Prediction Of Material Properties Research Articles

From density functional theory to machine learning predictive models for electrical properties of spinel oxides.

This work focuses on predicting and characterizing the electronic conductivity of spinel oxides, which are promising materials for energy storage devices and for the oxygen evolution and oxygen reduction reactions due to their attractive properties and abundance of transition metals that can act as active sites for catalysis. To this end, a new database was developed from first principles, including band structure and conductivity properties of spinel oxides, and machine learning algorithms were trained on this database to predict electronic conductivity and band gaps based solely on the compositions. The models developed in this study are scaled from the quantum level up to a continuum conductivity model. The relatively small database used in this study allowed for accurate predictions of band gap and conductivity. By altering the composition of spinel oxides, the model was able to predict high conductivity for spinels with high nickel content and to match experimental trends for manganese cobalt spinels. The ability to predict material properties is especially important in energy conversion devices such as batteries and supercapacitors where redox reactions take place.

Plantar pressure distribution using personalised 3D printed lattice insoles with distributed stiffness

This work presents a proof-of-concept study on the use of additive manufacturing for developing customised lattice topology insoles, with the aim of redistributing plantar pressures. Four cubic lattice structures were applied to five sections of the moulded insole contours, which were based in 3D foot scans using Ansys material designer to predict material properties. The variation of the elastic modulus across the sections was controlled via the lattice cell structure volume ratio. The insole lattice structures were made from elastic photosensitive liquid resin resulting in flexible parts. A commercially available shoe was used to compare pressure distribution between the original and customised insole. Plantar pressure distribution and peak pressures were measured during standing, walking, and running. Pressure redistribution varied between insole conditions. During standing, the customised insole reduced peak pressure at the left heel, while the right foot experienced increased peak pressure compared to the control insole. Substantial peak pressure reduction was observed on the left foot during walking and running whilst the reduction was much less for the right foot. The study highlights the potential of additive manufacturing for cost-effective, accurate, and personalized insole production, offering material control and pressure redistribution benefits for various applications like injury prevention and rehabilitation.

Uncertainty quantification in multivariable regression for material property prediction with Bayesian neural networks

With the increased use of data-driven approaches and machine learning-based methods in material science, the importance of reliable uncertainty quantification (UQ) of the predicted variables for informed decision-making cannot be overstated. UQ in material property prediction poses unique challenges, including multi-scale and multi-physics nature of materials, intricate interactions between numerous factors, limited availability of large curated datasets, etc. In this work, we introduce a physics-informed Bayesian Neural Networks (BNNs) approach for UQ, which integrates knowledge from governing laws in materials to guide the models toward physically consistent predictions. To evaluate the approach, we present case studies for predicting the creep rupture life of steel alloys. Experimental validation with three datasets of creep tests demonstrates that this method produces point predictions and uncertainty estimations that are competitive or exceed the performance of conventional UQ methods such as Gaussian Process Regression. Additionally, we evaluate the suitability of employing UQ in an active learning scenario and report competitive performance. The most promising framework for creep life prediction is BNNs based on Markov Chain Monte Carlo approximation of the posterior distribution of network parameters, as it provided more reliable results in comparison to BNNs based on variational inference approximation or related NNs with probabilistic outputs.

Material Property Prediction Using Machine Learning

The ability to accurately predict the properties of materials is crucial for numerous applications across various industries, including materials science, engineering, and manufacturing. With the advent of machine learning (ML) techniques, researchers have gained powerful tools to model and predict material properties based on their composition, structure, and processing conditions. This review paper provides a comprehensive overview of material property prediction using machine learning. It covers the historical development, available ML models, recent trends, and prospects in this rapidly evolving domain.

JARVIS-Leaderboard: a large scale benchmark of materials design methods

Lack of rigorous reproducibility and validation are significant hurdles for scientific development across many fields. Materials science, in particular, encompasses a variety of experimental and theoretical approaches that require careful benchmarking. Leaderboard efforts have been developed previously to mitigate these issues. However, a comprehensive comparison and benchmarking on an integrated platform with multiple data modalities with perfect and defect materials data is still lacking. This work introduces JARVIS-Leaderboard, an open-source and community-driven platform that facilitates benchmarking and enhances reproducibility. The platform allows users to set up benchmarks with custom tasks and enables contributions in the form of dataset, code, and meta-data submissions. We cover the following materials design categories: Artificial Intelligence (AI), Electronic Structure (ES), Force-fields (FF), Quantum Computation (QC), and Experiments (EXP). For AI, we cover several types of input data, including atomic structures, atomistic images, spectra, and text. For ES, we consider multiple ES approaches, software packages, pseudopotentials, materials, and properties, comparing results to experiment. For FF, we compare multiple approaches for material property predictions. For QC, we benchmark Hamiltonian simulations using various quantum algorithms and circuits. Finally, for experiments, we use the inter-laboratory approach to establish benchmarks. There are 1281 contributions to 274 benchmarks using 152 methods with more than 8 million data points, and the leaderboard is continuously expanding. The JARVIS-Leaderboard is available at the website: https://pages.nist.gov/jarvis_leaderboard/

Accelerating material property prediction using generically complete isometry invariants

Periodic material or crystal property prediction using machine learning has grown popular in recent years as it provides a computationally efficient replacement for classical simulation methods. A crucial first step for any of these algorithms is the representation used for a periodic crystal. While similar objects like molecules and proteins have a finite number of atoms and their representation can be built based upon a finite point cloud interpretation, periodic crystals are unbounded in size, making their representation more challenging. In the present work, we adapt the Pointwise Distance Distribution (PDD), a continuous and generically complete isometry invariant for periodic point sets, as a representation for our learning algorithm. The PDD distinguished all (more than 660 thousand) periodic crystals in the Cambridge Structural Database as purely periodic sets of points without atomic types. We develop a transformer model with a modified self-attention mechanism that combines PDD with compositional information via a spatial encoding method. This model is tested on the crystals of the Materials Project and Jarvis-DFT databases and shown to produce accuracy on par with state-of-the-art methods while being several times faster in both training and prediction time.

SA-GNN: Prediction of material properties using graph neural network based on multi-head self-attention optimization

With the development of science and technology and the improvement of hardware computing power, the application of large models in the field of artificial intelligence (AI) has become a current research hotspot Among the focal points in the field of deep learning, AI for science is one of the highlighted areas, utilizing deep learning methods for pattern recognition, anomaly detection, predictive analysis, and more on a large scale of scientific data. In the realm of materials science, the structure of crystals is composed of edges and nodes, making it readily representable as a graph. In previous research, some typical models, such as the MEGNet model, utilized their graph neural network features to fit computational results based on density functional theory for predicting various material properties. Building on this concept, the authors propose a novel graph neural network (GNN) model, optimized with a Multi-Head Self-Attention (MHSA) mechanism, for predicting materials data with crystal structures. This model is named self-attention enhanced graph neural network. The model segments the input data into three parts: edges, nodes, and global features. The graph convolutional layer module is primarily used for aggregating node, edge, and global features, learning node representations, and capturing higher-order neighborhood information through multiple layers of GNN. The MHSA component allows nodes to learn global dependencies, providing different representation subspaces for the nodes. In comparison with other machine learning and deep learning models, the results indicate an improvement in the predictive accuracy of this model. A new graph neural network (GNN) model called Self-Attention Enhanced Graph Neural Network (SA-GNN) is proposed for predicting the properties of materials with crystal structures. This model incorporates multi-head self-attention to allow nodes to learn global dependencies and generate different representational subspaces. Compared to other machine learning and deep learning models, the results show improved predictive accuracy, demonstrating the potential of graph networks combined with self-attention for modeling crystal material data.

Data-Driven Compression of Electron-Phonon Interactions

First-principles calculations of electron interactions in materials have seen rapid progress in recent years, with electron-phonon (e−ph) interactions being a prime example. However, these techniques use large matrices encoding the interactions on dense momentum grids, which reduces computational efficiency and obscures interpretability. For e−ph interactions, existing interpolation techniques leverage locality in real space, but the high dimensionality of the data remains a bottleneck to balance cost and accuracy. Here we show an efficient way to compress e−ph interactions based on singular value decomposition (SVD), a widely used matrix and image compression technique. Leveraging (un)constrained SVD methods, we accurately predict material properties related to e−ph interactions—including charge mobility, spin relaxation times, band renormalization, and superconducting critical temperature—while using only a small fraction (1%–2%) of the interaction data. These findings unveil the hidden low-dimensional nature of e−ph interactions. Furthermore, they accelerate state-of-the-art first-principles e−ph calculations by about 2 orders of magnitude without sacrificing accuracy. Our Pareto-optimal parametrization of e−ph interactions can be readily generalized to electron-electron and electron-defect interactions, as well as to other couplings, advancing quantitative studies of condensed matter. Published by the American Physical Society 2024

The significance of materials informatics on material science

In 2011, U.S. President Barack Obama proposed the Material Genome Project. Want to high high-speed and low-cost methods to develop material science, which promoted the rapid development of materials informatics. Materials informatics is an interdisciplinary field that employs the principles of informatics to enhance the comprehension, utilization, and advancement of materials within the realm of materials science. Materials informatics can take multiple approaches and influence many aspects of new material development. Material informatics will cause great changes in the material industry and promote the rapid development of materials. A variety of materials informatics schemes have been developed to analyze materials, and there have been many successful cases worth applying. This article introduces the basic concepts and main research fields of materials informatics and describes four main steps in materials informatics: data collection, data processing, the establishment of a database, construction of a model. The main applications of materials informatics include simulation and prediction of material properties, development of new materials, material optimization and performance improvement. Thinking about the difficult problems in the field of materials informatics for application.

Picturing the Gap Between the Performance and US-DOE's Hydrogen Storage Target: A Data-Driven Model for MgH2 Dehydrogenation.

Developing solid-state hydrogen storage materials requires a comprehensive understanding of the dehydrogenation chemistry of a solid-state hydride. Transition state search and kinetics calculations are essential to understanding and designing high-performance solid-state hydrogen storage materials by filling in the knowledge gap that current experimentscannot measure. However, the ab initio analysis of these processes is expensive and time-consuming. Searching for descriptors to accurately predict the energy barrier is urgently needed, to accelerate the prediction of hydrogen storage material properties and identify the opportunities and challenges. Herein, we develop a data-driven model to describe and predict the dehydrogenation barriers of a typical solid-state hydrogen storage material, MgH2, based on the combination of the crystal Hamilton population orbital of Mg-H bond and the distance between atomic hydrogen.All the parameters in this model can be directly calculated with significantly less computational cost than conventional transition state search, so that the dehydrogenation performance of hydrogen storage materials can be predicted efficiently. Finally, we found that this model leads to excellent agreement with typical experimental measurements reported to date and provides clear design guidelines on how to propel the performance of MgH2 closer to the target set by the United States Department of Energy (US-DOE).

Picturing the Gap Between the Performance and US‐DOE’s Hydrogen Storage Target: A Data‐Driven Model for MgH2 Dehydrogenation

Developing solid‐state hydrogen storage materials requires a comprehensive understanding of the dehydrogenation chemistry of a solid‐state hydride. Transition state search and kinetics calculations are essential to understanding and designing high‐performance solid‐state hydrogen storage materials by filling in the knowledge gap that current experiments cannot measure. However, the ab initio analysis of these processes is expensive and time‐consuming. Searching for descriptors to accurately predict the energy barrier is urgently needed, to accelerate the prediction of hydrogen storage material properties and identify the opportunities and challenges. Herein, we develop a data‐driven model to describe and predict the dehydrogenation barriers of a typical solid‐state hydrogen storage material, MgH2, based on the combination of the crystal Hamilton population orbital of Mg‐H bond and the distance between atomic hydrogen. All the parameters in this model can be directly calculated with significantly less computational cost than conventional transition state search, so that the dehydrogenation performance of hydrogen storage materials can be predicted efficiently. Finally, we found that this model leads to excellent agreement with typical experimental measurements reported to date and provides clear design guidelines on how to propel the performance of MgH2 closer to the target set by the United States Department of Energy (US‐DOE).

Analyzing microstructure relationships in porous copper using a multi-method machine learning-based approach

The prediction of material properties from a given microstructure and its reverse engineering displays an essential ingredient for accelerated material design. However, a comprehensive methodology to uncover the processing-structure-property relationship is still lacking. Herein, we develop a methodology capable of understanding this relationship for differently processed porous materials. We utilize a multi-method machine learning approach incorporating tomographic image data acquisition, segmentation, microstructure feature extraction, feature importance analysis and synthetic microstructure reconstruction. Enhanced segmentation with an accuracy of about 95% based on an efficient annotation technique provides the basis for accurate microstructure quantification, prediction and understanding of the correlation of the extracted microstructure features and electrical conductivity. We show that a diffusion probabilistic model superior to a generative adversarial network model, provides synthetic microstructure images including physical information in agreement with real data, an essential step to predicting properties of unseen conditions.

Modification Method of Expanded Polystyrene Granular Concrete Based on Computer Data Analysis

Expanded Polystyrene Granular concrete (EPS) is renowned for its exceptional lightweight and thermal insulation properties, yet it is hindered by its low strength. Despite the existence of numerous modification methods, there remains a dearth of quantification and summarization, particularly in the era of Artificial Intelligence (AI). Consequently, this study undertook the quantification of several conventional and novel modifications, uncovering intriguing challenges and discerning future trends in modification techniques. These methods encompass nanomaterials, phase change materials, ceramic concrete, and composite modifications, all aimed at bolstering mechanical properties and introducing novel functionalities to address diverse environmental, energy, and engineering requirements. Challenges encompass intricate processes, high costs, material compatibility issues, nascent processes, and application hurdles. Leveraging the advancements in simulation and AI technology, numerical simulations can meticulously replicate the microstructure of EPS granules and the physical attributes of concrete in numerical models, facilitating accurate prediction of material properties. The application of artificial intelligence techniques also includes utilizing big data analysis to optimize the proportions of various materials in EPS granular concrete, providing a valuable framework for the study of high-performance EPS granular concrete and significantly contributing to the enhancement of concrete performance.

Dynamic graph attention network-based crystal space-enriched representation for improving material property prediction

The machine learning approach applied to materials discovery is a popular research direction. Knowledge of quantum chemistry explains that the structure of a material determines its properties. Graph neural networks (GNNs) provide a unique way of predicting the macroscopic properties of molecules and crystals rather than by solving the computationally expensive Schrödinger equation. Graph neural networks can abundantly transform the structural information of materials into corresponding features, and many models based on graph neural networks have been applied to predict material properties. We developed a new model (DYCGNN) containing a node update module for our designed edge-graph attention network composition. Through the application of the edge-gatv2 module, this module can effectively learn the complex relationship between nodes and neighbouring nodes in the crystal. Based on the calculated weight coefficients of each neighbouring node, the representation of the node is updated more effectively. In addition, we fuse the position information of the nodes into the node eigenvectors to complement the spatial information of the crystal and enrich the complete representation of the crystal. As we investigate the DYCGNN model, we find that our approach can outperform the predictions of previous models and provide insights into material crystallization.

Unraveling the impact of initial choices and in-loop interventions on learning dynamics in autonomous scanning probe microscopy

The current focus in Autonomous Experimentation (AE) is on developing robust workflows to conduct the AE effectively. This entails the need for well-defined approaches to guide the AE process, including strategies for hyperparameter tuning and high-level human interventions within the workflow loop. This paper presents a comprehensive analysis of the influence of initial experimental conditions and in-loop interventions on the learning dynamics of Deep Kernel Learning (DKL) within the realm of AE in scanning probe microscopy. We explore the concept of the “seed effect,” where the initial experiment setup has a substantial impact on the subsequent learning trajectory. Additionally, we introduce an approach of the seed point interventions in AE allowing the operator to influence the exploration process. Using a dataset from Piezoresponse Force Microscopy on PbTiO3 thin films, we illustrate the impact of the “seed effect” and in-loop seed interventions on the effectiveness of DKL in predicting material properties. The study highlights the importance of initial choices and adaptive interventions in optimizing learning rates and enhancing the efficiency of automated material characterization. This work offers valuable insights into designing more robust and effective AE workflows in microscopy with potential applications across various characterization techniques.

A node graph using SEM images for material properties prediction with a case study of composite material

Establishing a mapping model between the microstructure and material properties of composite materials is crucial for material development. Scanning electron microscope (SEM) images are widely used for the prediction of material properties. However, the prediction from a single SEM image is independent and does not fully reflect the microstructure characteristics. To address this issue, this paper proposes a node graph construction strategy for SEM images and establishes a multi-graph-based graph attention network (GAT) material property prediction model to achieve the convergence of mutual complementation in microstructure features by using GAT. Firstly, multiple SEM images are constructed into node graphs by a microstructure feature encoder. Next, the microstructure features of multiple SEM images on the node graphs are mutually complemented and converged by using GAT. Finally, the prediction is carried out by using multiple SEM images. The experimental results show that the proposed method shows better performance than other methods.

Computing formation enthalpies through an explainable machine learning method: the case of lanthanide orthophosphates solid solutions

In the last decade, the use of AI in Condensed Matter physics has seen a steep increase in the number of problems tackled and methods employed. A number of distinct Machine Learning approaches have been employed in many different topics; from prediction of material properties to computation of Density Functional Theory potentials and inter-atomic force fields. In many cases, the result is a surrogate model which returns promising predictions but is opaque on the inner mechanisms of its success. On the other hand, the typical practitioner looks for answers that are explainable and provide a clear insight into the mechanisms governing a physical phenomena. In this study, we describe a proposal to use a sophisticated combination of traditional Machine Learning methods to obtain an explainable model that outputs an explicit functional formulation for the material property of interest. We demonstrate the effectiveness of our methodology in deriving a new highly accurate expression for the enthalpy of formation of solid solutions of lanthanide orthophosphates.

Atoms as words: A novel approach to deciphering material properties using NLP-inspired machine learning on crystallographic information files (CIFs)

In condensed matter physics and materials science, predicting material properties necessitates understanding intricate many-body interactions. Conventional methods such as density functional theory and molecular dynamics often resort to simplifying approximations and are computationally expensive. Meanwhile, recent machine learning methods use handcrafted descriptors for material representation, which sometimes neglect vital crystallographic information and are often limited to single property prediction or a sub-class of crystal structures. In this study, we pioneer an unsupervised strategy, drawing inspiration from natural language processing to harness the underutilized potential of Crystallographic Information Files (CIFs). We conceptualize atoms and atomic positions within a crystallographic information file similarly to words in textual content. Using a Word2Vec-inspired technique, we produce atomic embeddings that capture intricate atomic relationships. Our model, CIFSemantics, trained on the extensive material project dataset, adeptly predicts 15 distinct material properties from the CIFs. Its performance rivals that of specialized models, marking a significant step forward in material property predictions.

Prediction of particle-reinforced composite material properties based on an improved Halpin–Tsai model

This paper introduces an improved Halpin–Tsai model to predict the mechanical, thermal, and electrical properties of silicon-carbide-reinforced polypropylene composites. The model considers the influence of porosity and corresponding silicon-carbide volume fractions and derives relationships between material property shape factors and the aspect ratio, silicon-carbide volume fraction, and porosity. The improved model’s predictions exhibit errors of 4.00% for mechanical properties, 2.13% for thermal properties, and 2.24% for electrical properties when compared to finite element analysis. This study demonstrates that the improved Halpin–Tsai model can effectively predict the properties of silicon-carbide-reinforced polypropylene composites, aiding in the design and optimization of these materials.

Prediction and comparative analysis of friction material properties using a GA-SVM optimization model

PurposeWith the rapid advancement in the automotive industry, the friction coefficient (FC), wear rate (WR) and weight loss (WL) have emerged as crucial parameters to measure the performance of automotive braking systems, so the FC, WR and WL of friction material are predicted and analyzed in this work, with an aim of achieving accurate prediction of friction material properties.Design/methodology/approachGenetic algorithm support vector machine (GA-SVM) model is obtained by applying GA to optimize the SVM in this work, thus establishing a prediction model for friction material properties and achieving the predictive and comparative analysis of friction material properties. The process parameters are analyzed by using response surface methodology (RSM) and GA-RSM to determine them for optimal friction performance.FindingsThe results indicate that the GA-SVM prediction model has the smallest error for FC, WR and WL, showing that it owns excellent prediction accuracy. The predicted values obtained by response surface analysis are closed to those of GA-SVM model, providing further evidence of the validity and the rationality of the established prediction model.Originality/valueThe relevant results can serve as a valuable theoretical foundation for the preparation of friction material in engineering practice.