Discovery Of New Materials Research Articles

High-throughput simulations for insight into grain boundary structure-property relationships and other complex microstructural phenomena

High-throughput simulations can be a powerful tool in the discovery of new materials and behaviors. As part of a special issue on Rising Stars in Computational Materials Science, this article uses the work of the author to show how high-throughput simulations have had an impact in grain boundary structure-property relationships and other complex microstructural phenomena. The work demonstrates how new tools designed to analyze large datasets produced by high-throughput simulations are enabling comprehensive grain boundary structure-property relationships to be obtained. High-throughput simulations are also used to demonstrate the impact descriptive and inferential statistics has had in extracting key aspects of deformation in metallic glasses. Finally, several different examples are used to show a balanced approach between simulations designed to survey and simulations designed for detailed analysis. Together, the two approaches provide a comprehensive picture of the variety of behaviors that exist, while ensuring that the physics underlying the behaviors are thoroughly understood.

(Invited) In Quest of High Voltage Insertion Compounds for Li-Ion and Na-Ion Batteries

A salient aspect of research in batteries is the discovery of new materials or novel properties of existing compounds. In this quest, the traditional approach is to focus on archetype compounds in which a desirable property was first observed, stimulating further investigations. This has been the innovation pathway of many battery materials (e.g., LiCoO2, LiFePO4) which are found in commercial Li-ion batteries. Indeed, most Li-ion cathode materials belong to a handful of structural families: layered oxides, spinel, and olivine. Similarly, many good Li-ion solid electrolytes crystalize in a few crystal structures: perovskites, NASICON-like, LISICON-like, garnets, and argyrodite. Yet this trial-and-error exploratory research based on extrapolating known solutions to new compositional spaces has high demands in terms of synthesis and characterization times that hinder the emergence of new, disruptive compounds. In this contribution we will present the strategies, using high-throughput bond-valence calculations and machine learning, that have allowed us to develop new high-voltage materials for the Li-ion and Na-ion batteries. Following this approach our recent results related to the nitridophosphate family (Na3TM(PO3)3N), where N assists an inductively induced high redox potential, will be shown. Within this family, Na3V(PO3)3N has been found to be the Na-ion cathode material with the highest operation voltage for the VIV+/VIII+ redox couple together with Na7V3(P2O7)4. Strategies to further lower the cost and improve the safety of these systems will be shown.

Printing megalibraries

Discovery of new materials has been traditionally based on a trial-and-error approach. Now, miniaturized nanoreactors have been developed for the synthesis and analysis of nanoparticle megalibraries of different size and composition.

Laboratory-Based X-ray Absorption Spectroscopy on a Working Pouch Cell Battery at Industrially-Relevant Charging Rates

Li-ion battery (LIB) research has continuing importance for the entire range of applications from consumer products to vehicle electrification and grid stabilization. In many cases, standard electrochemical methods only provide an overall voltage or specific capacity, giving an inadequate description of parallel redox processes or chemical gradients at the particle and pack level. X-ray absorption fine structure (XAFS) is frequently used to augment bulk electrochemical data, as it provides element-specific changes in oxidation state and local atomic structure. Such microscopic descriptors are crucial for elucidating charge transfer and structural changes associated with bonding or site mixing, two key factors in evaluating state of charge and modes of cell failure. However, the impact of XAFS on LIB research has been significantly constrained by a logistical barrier: contemporary XAFS work is performed almost exclusively at synchrotron X-ray light sources, where beamtime is infrequent and experiment time-frames are limited. Here we show that modern laboratory-based XAFS cannot only be applied to, e.g., characterization of ex situ LIB electrode materials, but can also be used for operando studies at industrially-relevant charging rates in a standard pouch cell preparation. Such capability enables accelerated discovery of new materials and improved operation modes for LIBs.

Machine Learning Constrained with Dimensional Analysis and Scaling Laws: Simple, Transferable, and Interpretable Models of Materials from Small Datasets

Machine learning (ML) from materials databases can accelerate the design and discovery of new materials through the development of accurate, computationally inexpensive models to predict materials properties. These models in turn enable rapid screening of large materials search space. However, materials datasets describing functional properties are typically small, which creates challenges pertaining to interpretability and transferability when exploring them with conventional ML approaches. Further, correlations within the dataset can lead to instability (nonunique functional models relating inputs to outputs) and overfitting. In this work, we address these issues by developing a new approach, in which ML with the Bootstrapped projected gradient descent algorithm is constrained with Buckingham Pi theorem-based dimensional analysis and scaling laws of relationships between different input descriptors (properties). This constrained learning model enables us to learn from small data and develop predictive m...

Eu3+-doped Sr2(Al1−xMgx)(Al1−xSi1+x)O7 phosphors: electronic, crystal structures and photoluminescence properties

The union of chemical compositions substitution and spectral controlling is of importance for the discovery of new materials or the implementation of properties optimization. In this work, we employed an effective strategy to regulate crystal structures by chemical unit co-substitution. We applied this strategy to Eu3+-doped Sr2(Al1−xMgx)(Al1−xSi1+x)O7 (0 ≤ x ≤ 1) solid solution phosphor, which was successfully synthesized by high temperature solid state reaction. The crystal structure remained the same group, P-421m, with controlling chemical compositions. The excitation peaks shifted between 263 and 270 nm and emission peaks shifted between 612 and 614 nm with a decreasing Stocks shift in overall tendency. The shift trend was clarified by Crystal Field Theory. The energy band structure and density of states of Sr2Al2SiO7 and Sr2MgSi2O7 were calculated by Density Functional Theory using the generalized gradient approximation. The band gap was also analyzed by diffuse reflectance spectrum as a contrast. The morphology was characterized by field emission scanning electron microscopy. Furthermore, the photoluminescence color of phosphors could be tuned from yellow to orange.

IEEE Journal on Exploratory Solid-State Computational Devices and Circuits—Volume 4, No. 2

Welcome to the fourth volume, second semiannual issue of the IEEE J ournal on Exploratory Solid-State Computational Devices and Circuits (JXCDC) a multidisciplinary, open access IEEE journal that is focused on publishing seminal research in the exploration for energy-efficient computing based on physics and materials to enable new devices, circuits, and architecture that will be of great interest to integrated circuit researchers and those working in the IT industry. The papers in the journal are selectively chosen to provide insight into the architectural, circuit, and device implications of emerging quantum nanoelectronic and nanomagnetic device technologies. Discovery of new materials, devices, and circuits for energy-efficient computational circuits will be needed to enable Moore’s law to continue for computing beyond the end of the roadmap for CMOS technologies, by enabling a significant improvement in energy efficiency and cost per function.

IEEE Magnetics Society Distinguished Lecturers for 2019

The magnetocaloric effect, that is, the reversible temperature change experienced by a magnetic material upon the application or removal of a magnetic field, has become a topic of increasing research interest due to its potential application in refrigeration at ambient temperature that is energy efficient and environmentally friendly [1] . From a technological point of view, the improvement of magnetic refrigeration systems can have a notable impact on society: A large fraction of the electricity consumed in residential and commercial markets is used for temperature and climate control. From the point of view of magnetic materials, research on this topic mainly focuses on the discovery of new materials with lower cost and enhanced performance. In addition, the characterization of the magnetocaloric effect can be used for more fundamental studies of the characteristics of phase transitions.

Two-Dimensional, Ordered, Double Transition Metal Carbides (MXenes): A New Family of Promising Catalysts for the Hydrogen Evolution Reaction

Generation of hydrogen by splitting water with the electrocatalytic approach could become a more sustainable way following the discovery of new materials, such as the 2D transition-metal carbides. Developing eco-friendly, low-cost, stable, and highly active nonprecious hydrogen evolution reaction (HER) catalysts is one of key factors for hydrogen energy economy. Two-dimensional metal carbide and nitride (MXenes) materials have shown characteristics of promising HER catalysts. Herein, we explored the conductive and thermal stability and electrocatalyst performance of four 2D ordered double MXenes M2′M″C2, Cr2TiC2, Cr2VC2, Mo2TiC2, and Mo2VC2, and their corresponding oxygen (O*)- or hydroxyl (OH*)-terminated MXenes by using density functional calculations. Results indicated that all the above MXenes are conductive, which are favored to charge transfer during HER. Four MXenes are fully terminated by O* under standard conditions [pH = 0, p(H2) = 1 bar, U = 0 V]. The Gibbs free energy for the adsorption of ato...

Characterization of a Novel Zinc Phosphate – Germanate Oxide System Doped with Erbium Ions

There is considerable interest in the discovery of new materials with applications in medicine. In this regard, a new germanium-zinc-phosphate oxide system with different contents of paramagnetic erbium ions was prepared and studied by X-ray diffraction and density and magnetic measurements. The structural and magnetic properties of the studied system were discussed as a function of the erbium ions content of samples. A crystalline ErPO4 phase appeared in the system for concentrations exceeding 3 mol% Er2O3. This structural change influences the density and Poisson’s ratio values. The magnetic susceptibility measurements suggest that the studied samples obey the Curie law.

Accelerated discovery of nanomaterials using computer simulation

Next-generation nanotechnology demands new materials and devices that are highly efficient, multifunctional, cost-effective and environmental friendly. The need to accelerate the discovery of new materials therefore becomes more pressing than ever. Over the past two decades, self-assembly techniques have provided a promising means for fabricating nanomaterials, where the underlying structures are formed by the self-organization of building blocks, such as nanoparticles, colloids and block copolymers, in a similar fashion to biological systems. The fundamental challenges to these bottom techniques are to design suitable assembling units, to tailor their interaction rules and to identify possible assembly pathways. In this report, we will demonstrate how computer simulation has been a powerful tool for tackling these fundamental challenges, providing not only profound insights into the complex interplay between the building blocks’ geometry and their interactions, but also valuable predictions to inspire on-going and future experiment. Theoretical background of self-assembly studies; simulation methods and data analysis tools commonly used will also be discussed.

Progress toward Solid State Synthesis by Design

Ages of history are defined by the underlying materials that promoted human development: stone, bronze, and iron ages. Since the middle of the last century, humanity has lived in a silicon age, where the development of the transistor ushered in new technologies previously thought inconceivable. But as technology has advanced, so have the requirements for new materials to sustain increasing physical demands. The field of solid state chemistry is dedicated to the discovery of new materials and phenomena, and though most materials discoveries in history have been through serendipity rather than careful reaction design, the last few decades have seen an increase in the number of materials discovered through a consideration of chemical reaction kinetics and thermodynamics. Materials by design have changed the way solid state chemists approach the synthesis of possible materials with interesting and useful properties. Unlike other chemistry subfields such as organic chemistry and biochemistry, solid state chemistry does not currently benefit from a toolbox of reactions that can allow for the synthesis of any arbitrary material. The diversity and complexity of the solid state phase space likely inhibits chemists from ever having such a toolbox. However, a thorough understanding of the various synthetic techniques involved in the synthesis of stable and metastable solids may be realized through an understanding of the reaction kinetics and thermodynamics. In the Account, we review the common synthesis techniques involved in the formation of metastable materials and break down their underlying chemistry to the simplest reaction mechanisms involved. The synthesis reactions of most metastable materials can be understood through these three reaction driving parameters, which include the exploitation of Le Chatelier's principle, thermo-kinetic reaction coupling, and lowering the activation energy of formation of the metastable product, and we identify several materials whose syntheses are described either by one or a combination of these driving parameters. We identify what exists at the frontier of materials discovery by design, including novel applications of supercritical fluids for tuning between "gas" and "solvent"-like environments. While conventional solvation requires changes in either the temperature or composition of the system, supercritical fluid solvation requires only changes in the fluid density, which opens up the possibilities for the synthesis of new materials. Most importantly, however, we look toward the future of materials synthesis by design and see that it must be a collaborative one. At present, chemists design materials using knowledge about chemical structure and reactivity but often target specific materials with very specific properties. In contrast, computational chemists perform calculations on millions of different elemental combinations and find many candidates of possible materials with interesting properties, though most of these are not realizable synthetically due to limitations in reactivity, kinetics, or thermodynamics. Synthetic harmony can be achieved through active collaboration and communication between these two subfields of chemistry, such that new calculations can incorporate complete knowledge about reaction kinetics and thermodynamics, and new syntheses target computationally predicted materials derived from an understanding of mapped reaction landscapes.

The Use of Cluster Expansions To Predict the Structures and Properties of Surfaces and Nanostructured Materials.

The construction of cluster expansions parametrized by first-principles calculations is a powerful tool for calculating properties of materials. In this Perspective, we discuss the application of cluster expansions to surfaces and nanomaterials. We review the fundamentals of the cluster expansion formalism and how machine learning is used to improve the predictive accuracy of cluster expansions. We highlight several representative applications of cluster expansions to surfaces and nanomaterials, demonstrating how cluster expansions help researchers build structure-property relationships and enable rational design to accelerate the discovery of new materials. Potential applications and future challenges of cluster expansions are also discussed.

A Transfer Learning Approach for Microstructure Reconstruction and Structure-property Predictions

Stochastic microstructure reconstruction has become an indispensable part of computational materials science, but ongoing developments are specific to particular material systems. In this paper, we address this generality problem by presenting a transfer learning-based approach for microstructure reconstruction and structure-property predictions that is applicable to a wide range of material systems. The proposed approach incorporates an encoder-decoder process and feature-matching optimization using a deep convolutional network. For microstructure reconstruction, model pruning is implemented in order to study the correlation between the microstructural features and hierarchical layers within the deep convolutional network. Knowledge obtained in model pruning is then leveraged in the development of a structure-property predictive model to determine the network architecture and initialization conditions. The generality of the approach is demonstrated numerically for a wide range of material microstructures with geometrical characteristics of varying complexity. Unlike previous approaches that only apply to specific material systems or require a significant amount of prior knowledge in model selection and hyper-parameter tuning, the present approach provides an off-the-shelf solution to handle complex microstructures, and has the potential of expediting the discovery of new materials.

Inverse molecular design using machine learning: Generative models for matter engineering.

The discovery of new materials can bring enormous societal and technological progress. In this context, exploring completely the large space of potential materials is computationally intractable. Here, we review methods for achieving inverse design, which aims to discover tailored materials from the starting point of a particular desired functionality. Recent advances from the rapidly growing field of artificial intelligence, mostly from the subfield of machine learning, have resulted in a fertile exchange of ideas, where approaches to inverse molecular design are being proposed and employed at a rapid pace. Among these, deep generative models have been applied to numerous classes of materials: rational design of prospective drugs, synthetic routes to organic compounds, and optimization of photovoltaics and redox flow batteries, as well as a variety of other solid-state materials.

Polymer Genome: A Data-Powered Polymer Informatics Platform for Property Predictions

The recent successes of the Materials Genome Initiative have opened up new opportunities for data-centric informatics approaches in several subfields of materials research, including in polymer science and engineering. Polymers, being inexpensive and possessing a broad range of tunable properties, are widespread in many technological applications. The vast chemical and morphological complexity of polymers though gives rise to challenges in the rational discovery of new materials for specific applications. The nascent field of polymer informatics seeks to provide tools and pathways for accelerated property prediction (and materials design) via surrogate machine learning models built on reliable past data. We have carefully accumulated a data set of organic polymers whose properties were obtained either computationally (bandgap, dielectric constant, refractive index, and atomization energy) or experimentally (glass transition temperature, solubility parameter, and density). A fingerprinting scheme that capt...

Computational design of bimetallic core-shell nanoparticles for hot-carrier photocatalysis

Computational design can accelerate the discovery of new materials with tailored properties, but applying this approach to plasmonic nanoparticles with diameters larger than a few nanometers is challenging as atomistic first-principles calculations are not feasible for such systems. In this paper, we employ a recently developed material-specific approach that combines effective mass theory for electrons with a quasistatic description of the localized surface plasmon to identify promising bimetallic core-shell nanoparticles for hot-electron photocatalysis. Specifically, we calculate hot-carrier generation rates of 100 different core-shell nanoparticles and find that systems with an alkali-metal core and a transition-metal shell exhibit high figures of merit for water splitting and are stable in aqueous environments. Our analysis reveals that the high efficiency of these systems is related to their electronic structure, which features a two-dimensional electron gas in the shell. Our calculations further demonstrate that hot-carrier properties are highly tunable and depend sensitively on core and shell sizes. The design rules resulting from our work can guide experimental progress towards improved solar energy conversion devices.

Large-Scale Computational Screening of Molecular Organic Semiconductors Using Crystal Structure Prediction

Predictive computational methods have the potential to significantly accelerate the discovery of new materials with targeted properties by guiding the choice of candidate materials for synthesis. Recently, a planar pyrrole-based azaphenacene molecule (pyrido[2,3-b]pyrido[3′,2′:4,5]pyrrolo[3,2-g]indole, 1) was synthesized and shown to have promising properties for charge transport, which relate to stacking of molecules in its crystal structure. Building on our methods for evaluating small-molecule organic semiconductors using crystal structure prediction, we have screened a set of 27 structural isomers of 1 to assess charge mobility in their predicted crystal structures. Machine-learning techniques are used to identify structural classes across the landscapes of all molecules and we find that, despite differences in the arrangement of hydrogen bond functionality, the predicted crystal structures of the molecules studied here can be classified into a small number of packing types. We analyze the predicted p...

Crystal Structure Prediction via Deep Learning.

We demonstrate the application of deep neural networks as a machine-learning tool for the analysis of a large collection of crystallographic data contained in the crystal structure repositories. Using input data in the form of multiperspective atomic fingerprints, which describe coordination topology around unique crystallographic sites, we show that the neural-network model can be trained to effectively distinguish chemical elements based on the topology of their crystallographic environment. The model also identifies structurally similar atomic sites in the entire data set of ∼50000 crystal structures, essentially uncovering trends that reflect the periodic table of elements. The trained model was used to analyze templates derived from the known crystal structures in order to predict the likelihood of forming new compounds that could be generated by placing elements into these structural templates in a combinatorial fashion. Statistical analysis of predictive performance of the neural-network model, which was applied to a test set of structures never seen by the model during training, indicates its ability to predict known elemental compositions with a high likelihood of success. In ∼30% of cases, the known compositions were found among the top 10 most likely candidates proposed by the model. These results suggest that the approach developed in this work can be used to effectively guide the synthetic efforts in the discovery of new materials, especially in the case of systems composed of three or more chemical elements.

Current and emerging practices of CALPHAD toward the development of high entropy alloys and complex concentrated alloys

Abstract