Prediction Of Material Properties Research Articles

Designing of potential polymer donors with lower reorganization energy and chemical space exploration in search of materials for efficient organic solar cells

Machine learning with fascinating material predicting capabilities has emerged as a growing throughput artificial intelligence technology to accelerate the design, screening and prediction of material properties. In this study, chemical descriptors are employed to train machine learning (ML) models for the designing of potential polymer donor materials aimed at enhancing the efficiency of organic solar cells (OSCs). To achieve this, ten ML models are evaluated. Among all, the random forest regressor model has shown better predictive ability. In addition, to quantify the importance of features, the numerical scores of the significance of the features are determined through Feature importance analysis. Moreover, 10k new polymers are generated employing the python-based method and their reorganization energies are further predicted using a pre-trained ML model. The assemblage of polymers is done on the basis of the reorganization energy, those with lower reorganization energy being preferred. Furthermore, the synthetic accessibility and thus the structural diversity among the chosen polymers donors are examined, revealing a structural difference among the chosen polymers. This work paves a way for the designing and screening of efficient semiconductors with low reorganization energy to increase the efficiency of OSCs.

Introducing a new correlation functional in density functional theory

The correlation functional holds significance in density functional theory as it addresses electron–electron interactions beyond the mean-field approximation, enhancing the accuracy of total energy calculations, electronic excitations, and the prediction of materials properties. There are several expressions to describe this energy, and each of them has a unique set of errors in calculating particular properties of materials. This work offers a new correlation functional by employing the density's dependence on ionization energy. We theoretically derived this functional and combined it with the previously reported ionization energy dependent exchange functional to investigate its effect on the total energy, bond energy, dipole moment, and zero-point energy of 62 molecules. The comparison of this new functional in respect to existing widely used correlation models including QMC, PBE, B3LYP and Chachiyo models shows how well it works in producing accurate results with minimal mean absolute error.

Differential impedance analysis — Extensions and applications in machine learning

The technique of differential impedance analysis (DIA) has shown promising results in identifying appropriate model orders when applied to electrochemical impedance spectroscopy (EIS) measurements of a given medium. However, even with this method it remains challenging to reliably deduce general material properties of the medium from impedance data alone. Here, we discuss a number of possible extensions and modifications of the technique and, in particular, an extension of the process from mere model order identification to a complete modelling approach. In addition, the combination of DIA and machine learning methods to predict material properties is explored. Our results were validated with impedance measurements between 20Hz and 1MHz of moulding sand containing varying amounts of quartz and chromite sand as well as bentonite and carbon.

Rewc-GNN Algorithm for the Property Prediction of Large-Scale Crystals.

The space group of a crystal describes the symmetry and periodic arrangement of its structure. As the fundamental element in the structure, it plays a vital role in determining the physical and chemical properties of crystals. The investigation of crystal space group information allows for the prediction of material properties, thereby providing guidance for material design and synthesis to enhance their performance or functionality. Currently prevalent first-principles-based computational methods exhibit good accuracy, but they rely heavily on computing resources, greatly limiting the efficiency of material screening. In this paper, our study is oriented toward the prediction the spatial group of crystals, and an algorithm named Rewc, based on graph neural networks (GNNs) is proposed. This algorithm encodes all atoms and the interactions between atoms in the crystal as features by combining Floyd algorithm and k-hop message passing and employs multilayer convolutional networks to extract connections between k layers. This allows for the automatic learning of more representative atomic vector representations through iterations of feature information for each atom and its neighbors. Experimental results demonstrate that the Rewc framework exhibits reliable accuracy and good generalization capabilities in predicting the crystal structure compared to previous GNN methods.

Rational Design of Deep Learning Networks Based on a Fusion Strategy for Improved Material Property Predictions.

The success of machine learning in predicting material properties is largely dependent on the design of the model. However, the current designs of deep learning models in materials science have the following prominent problems. First, the model design lacks a rational guidance strategy and heavily relies on a large amount of trial and error. Second, numerous deep learning models are utilized across various fields, each with its own advantages and disadvantages. Therefore, it is important to incorporate a fusion strategy to fully leverage them and further expand the design strategies of the models. To address these problems, we analyze that the main reason is the lack of a new feedback method rich in physical insights. In this study, we developed a feedback method called the Chemical Environment Clustering Vector (CECV) of compounds at different thresholds, which is rich in physical insights. Based on CECV, we rationally designed the Long Short-Term Memory and Gated Recurrent Unit fused with Deep Convolutional Neural Network (L-G-DCNN) to explore the field of structure-agnostic material property predictions. L-G-DCNN accurately captures the interactions between elements in compounds, enabling more accurate and efficient predictions of the material properties. Our results demonstrate that the performance of the L-G-DCNN surpasses the current state-of-the-art structure-agnostic models across 28 benchmark data sets, exhibiting superior sample efficiency and faster convergence speed. By employing different visualization methods, we demonstrate that the fusion strategy based on CECV significantly enhances the comprehension of the L-G-DCNN model design and provides a fresh perspective for researchers in the field of materials informatics.

Ab initio framework for deciphering trade-off relationships in multi-component alloys

While first-principles methods have been successfully applied to characterize individual properties of multi-principal element alloys (MPEA), their use in searching for optimal trade-offs between competing properties is hampered by high computational demands. In this work, we present a framework to explore Pareto-optimal compositions by integrating advanced ab initio-based techniques into a Bayesian multi-objective optimization workflow, complemented by a simple analytical model providing straightforward analysis of trends. We benchmark the framework by applying it to solid solution strengthening and ductility of refractory MPEAs, with the parameters of the strengthening and ductility models being efficiently computed using a combination of the coherent-potential approximation method, accounting for finite-temperature effects, and actively-learned moment-tensor potentials parameterized with ab initio data. Properties obtained from ab initio calculations are subsequently used to extend predictions of all relevant material properties to a large class of refractory alloys with the help of the analytical model validated by the data and relying on a few element-specific parameters and universal functions that describe bonding between elements. Our findings offer crucial insights into the traditional strength-vs-ductility dilemma of refractory MPEAs. The proposed framework is versatile and can be extended to other materials and properties of interest, enabling a predictive and tractable high-throughput screening of Pareto-optimal MPEAs over the entire composition space.

Structure-based out-of-distribution (OOD) materials property prediction: a benchmark study

In real-world materials research, machine learning (ML) models are usually expected to predict and discover novel exceptional materials that deviate from the known materials. It is thus a pressing question to provide an objective evaluation of ML model performances in property prediction of out-of-distribution (OOD) materials that are different from the training set. Traditional performance evaluation of materials property prediction models through the random splitting of the dataset frequently results in artificially high-performance assessments due to the inherent redundancy of typical material datasets. Here we present a comprehensive benchmark study of structure-based graph neural networks (GNNs) for extrapolative OOD materials property prediction. We formulate five different categories of OOD ML problems for three benchmark datasets from the MatBench study. Our extensive experiments show that current state-of-the-art GNN algorithms significantly underperform for the OOD property prediction tasks on average compared to their baselines in the MatBench study, demonstrating a crucial generalization gap in realistic material prediction tasks. We further examine the latent physical spaces of these GNN models and identify the sources of CGCNN, ALIGNN, and DeeperGATGNN’s significantly more robust OOD performance than those of the current best models in the MatBench study (coGN and coNGN) as a case study for the perovskites dataset, and provide insights to improve their performance.

AI for dielectric capacitors

Dielectric capacitors, characterized by ultra-high power densities, have been widely used in Internet of Everything terminals and vigorously developed to improve their energy storage performance for the goal of carbon neutrality. With the boom of machine learning (ML) methodologies, Artificial Intelligence (AI) has been deeply integrated into the research and development of dielectric capacitors, including predicting material properties, optimizing material composition and structure, augmenting theoretical knowledge and so on. Through typical application cases, we comprehensively review that AI has greatly broadened the scope of the design and discovery of dielectric capacitors at multiple scales, ranging from atoms/molecules to domains/grains, films/bulks, and devices/systems. Finally, an outlook on potential solutions to current challenges and some novel applications and breakthroughs that AI may facilitate in the field of dielectric capacitors are highlighted.

AtomGPT: Atomistic Generative Pretrained Transformer for Forward and Inverse Materials Design.

Large language models (LLMs) such as generative pretrained transformers (GPTs) have shown potential for various commercial applications, but their applicability for materials design remains underexplored. In this Letter, AtomGPT is introduced as a model specifically developed for materials design based on transformer architectures, demonstrating capabilities for both atomistic property prediction and structure generation. This study shows that a combination of chemical and structural text descriptions can efficiently predict material properties with accuracy comparable to graph neural network models, including formation energies, electronic bandgaps from two different methods, and superconducting transition temperatures. Furthermore, AtomGPT can generate atomic structures for tasks such as designing new superconductors, with the predictions validated through density functional theory calculations. This work paves the way for leveraging LLMs in forward and inverse materials design, offering an efficient approach to the discovery and optimization of materials.

Optimizing high-entropy alloys using deep neural networks

Exceptional mechanical properties of multi-principal element alloys have been typically achieved through manual trial-and-error approaches. The advances in artificial neural networks (ANN) have recently allowed for the development of predictive alloy design ANN models that take into account a large variety of prior experimental findings. However, when mechanical performance is considered, prior processing protocols (e.g. annealing, cold working) are almost impossible to track across different research groups and material classes. In this work, we propose to reverse the use of the yield stress point, setting it as an explicit input (instead of predictive output) parameter that is then used for the quantification of the material state, towards predicting material properties, such as the Ultimate Tensile Strength (UTS). This novel approach specifically addresses the challenge of predicting and improving ductility, a common issue in existing methods focused on yield stress, hardness, and phases. By combining available, validated databases of eleven elements and 734 total materials, we develop an ANN that receives as input the compositions out of 11 possible elements and the "observed" yield stress. Then, we utilize this model to perform predictions for 5-element concentrated alloys with exceptional UTS. We show that this ANN modeling strategy leads to exceptional confidence scores, and then, we use this ANN model for the design of HEAs, which we further discuss.

Chemistry and Local Environment Adaptive Representation graphs as material descriptors

Accelerating materials design and property prediction via supervised learning involves manually generating feature vectors or performing complicated atom coordinates transformation, which restricts the model to a limited set of crystal structures or makes it challenging to provide chemical insights. In this work, we devised a unique low-dimensional featurization technique known as "Chemistry and Local Environment Adaptive Representation" (CLEAR) graphs to automatically learn the properties of materials through the chemistry of the atom connections. CLEAR is an adaptive featurization method that integrates the chemistry of atoms with their atomic environment using Voronoi nearest neighbours (NN). In addition, integrating the CLEAR descriptors with explainable machine learning models unravels its potential to obtain numerous scientific insights. We applied this approach to study the stability of compositional and configurational diverse high entropy alloys (HEAs). The proposed framework provides a universal low-dimensional representation with chemistry-informed local environment descriptors, which outperforms the prediction of phases and formation energies in HEAs.

Machine Learning Techniques for Predicting Conductive Properties of New Materials

The study "Machine Learning Techniques for Predicting Conductive Properties of New Materials" explores the application of advanced machine learning (ML) algorithms to predict the conductive properties of novel materials, accelerating the discovery and development process in materials science. Traditional methods for assessing conductive properties are often time-consuming and expensive, necessitating a more efficient approach. This research leverages various ML techniques, including supervised learning algorithms such as support vector machines, decision trees, and neural networks, to analyze large datasets of material properties and predict conductivity with high accuracy. Feature selection and engineering processes are employed to identify the most significant attributes influencing conductivity. The study also compares the performance of different ML models, optimizing hyperparameters to enhance prediction reliability. Results demonstrate that ML models can significantly reduce the experimental burden, offering rapid and precise predictions that align closely with empirical data. The integration of ML in materials science presents a transformative approach, enabling faster identification of promising conductive materials, thereby fostering advancements in electronics, energy storage, and other technological domains. The study highlights the potential of ML to revolutionize material property prediction, paving the way for accelerated innovation and application in various industries.

Addressing the Accuracy-Cost Trade-off in Material Property Prediction Using a Teacher-Student Strategy.

Deep learning has catalyzed a transformative shift in material discovery, offering a key advantage over traditional experimental and theoretical methods by significantly reducing associated costs. Models adept at predicting properties from chemical compositions alone do not require structural information. However, this cost-efficient approach compromises model precision, particularly in Chemical Composition-based Property Prediction Models (CPMs), which are notably less accurate than Structure-based Property Prediction Models (SPMs). Addressing this challenge, our study introduces a novel Teacher-Student (TS) strategy, where a pretrained SPM serves as an instructive 'teacher' to enhance the CPM's precision. This TS strategy successfully harmonizes low-cost exploration with high accuracy, achieving a significant 47.1% reduction in relative error in scenarios involving 100 data entries. We also evaluate the effectiveness of the proposed strategy by employing perovskites as a case study. This method represents a significant advancement in the exploration and identification of valuable materials, leveraging CPM's potential while overcoming its precision limitations.

Enhancing Material Property Predictions through Optimized KNN Imputation and Deep Neural Network Modeling

In materials science, the integrity and completeness of datasets are critical for robust predictive modeling. Unfortunately, material datasets frequently contain missing values due to factors such as measurement errors, data non-availability, or experimental limitations, which can significantly undermine the accuracy of property predictions. To tackle this challenge, we introduce an optimized K-Nearest Neighbors (KNN) imputation method, augmented with Deep Neural Network (DNN) modeling, to enhance the accuracy of predicting material properties. Our study compares the performance of our Enhanced KNN method against traditional imputation techniques—mean imputation and Multiple Imputation by Chained Equations (MICE). The results indicate that our Enhanced KNN method achieves a superior R² score of 0.973, which represents a significant improvement of 0.227 over Mean imputation, 0.141 over MICE, and 0.044 over KNN imputation. This enhancement not only boosts the data integrity but also preserves the statistical characteristics essential for reliable predictions in materials science.

A review on the applications of graph neural networks in materials science at the atomic scale

AbstractIn recent years, interdisciplinary research has become increasingly popular within the scientific community. The fields of materials science and chemistry have also gradually begun to apply the machine learning technology developed by scientists from computer science. Graph neural networks (GNNs) are new machine learning models with powerful feature extraction, relationship inference, and compositional generalization capabilities. These advantages drive researchers to design computational models to accelerate material property prediction and new materials design, dramatically reducing the cost of traditional experimental methods. This review focuses on the principles and applications of the GNNs. The basic concepts and advantages of the GNNs are first introduced and compared to the traditional machine learning and neural networks. Then, the principles and highlights of seven classic GNN models, namely crystal graph convolutional neural networks, iCGCNN, Orbital Graph Convolutional Neural Network, MatErials Graph Network, Global Attention mechanism with Graph Neural Network, Atomistic Line Graph Neural Network, and BonDNet are discussed. Their connections and differences are also summarized. Finally, insights and prospects are provided for the rapid development of GNNs in materials science at the atomic scale.

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

AbstractDeveloping solid‐state hydrogen storage materials is as pressing as ever, which 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 experimental techniques cannot measure. However, the ab initio analysis of these processes is computationally 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 in this field. Herein, we develop a data‐driven model to describe and predict the dehydrogenation barriers of a typical solid‐state hydrogen storage material, magnesium hydride (MgH2), based on the combination of the crystal Hamilton population orbital of Mg−H bond and the distance between atomic hydrogen. By deriving the distance energy ratio, this model elucidates the key chemistry of the reaction kinetics. 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).

Universal materials model of deep-learning density functional theory Hamiltonian

Realizing large materials models has emerged as a critical endeavor for materials research in the new era of artificial intelligence, but how to achieve this fantastic and challenging objective remains elusive. Here, we propose a feasible pathway to address this paramount pursuit by developing universal materials models of deep-learning density functional theory Hamiltonian (DeepH), enabling computational modeling of the complicated structure-property relationship of materials in general. By constructing a large materials database and substantially improving the DeepH method, we obtain a universal materials model of DeepH capable of handling diverse elemental compositions and material structures, achieving remarkable accuracy in predicting material properties. We further showcase a promising application of fine-tuning universal materials models for enhancing specific materials models. This work not only demonstrates the concept of DeepH’s universal materials model but also lays the groundwork for developing large materials models, opening up significant opportunities for advancing artificial intelligence-driven materials discovery.

Modeling Charged Defects Using the Defect Energy Formalism With a Charge Sublattice Within a CALPHAD Framework

AbstractThe non‐stoichiometry of material compounds significantly influences their functional properties. In semiconductors, point defects determine whether a material is n‐type or p‐type and the concentration of charge carriers. Even in structural alloys and ion conductors used in batteries and fuel cells, non‐stoichiometry and dilute defects play an integral role in their function. The design of metal alloys has advanced rapidly with the aid of thermodynamic modeling using calculation of phase diagrams (CALPHAD) to predict the phases produced during different processing conditions. While thermodynamic modeling has been done previously by fitting experimental data, the advent of first‐principle techniques has enabled the computational prediction of material properties. The defect energy formalism (DEF) is described for modeling charged and uncharged defects in compounds, showing that it accurately predicts non‐stoichiometry using density functional theory (DFT), without fitting experiments. The model reproduces the expected defect and free‐charge carrier concentrations using the statistical mechanics approach commonly used in most DFT defect studies. Finally, the model is used to accurately predict the single‐phase region of PbTe with no fitting to measured defect concentrations. This method can revolutionize materials development of insulators, semiconductors, and even metals by allowing rapid DFT calculations to replace laborious experiments when dilute defects are involved.

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.