decisions. To challenge the approach, multiobjective optimization was used to find filters that simultaneously lower the ionization potential by −0.5 eV and raise the electron affinity by 0.5 eV. We find that the diameter of graphene nanoflakes is the most useful structural feature of graphene but is superseded by the oxygen concentration and proximity to the edges in graphene oxide. Achieving our challenging design goal was not possible, but a significant and balanced shift in the properties (in the right directions) could be obtained and accompanied by improved quality and performance. This general approach could be used to predict filters and to guide experimental design to separate samples for specific applications