Computational Materials Design Research Articles

An AI-driven multiscale methodology to develop transparent wood as sustainable functional material by using the SSbD concept

Efficient design, production, and optimization of new safe and sustainable by design materials for various industrial sectors is an on-going challenge for our society, poised to escalate in the future. Wood-based composite materials offer an attractive sustainable alternative to high impact materials such as glass and polymers and have been the focus of experimental research and development for years. Computational and AI-based materials design provides significant speed-up the development of these materials compared to traditional methods of development. However, reliable numerical models are essential for achieving this goal. The AI-TranspWood project, recently funded by the European Commission, has the ambition to develop such computational and AI-based tools in the context of transparent wood (TW), a promising composite with potential applications in various industrial fields. In this project we advance the development specifically by using an Artificial Intelligence (AI)-driven multiscale methodology.

Exploration of novel boron nitride polymorphs: High-throughput screening combined with multi-task orbital crystal graph convolutional neural network (MT-OCGCN)

In response to the limitations of traditional material property prediction methods, we have developed a Multi-task Orbital Crystal Graph Convolutional Neural Network (MT-OCGCN). This innovative model allows for sharing parameters and computational resources, significantly improving prediction efficiency and accuracy. Experimental comparisons show that the model can achieve multi-task prediction with high accuracy for both strong and weak correlation properties, this offers an efficient approach for computational materials screening and design. Combined with high-throughput screening has resulted in the discovery of 13 new boron nitride polymorphs in the P21/c phase, among which 7 are characterized as wide direct band gap semiconductors. Furthermore, we have comprehensively analyzed the mechanical, thermal, and electronic properties of these new boron nitride polymorphs. It can be asserted that MT-OCGCN has the capability to precisely forecast the physical properties of novel structures that are absent from current databases.

Covalent Organic Frameworks in Computational Design of Second-Harmonic Generation Materials: Role of Tetrel Atoms and Their Interactions.

Modern approaches to the design of nonlinear optical materials often rely on computational techniques. Here, we discuss the effects of the variation in the center tetrel atoms, Tt = C, Si, or Ge, in a series of covalent organic frameworks of the COF-102 family. The effects of halogen substitution, Hal = Cl, Br, or I on intramolecular tetrel bonding are also discussed. The characteristics of the calculated electron density have been implemented to describe the features of the electron distribution around the central fragment involving a tetrahedral tetrel atom. The effect of the central Tt atom leads to a dramatic change in the character of electron delocalization on the Tt-Car bond with aromatic rings. The location of the halogen atom at the ortho-position of the aromatic ring leads to the formation of tetrel bonds, halogen bonds, or other noncovalent interactions. The changes in the second-order electric susceptibility χ(2) have been studied in order to describe the strength of nonlinear optical properties within the periodic couple-perturbed Kohn-Sham approach. A counterintuitive trend for the χ(2) decrease is observed upon substitution of H > Cl > Br > I at the ortho-position of the phenyl ring. This is due to the corresponding elongation of the Tt-Car bond.

Computational design of energy‐related materials: From first‐principles calculations to machine learning

AbstractEnergy‐related materials are crucial for advancing energy technologies, improving efficiency, reducing environmental impacts, and supporting sustainable development. Designing and discovering these materials through computational techniques necessitates a comprehensive understanding of the material space, which is defined by the constituent atoms, composition, and structure. Depending on the search space involved in the investigation, the computational materials design can be categorized into four primary approaches: atomic substitution in fixed prototype structures, crystal structure prediction (CSP), variable‐composition CSP, and inverse design across the entire materials space. This review provides an overview of these paradigms, detailing the concepts, strategies, and applications pertinent to energy‐related materials. The progression from first‐principles calculations to machine learning techniques is emphasized, with the aim of enhancing understanding and elucidating new advancements in computationally design of energy‐related materials.This article is categorized under: Structure and Mechanism > Computational Materials Science Data Science > Artificial Intelligence/Machine Learning Electronic Structure Theory > Density Functional Theory

Slip Opacity and Fast Osmotic Transport of Hydrophobes at Aqueous Interfaces with Two-Dimensional Materials.

We investigate the interfacial transport of water and hydrophobic solutes on van der Waals bilayers and heterostructures formed by stacking graphene, hBN, and MoS2 using extensive ab initio molecular dynamics simulations. We compute water slippage and the diffusio-osmotic transport coefficient of hydrophobic particles at the interface by combining hydrodynamics and the theory of the hydrophobic effect. We find that slippage is dominated by the layer that is in direct contact with water and only marginally altered by the second layer, leading to a so-called "slip opacity". The screening of the lateral forces, where the liquid does not feel the forces coming from the second nearest layer, is one of the factors leading to the "slip opacity" in our systems. The diffusio-osmotic transport of small hydrophobes (with a radius below 2.5 Å) is also affected by the slip opacity, being dramatically enhanced by slippage. Furthermore, the direction of diffusio-osmotic flow is controlled by the solute size, with the flow in the opposite direction of the concentration gradient for smaller hydrophobes, and vice versa for larger ones. We connect our findings to the wetting properties of two-dimensional materials, and we propose that slippage and wetting can be controlled separately: whereas the slippage is mostly determined by the layer in closer proximity to water, wetting can be finely tuned by stacking different two-dimensional materials. Our study advances the computational design of two-dimensional materials and van der Waals heterostructures, enabling precise control over wetting and slippage properties for applications in coatings and water purification membranes.

Interrelationship between Different Aspect of Materials for Low Temperature Water Splitting Technologies

One of challenging aspects of developing novel functional materials for water splitting technologies (or computational materials design in general) is that there are usually correlation between different aspect of material properties that are both relevant for the device performance, such as activity of catalyst and its stability, therefore focusing only on one (or limited number of) aspect does not necessary lead to satisfactory outcome as such a correlation tend to be detrimental in nature with small number of exception [1,2,3]. In this presentation, we will review our effort in understanding various cases of such conflicting conditions, in screening catalyst (stability vs activity[4]), in identifying materials synthesis condition (presence of competing phases[5,6]), in understanding the effect of operation conditions (ex. reaction vs diffusion of intermediates[7,8]/operation condition vs transport[9,10]). We will discuss possible strategies that could help us deconvoluting such correlating factors so as to accelerating materials discovery/development for low temperature water splitting technologies and beyond [11]. Y. Xiao et al., Nano Letters 22, 2236 (2022)G Zeng et al., Nature Mater. 20, 1130 (2021)Y. Liu et al., Nature Ener. 2, 1 (2017)W.I Choi et al., Adv. Ener. Mater. 5, 1501423 (2015)N. Gaillard et al., Mater. Adv. 2, 5752 (2021)J.A. Trindell et al., Chem. Mater. 34, 7712 (2022)J.C. Fornaciari et al., Electrochimica Acta 405, 139810 (2022)Z. Zou et al., ACS App. Ener. Mater. DOI:10.1021/acsaem.3c02136 (2023)A.J.E. Rowberg et al., Mater. Adv. 4, 6233 (2023)A.J.E. Rowberg et al., Phys. Rev. Mater. 7, 015402 (2023)M.D. Witman et al., Nature Comp. Sci. 3, 675 (2023)

Interscalable material microstructure organization in performance-based computational design

Various parameters can be integrated in material-based computational design in architecture. Materials are the main driver of these processes and evaluated with the constraints related to the form, performance, and fabrication techniques. However, current methodologies mostly involve investigating already existing materials. Studies on computational material design, in which new materials are developed by designing their microstructures in response to the performative issues, are generally undertaken at the material scale, and not adopted to the architectural design process yet. To resolve this issue, the methodology titled Interscalable Material Microstructure Organization in Performance-based Computational Design (I2MO_PCD) is developed and presented in three stages, including (1) identification of different types of material microstructures, (2) computational material design, and (3) prototyping. Data-based material modelling and visualization, and algorithmic modelling techniques are utilized, followed by various performance simulations as a part of an iterative process. New microstructure organizations are designed computationally, organized under three main groups as linear-curvilinear, crystal and metaball-voronoi. The outcomes of different performance analyses, including structure, radiation, direct sun hours, acoustics and thermal bridge were compared. Thus, the role of geometrical organization of microstructures, scales and material types in different performance computations were identified, by designing and fabricating synthetic materials.

Machine Learning Prediction Models for Solid Electrolytes Based on Lattice Dynamics Properties.

Recently, machine-learning approaches have accelerated computational materials design and the search for advanced solid electrolytes. However, the predictors are currently limited to static structural parameters, which may not fully account for the dynamic nature of ionic transport. In this study, we meticulously curated features considering dynamic properties and developed machine-learning models to predict the ionic conductivity, σ, of solid electrolytes. We compiled 14 phonon-related descriptors from first-principles phonon calculations along with 16 descriptors related to the structure and electronic properties. Our logistic regression classifiers exhibit an accuracy of 93%, while the random forest regression model yields a root-mean-square error for log(σ) of 1.179 S/cm and R2 of 0.710. Notably, phonon-related features are essential for estimating the ionic conductivities in both models. Furthermore, we applied our prediction model to screen 264 Li-containing materials and identified 11 promising candidates as potential superionic conductors.

Local Ordering, Distortion, and Redox Activity in (La0.75Sr0.25)(Mn0.25Fe0.25Co0.25Al0.25)O3 Investigated by a Computational Workflow for Compositionally Complex Perovskite Oxides.

Mixing multiple cations can result in a significant configurational entropy, offer a new compositional space with vast tunability, and introduce new computational challenges. For applications such as the two-step solar thermochemical hydrogen (STCH) generation techniques, we demonstrate that using density functional theory (DFT) combined with Metropolis Monte Carlo method (DFT-MC) can efficiently sample the possible cation configurations in compositionally complex perovskite oxide (CCPO) materials, with (La0.75Sr0.25)(Mn0.25Fe0.25Co0.25Al0.25)O3 as an example. In the presence of oxygen vacancies (VO), DFT-MC simulations reveal a significant increase of the local site preference of the cations (short-range ordering), compared to a more random mixing without VO. Co is found to be the redox-active element and the VO is the preferentially generated next to Co due to the stretched Co-O bonds. A clear definition of the vacancy formation energy (Evf) is proposed for CCPO in an ensemble of structures evolved in parallel from independent DFT-MC paths. By combining the distribution of Evf with VO interactions into a statistical model, the oxygen nonstoichiometry (δ), under the STCH thermal reduction and oxidation conditions, is predicted and compared with the experiments. Similar to the experiments, the predicted δ can be used to extract the enthalpy and entropy of reduction using the van't Hoff method, providing direct comparisons with the experimental results. This procedure provides a full predictive workflow for using DFT-MC to obtain possible local ordering or fully random structures, understand the redox activity of each element, and predict the thermodynamic properties of CCPOs, for computational screening and design of these CCPO materials at STCH conditions.

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.

Quantum-to-classical modeling of monolayer Ge2Se2 and its application in photovoltaic devices.

Since the discovery of graphene in 2004, the unique properties of two-dimensional materials have sparked intense research interest regarding their use as alternative materials in various photonic applications. Transition metal dichalcogenide monolayers have been proposed as transport layers in photovoltaic cells, but the promising characteristics of group IV-VI dichalcogenides are yet to be thoroughly investigated. This manuscript reports on monolayer Ge2Se2 (a group IV-VI dichalcogenide), its optoelectronic behavior, and its potential application in photovoltaics. When employed as a hole transport layer, the material fosters an astonishing device performance. We use ab initio modeling for the material prediction, while classical drift-diffusion drives the device simulations. Hybrid functionals calculate electronic and optical properties to maintain high accuracy. The structural stability has been verified using phonon spectra. The E-k dispersion reveals the investigated material's key electronic properties. The calculations reveal a direct bandgap of 1.12 eV for monolayer Ge2Se2. We further extract critical optical parameters using the Kubo-Greenwood formalism and Kramers-Kronig relations. A significantly large absorption coefficient and a high dielectric constant inspired the design of a monolayer Ge2Se2-based solar cell, exhibiting a high open circuit voltage of V oc = 1.11 V, a fill factor of 87.66%, and more than 28% power conversion efficiency at room temperature. Our findings advocate monolayer Ge2Se2 for various optoelectronic devices, including next-generation solar cells. The hybrid quantum-to-macroscopic methodology presented here applies to broader classes of 2D and 3D materials and structures, showing a path to the computational design of future photovoltaic materials.

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.