Computational Materials Design Research Articles

Phononic origin of the infrared dielectric properties of RE2O3 (RE = Y, Gd, Ho, Lu) compounds

Understanding the phononic origin of the infrared (IR) dielectric properties of yttria (Y2O3) and other rare-earth sesquioxides (RE2O3) is a fundamental task in the search of appropriate RE2O3 materials that serve particular IR optical applications. We herein investigate the IR dielectric properties of RE2O3 (RE = Y, Gd, Ho, Lu) using density functional theory-based phonon calculations and Lorentz oscillator model. The abundant IR-active optical phonon modes that are available for effective absorption of photons result in high reflectance of RE2O3, among which four IR-active modes originated from large distortions of REO6 octahedra are found to contribute dominantly to the phonon dielectric constants. Particularly, the present calculation method by considering one-phonon absorption process is demonstrated with good reliability in predicting the IR dielectric parameters of RE2O3 at the far-IR as well as the vicinity of mid-IR region, and the potential cutoff frequency/wavelength of its applicability is disclosed as characterized by the maximum frequency of IR-active longitudinal phonon modes. The results deepen the understanding on IR dielectric properties of RE2O3, and aid the computational design of materials with appropriate IR properties.

The success of computational material design for sustainable energy catalysis

Computational material design (CMD) employs quantum mechanical simulations, density functional theory, and machine learning techniques to correlate electronic structural attributes with physical and chemical properties of materials. Over the last decade, CMD has proven to be critical to the advancement of materials science and a variety of engineering fields. This contribution provides an overview of CMD’s success in driving materials discovery for catalysis in the context of sustainable energy applications. Specifically, we discuss how CMD has enabled the development of catalysts for three electrochemical processes that are critical to sustainable energy applications, oxygen reduction reactions, water oxidation reactions, and CO2 reduction reactions. We illustrate how CMD provides a powerful and efficient method for understanding underlying reaction mechanisms as well as predicting and optimizing catalyst properties. Furthermore, we demonstrate how this strategic approach has enabled researchers to effectively navigate the vast chemical space of potential catalysts and rapidly identify novel materials possessing desirable electronic structures and catalytic activity.

Computational Design of Inorganic Solid-State Electrolyte Materials for Lithium-Ion Batteries

Computational Design of Inorganic Solid-State Electrolyte Materials for Lithium-Ion Batteries

AFLOW-CCE for the thermodynamics of ionic materials.

Accurate thermodynamic stability predictions enable data-driven computational materials design. Standard density functional theory (DFT) approximations have limited accuracy with average errors of a few hundred meV/atom for ionic materials, such as oxides and nitrides. Thus, insightful correction schemes as given by the coordination corrected enthalpies (CCE) method, based on an intuitive parametrization of DFT errors with respect to coordination numbers and cation oxidation states, present a simple, yet accurate solution to enable materials stability assessments. Here, we illustrate the computational capabilities of our AFLOW-CCE software by utilizing our previous results for oxides and introducing new results for nitrides. The implementation reduces the deviations between theory and experiment to the order of the room temperature thermal energy scale, i.e., ∼25 meV/atom. The automated corrections for both materials classes are freely available within the AFLOW ecosystem via the AFLOW-CCE module, requiring only structural inputs.

Deep Learning on Atomistic Physical Fields of Graphene for Strain and Defect Engineering

Strain and defect engineering have profound applications in two‐dimensional materials, where it is important to determine the equilibrated atomistic structures with defect conditions under mechanical deformations for computational materials design. Nevertheless, how to efficiently predict relaxed atomistic structures and the associated physical fields on each atom or bond under evolving mechanical deformations remains as an essential challenge. To address this issue, a deep neural network architecture is designed to embed the state of applied strains into the defect‐engineered atomistic geometry, so that deformation‐coupled physical fields of interests on atoms or bonds can be predicted or derived over continuous state of deformations. For demonstration, the combination of applied tensile strains and shear strain on monolayer graphene with random distribution of Stone–Wales defects and vacancy defects is considered. The unique advantage of this framework is the development of strain‐embedding concept combined with generative adversarial network, which can be feasibly extended to other material and other conditions. The computational approach sheds light on boosting the efficiency of evaluating physical properties of 2D materials under complex strain and defect states.

The design and optimization of heterogeneous catalysts using computational methods

Computational design of catalytic materials is a high dimensional structure optimization problem that is limited by the bottleneck of expensive quantum computation tools. An illustration of interaction of different factors involved in the design and optimization of a catalyst.

High-Temperature Polymer Dielectrics Designed Using an Invertible Molecular Graph Generative Model.

Generating new molecules with the desired physical or chemical properties is the key challenge of computational material design. Deep learning techniques are being actively applied in the field of data-driven material informatics and provide a promising way to accelerate the discovery of innovative materials. In this work, we utilize an invertible graph generative model to generate hypothetical promising high-temperature polymer dielectrics. A molecular graph generative model based on the invertible normalizing flow is trained on a data set containing 250k polymer molecular graphs (mostly generated by an RNN-based generative model) to learn the invertible transformations between latent distributions and molecular graph structures. When generating molecular graphs, a sample vector is drawn from the latent space, and then an adjacency tensor and node attribute matrix are generated through two invertible flows in two steps and assembled into a molecular graph. The model has the merits of exact likelihood training and an efficient one-shot generation process. The learned latent space is used to generate polymers with a high glass-transition temperature (Tg) and a wide band gap (Eg) for the application of high-temperature energy storage film capacitors. This work contributes to the efficient design of high-temperature polymer dielectrics by using deep generative models.

(Invited) First-Principles Electrochemistry Beyond Grand-Canonical DFT

Electrochemistry underpins a significant share of modern energy conversion, energy storage and chemical synthesis technologies. Computational design of electrode, catalyst and electrolyte materials has primarily relied on approximate trends from first-principles density-functional theory calculations: the time and length scales involved in electrochemical processes are extremely challenging to capture in quantum-mechanical calculations. This leads to two key challenges on the path to first-principles electrochemistry: (1) including liquid and electrolyte distribution effects on reaction energetics, and (2) predicting correct charge states of reacting species adsorbed on electrode surfaces. To address these challenges, we incorporate classical statistical models of liquids and electrolytes within quantum simulations of electrochemical interfaces using joint density-functional theory. We have also developed grand-canonical electronic structure methods to realistically simulate electrochemical processes by automatically equilibrating charge states at a fixed electrochemical potential. After showcasing the need for such techniques with representative examples, I will point out open questions in the accuracy of both electronic structure and solvation effects, and introduce the BEAST collaboration targeting Beyond-DFT Electrochemistry with Accelerated and Solvated Techniques.

Computational design of promising 2D electrode materials for Li-ion and Li–S battery applications

Computational design of promising 2D electrode materials for Li-ion and Li–S battery applications

Genomic materials design: CALculation of PHAse Dynamics

The CALPHAD system of fundamental phase-level databases, now known as the Materials Genome, has enabled a mature technology of computational materials design and qualification that has already met the acceleration goals of the national Materials Genome Initiative. As first commercialized by QuesTek Innovations, the methodology combines efficient genomic-level parametric design of new material composition and process specifications with multidisciplinary simulation-based forecasting of manufacturing variation, integrating efficient uncertainty management. Recent projects demonstrated under the multi-institutional CHiMaD Design Center notably include novel alloys designed specifically for additive manufacturing. With the proven success of the CALPHAD-based Materials Genome technology, current university research emphasizes new methodologies for affordable accelerated expansion of more accurate CALPHAD databases. Rapid adoption of these new capabilities by US apex corporations has compressed the materials design and development cycle to under 2 years, enabling a new “materials concurrency” integrated into a new level of concurrent engineering supporting an unprecedented level of manufacturing innovation.

Computational Materials Design for Ceramic Nuclear Waste Forms Using Machine Learning, First-Principles Calculations, and Kinetics Rate Theory.

Ceramic waste forms are designed to immobilize radionuclides for permanent disposal in geological repositories. One of the principal criteria for the effective incorporation of waste elements is their compatibility with the host material. In terms of performance under environmental conditions, the resistance of the waste forms to degradation over long periods of time is a critical concern when they are exposed to natural environments. Due to their unique crystallographic features and behavior in nature environment as exemplified by their natural analogues, ceramic waste forms are capable of incorporating problematic nuclear waste elements while showing promising chemical durability in aqueous environments. Recent studies of apatite- and hollandite-structured waste forms demonstrated an approach that can predict the compositions of ceramic waste forms and their long-term dissolution rate by a combination of computational techniques including machine learning, first-principles thermodynamics calculations, and modeling using kinetic rate equations based on critical laboratory experiments. By integrating the predictions of elemental incorporation and degradation kinetics in a holistic framework, the approach could be promising for the design of advanced ceramic waste forms with optimized incorporation capacity and environmental degradation performance. Such an approach could provide a path for accelerated ceramic waste form development and performance prediction for problematic nuclear waste elements.

Thermodynamics and its prediction and CALPHAD modeling: Review, state of the art, and perspectives

Thermodynamics is a science concerning the state of a system, whether it is stable, metastable, or unstable, when interacting with its surroundings. The combined law of thermodynamics derived by Gibbs about 150 years ago laid the foundation of thermodynamics. In Gibbs combined law, the entropy production due to internal processes was not included, and the 2nd law was thus practically removed from the Gibbs combined law, so it is only applicable to systems under equilibrium, thus commonly termed as equilibrium or Gibbs thermodynamics. Gibbs further derived the classical statistical thermodynamics in terms of the probability of configurations in a system in the later 1800's and early 1900's. With the quantum mechanics (QM) developed in 1920's, the QM-based statistical thermodynamics was established and connected to classical statistical thermodynamics at the classical limit as shown by Landau in the 1940's. In 1960's the development of density functional theory (DFT) by Kohn and co-workers enabled the QM prediction of properties of the ground state of a system. On the other hand, the entropy production due to internal processes in non-equilibrium systems was studied separately by Onsager in 1930's and Prigogine and co-workers in the 1950's. In 1960's to 1970's the digitization of thermodynamics was developed by Kaufman in the framework of the CALculation of PHAse Diagrams (CALPHAD) modeling of individual phases with internal degrees of freedom. CALPHAD modeling of thermodynamics and atomic transport properties has enabled computational design of complex materials in the last 50 years. Our recently termed zentropy theory integrates DFT and statistical mechanics through the replacement of the internal energy of each individual configuration by its DFT-predicted free energy. The zentropy theory is capable of accurately predicting the free energy of individual phases, transition temperatures and properties of magnetic and ferroelectric materials with free energies of individual configurations solely from DFT-based calculations and without fitting parameters, and is being tested for other phenomena including superconductivity, quantum criticality, and black holes. Those predictions include the singularity at critical points with divergence of physical properties, negative thermal expansion, and the strongly correlated physics. Those individual configurations may thus be considered as the genomic building blocks of individual phases in the spirit of the materials genome®. This has the potential to shift the paradigm of CALPHAD modeling from being heavily dependent on experimental inputs to becoming fully predictive with inputs solely from DFT-based calculations and machine learning models built on those calculations and existing experimental data through newly developed and future open-source tools. Furthermore, through the combined law of thermodynamics including the internal entropy production, it is shown that the kinetic coefficient matrix of independent internal processes is diagonal with respect to the conjugate potentials in the combined law, and the cross phenomena that the phenomenological Onsager flux and reciprocal relationships are due to the dependence of the conjugate potential of a molar quantity on nonconjugate molar quantities and other potentials, which can be predicted by the zentropy theory and CALPHAD modeling.

Evaluating Residual Stress in Carbon Fiber-Reinforced Polymer (CFRP) at Microscale Using Fiber Push-Out Experiment and Finite Element Modeling.

Microscale residual stress may develop during the manufacturing of Carbon Fiber-Reinforced Polymer (CFRP) composites and negatively affect apparent macroscale mechanical properties. Accordingly, accurately capturing residual stress may be essential in computational methods used for composite material design. This work presents a new data-driven methodology for the evaluation of microscale residual stress in CFRPs using fiber push-out experiments with in situ scanning electron microscopy (SEM) imaging. SEM images reveal significant through-thickness matrix sink-in deformation in resin-rich areas after nearby fibers are pushed out, which is attributed to the release of microscale process-induced residual stress. The sink-in deformation is measured experimentally, and a Finite Element Model Updating (FEMU) method is used to retrieve the associated residual stress. The finite element (FE) analysis includes simulation of the curing process, test sample machining, and fiber push-out experiment. Significant out-of-plane matrix deformation larger than 1% of the specimen thickness is reported and associated with a high level of residual stress in resin-rich areas. This work emphasizes the importance of in situ data-driven characterization for integrated computational materials engineering (ICME) and material design.

Colloquium : Room temperature superconductivity: The roles of theory and materials design

For half a century after the discovery of superconductivity, materials exploration for better superconductors proceeded without knowledge of the underlying mechanism. The 1957 BCS theory cleared that up: the superconducting state occurs due to pairing of electrons over the Fermi surface. Over the following half century higher critical temperature T$_c$ was achieved only serendipitously as new materials were synthesized. Meanwhile the formal theory of phonon-coupled superconductivity at the material-dependent level became highly developed: given a known compound, its value of T$_c$, the superconducting gap function, and several other properties of the superconducting state became available independent of further experimental input. More recently, density functional theory based computational materials design has progressed to a predictive level -- new materials can be predicted on the basis of various numerical algorithms. Taken together, these capabilities enable theoretical prediction of new superconductors. Here the process that resulted in three new highest temperature superconductors, predicted numerically, confirmed experimentally -- SH$_3$, LaH$_{10}$, and YH$_9$ -- is recounted. These hydrides have T$_c$ in the 200-280K range at megabar pressures, and here the development will be chronicled. Current activities and challenges are discussed, together with Regularities in compressed hydrides that can guide further exploration.

Perspective on computational design of two-dimensional materials with robust multiferroic coupling

Two-dimensional (2D) multiferroic materials with robust magnetoelectric coupling and controllable topological solitons (such as skyrmions) are promising candidates for advanced information storage and processing. Due to the limitations of experimental techniques, first-principles investigations stand out in answering fundamental questions of 2D multiferroic couplings, thus providing guidance for experimental validation. Herein, we will give a review of recent theoretical progress in the exploration of 2D multiferroic coupling via structural design and molecular engineering approach. Particularly, we will focus on (i) how to design the multiferroic structure in the 2D form; (ii) how to achieve robust magnetoelectric coupling; and (iii) how to electrically control the magnetic skyrmion via multiferroic effects. Finally, we give some perspectives on the remaining challenges and opportunities for predicting 2D multiferroic materials.

Siloxane Molecules: Nonlinear Elastic Behavior and Fracture Characteristics.

Fracture phenomena in soft materials span multiple length and time scales. This poses a major challenge in computational modeling and predictive materials design. To pass quantitatively from molecular to continuum scales, a precise representation of the material response at the molecular level is vital. Here, we derive the nonlinear elastic response and fracture characteristics of individual siloxane molecules using molecular dynamics (MD) studies. For short chains, we find deviations from classical scalings for both the effective stiffness and mean chain rupture times. A simple model of a nonuniform chain of Kuhn segments captures the observed effect and agrees well with MD data. We find that the dominating fracture mechanism depends on the applied force scale in a nonmonotonic fashion. This analysis suggests that common polydimethylsiloxane (PDMS) networks fail at cross-linking points. Our results can be readily lumped into coarse-grained models. Although focusing on PDMS as a model system, our study presents a general procedure to pass beyond the window of accessible rupture times in MD studies employing mean first passage time theory, which can be exploited for arbitrary molecular systems.

Computational Design and Manufacturing of Sustainable Materials through First-Principles and Materiomics.

Engineered materials are ubiquitous throughout society and are critical to the development of modern technology, yet many current material systems are inexorably tied to widespread deterioration of ecological processes. Next-generation material systems can address goals of environmental sustainability by providing alternatives to fossil fuel-based materials and by reducing destructive extraction processes, energy costs, and accumulation of solid waste. However, development of sustainable materials faces several key challenges including investigation, processing, and architecting of new feedstocks that are often relatively mechanically weak, complex, and difficult to characterize or standardize. In this review paper, we outline a framework for examining sustainability in material systems and discuss how recent developments in modeling, machine learning, and other computational tools can aid the discovery of novel sustainable materials. We consider these through the lens of materiomics, an approach that considers material systems holistically by incorporating perspectives of all relevant scales, beginning with first-principles approaches and extending through the macroscale to consider sustainable material design from the bottom-up. We follow with an examination of how computational methods are currently applied to select examples of sustainable material development, with particular emphasis on bioinspired and biobased materials, and conclude with perspectives on opportunities and open challenges.

Computational design of self-assembling peptide chassis materials for synthetic cells

High-throughput screening based on molecular simulation, machine learning and black-box optimization identifies promising candidates for forming self-assembled peptide vesicles.

Neural Inverse Design of Nanostructures (NIDN)

In the recent decade, computational tools have become central in material design, allowing rapid development cycles at reduced costs. Machine learning tools are especially on the rise in photonics. However, the inversion of the Maxwell equations needed for the design is particularly challenging from an optimization standpoint, requiring sophisticated software. We present an innovative, open-source software tool called Neural Inverse Design of Nanostructures (NIDN) that allows designing complex, stacked material nanostructures using a physics-based deep learning approach. Instead of a derivative-free or data-driven optimization or learning method, we perform a gradient-based neural network training where we directly optimize the material and its structure based on its spectral characteristics. NIDN supports two different solvers, rigorous coupled-wave analysis and a finite-difference time-domain method. The utility and validity of NIDN are demonstrated on several synthetic examples as well as the design of a 1550 nm filter and anti-reflection coating. Results match experimental baselines, other simulation tools, and the desired spectral characteristics. Given its full modularity in regard to network architectures and Maxwell solvers as well as open-source, permissive availability, NIDN will be able to support computational material design processes in a broad range of applications.

Aflow++: A C++ framework for autonomous materials design

The realization of novel technological opportunities given by computational and autonomous materials design requires efficient and effective frameworks. For more than two decades, aflow++ (Automatic-Flow Framework for Materials Discovery) has provided an interconnected collection of algorithms and workflows to address this challenge. This article contains an overview of the software and some of its most heavily-used functionalities, including algorithmic details, standards, and examples. Key thrusts are highlighted: the calculation of structural, electronic, thermodynamic, and thermomechanical properties in addition to the modeling of complex materials, such as high-entropy ceramics and bulk metallic glasses. The aflow++ software prioritizes interoperability, minimizing the number of independent parameters and tolerances. It ensures consistency of results across property sets — facilitating machine learning studies. The software also features various validation schemes, offering real-time quality assurance for data generated in a high-throughput fashion. Altogether, these considerations contribute to the development of large and reliable materials databases that can ultimately deliver future materials systems.