Deep learning (DL) is one of the fastest-growing topics in materials data science, with rapidly emerging applications spanning atomistic, image-based, spectral, and textual data modalities. DL allows analysis of unstructured data and automated identification of features. The recent development of large materials databases has fueled the application of DL methods in atomistic prediction in particular. In contrast, advances in image and spectral data have largely leveraged synthetic data enabled by high-quality forward models as well as by generative unsupervised DL methods. In this article, we present a high-level overview of deep learning methods followed by a detailed discussion of recent developments of deep learning in atomistic simulation, materials imaging, spectral analysis, and natural language processing. For each modality we discuss applications involving both theoretical and experimental data, typical modeling approaches with their strengths and limitations, and relevant publicly available software and datasets. We conclude the review with a discussion of recent cross-cutting work related to uncertainty quantification in this field and a brief perspective on limitations, challenges, and potential growth areas for DL methods in materials science.
As we begin to reach the limits of classical computing, quantum computing has emerged as a technology that has captured the imagination of the scientific world. While for many years, the ability to execute quantum algorithms was only a theoretical possibility, recent advances in hardware mean that quantum computing devices now exist that can carry out quantum computation on a limited scale. Thus, it is now a real possibility, and of central importance at this time, to assess the potential impact of quantum computers on real problems of interest. One of the earliest and most compelling applications for quantum computers is Feynman's idea of simulating quantum systems with many degrees of freedom. Such systems are found across chemistry, physics, and materials science. The particular way in which quantum computing extends classical computing means that one cannot expect arbitrary simulations to be sped up by a quantum computer, thus one must carefully identify areas where quantum advantage may be achieved. In this review, we briefly describe central problems in chemistry and materials science, in areas of electronic structure, quantum statistical mechanics, and quantum dynamics that are of potential interest for solution on a quantum computer. We then take a detailed snapshot of current progress in quantum algorithms for ground-state, dynamics, and thermal-state simulation and analyze their strengths and weaknesses for future developments.
G. R. Schleder, A. C. Padilha, C. M. Acosta
et al.
Recent advances in experimental and computational methods are increasing the quantity and complexity of generated data. This massive amount of raw data needs to be stored and interpreted in order to advance the materials science field. Identifying correlations and patterns from large amounts of complex data is being performed by machine learning algorithms for decades. Recently, the materials science community started to invest in these methodologies to extract knowledge and insights from the accumulated data. This review follows a logical sequence starting from density functional theory as the representative instance of electronic structure methods, to the subsequent high-throughput approach, used to generate large amounts of data. Ultimately, data-driven strategies which include data mining, screening, and machine learning techniques, employ the data generated. We show how these approaches to modern computational materials science are being used to uncover complexities and design novel materials with enhanced properties. Finally, we point to the present research problems, challenges, and potential future perspectives of this new exciting field.
Advances in machine learning have impacted myriad areas of materials science, such as the discovery of novel materials and the improvement of molecular simulations, with likely many more important developments to come. Given the rapid changes in this field, it is challenging to understand both the breadth of opportunities and the best practices for their use. In this review, we address aspects of both problems by providing an overview of the areas in which machine learning has recently had significant impact in materials science, and then we provide a more detailed discussion on determining the accuracy and domain of applicability of some common types of machine learning models. Finally, we discuss some opportunities and challenges for the materials community to fully utilize the capabilities of machine learning.
Large-scale atomistic computer simulations of materials rely on interatomic potentials providing computationally efficient predictions of energy and Newtonian forces. Traditional potentials have served in this capacity for over three decades. Recently, a new class of potentials has emerged, which is based on a radically different philosophy. The new potentials are constructed using machine-learning (ML) methods and a massive reference database generated by quantum-mechanical calculations. While the traditional potentials are derived from physical insights into the nature of chemical bonding, the ML potentials utilize a high-dimensional mathematical regression to interpolate between the reference energies. We review the current status of the interatomic potential field, comparing the strengths and weaknesses of the traditional and ML potentials. A third class of potentials is introduced, in which an ML model is coupled with a physics-based potential to improve the transferability to unknown atomic environments. The discussion is focused on potentials intended for materials science applications. Possible future directions in this field are outlined.
Dr. Muji Setiyo, Zulfikar Bagus Pambuko, Veni Soraya
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International Nuclear Science, Technology and Engineering Conference 2021 (iNuSTEC2021) “Nuclear Science and Technology for Socio-economic Development” Universiti Teknologi Malaysia (UTM) Skudai, Johor, Malaysia 10-12 October 2021 Editors Nahrul Khair Alang Md Rashid, Universiti Teknologi Malaysia Khaidzir Hamzah, Universiti Teknologi Malaysia Mohsin Mohd Sies, Universiti Teknologi Malaysia Jasman Zainal, Universiti Teknologi Malaysia Muhammad Arif Sazali, Universiti Teknologi Malaysia Muhammad Syahir Sarkawi, Universiti Teknologi Malaysia Nur Syazwani Mohd Ali, Universiti Teknologi Malaysia Khairulnadzmi Jamaluddin, Universiti Teknologi Malaysia Abdul Aziz Mohamed, Malaysian Nuclear Society Faridah Mohamad Idris, Malaysian Nuclear Agency Julie Andrianny Murshidi, Malaysian Nuclear Agency Faizal K P Kunchi Mohamed, Universiti Kebangsaan Malaysia Hassan Mohamed, Universiti Tenaga Nasional Abu Hassan Husin, Universiti Teknologi MARA All papers have been peer-reviewed List of Supporting/Sponsoring Organisations, Preface, Contents, iNuSTEC2021 Organising Committee, Inustec 2021 Awards And Recognition, Acknowledgement and Images are available in this pdf
While the uptake of data-driven approaches for materials science and chemistry is at an exciting, early stage, to realise the true potential of machine learning models for successful scientific discovery, they must have qualities beyond purely predictive power. The predictions and inner workings of models should provide a certain degree of explainability by human experts, permitting the identification of potential model issues or limitations, building trust on model predictions and unveiling unexpected correlations that may lead to scientific insights. In this work, we summarize applications of interpretability and explainability techniques for materials science and chemistry and discuss how these techniques can improve the outcome of scientific studies. We discuss various challenges for interpretable machine learning in materials science and, more broadly, in scientific settings. In particular, we emphasize the risks of inferring causation or reaching generalization by purely interpreting machine learning models and the need of uncertainty estimates for model explanations. Finally, we showcase a number of exciting developments in other fields that could benefit interpretability in material science and chemistry problems.
To fill the gap between accurate (and expensive) ab initio calculations and efficient atomistic simulations based on empirical interatomic potentials, a new class of descriptions of atomic interactions has emerged and been widely applied; i.e., machine learning potentials (MLPs). One recently developed type of MLP is the Deep Potential (DP) method. In this review, we provide an introduction to DP methods in computational materials science. The theory underlying the DP method is presented along with a step-by-step introduction to their development and use. We also review materials applications of DPs in a wide range of materials systems. The DP Library provides a platform for the development of DPs and a database of extant DPs. We discuss the accuracy and efficiency of DPs compared with ab initio methods and empirical potentials.
Abstract The advent of big data and algorithmic developments in the field of machine learning (and artificial intelligence, in general) have greatly impacted the entire spectrum of physical sciences, including materials science. Materials data, measured or computed, combined with various techniques of machine learning have been employed to address a myriad of challenging problems, such as, development of efficient and predictive surrogate models for a range of materials properties, screening and down-selection of novel candidate materials for targeted applications, new methodologies to improve and further expedite molecular and atomistic simulations, with likely many more important developments to come in the foreseeable future. While the applications thus far have provided a glimpse of the true potential data-enabled routes have to offer, it has also become clear that further progress in this direction hinges on our ability to understand, explain and rationalize findings of a machine learning model in light of the domain-knowledge. This focused review provides an overview of the main areas where machine learning has been widely and successfully used in materials science. Subsequently, a brief discussion of several techniques that have been helpful in extracting physically-meaningful insights, causal relationships and design-centric knowledge from materials data is provided. Finally, we identify some of the imminent opportunities and challenges that materials community faces in this exciting and rapidly growing field.
Amalie Trewartha, Nicholas Walker, Haoyan Huo
et al.
Summary A bottleneck in efficiently connecting new materials discoveries to established literature has arisen due to an increase in publications. This problem may be addressed by using named entity recognition (NER) to extract structured summary-level data from unstructured materials science text. We compare the performance of four NER models on three materials science datasets. The four models include a bidirectional long short-term memory (BiLSTM) and three transformer models (BERT, SciBERT, and MatBERT) with increasing degrees of domain-specific materials science pre-training. MatBERT improves over the other two BERTBASE-based models by 1%∼12%, implying that domain-specific pre-training provides measurable advantages. Despite relative architectural simplicity, the BiLSTM model consistently outperforms BERT, perhaps due to its domain-specific pre-trained word embeddings. Furthermore, MatBERT and SciBERT models outperform the original BERT model to a greater extent in the small data limit. MatBERT’s higher-quality predictions should accelerate the extraction of structured data from materials science literature.
Qiaohao Liang, Aldair E. Gongora, Zekun Ren
et al.
Bayesian optimization (BO) has been leveraged for guiding autonomous and high-throughput experiments in materials science. However, few have evaluated the efficiency of BO across a broad range of experimental materials domains. In this work, we quantify the performance of BO with a collection of surrogate model and acquisition function pairs across five diverse experimental materials systems. By defining acceleration and enhancement metrics for materials optimization objectives, we find that surrogate models such as Gaussian Process (GP) with anisotropic kernels and Random Forest (RF) have comparable performance in BO, and both outperform the commonly used GP with isotropic kernels. GP with anisotropic kernels has demonstrated the most robustness, yet RF is a close alternative and warrants more consideration because it is free from distribution assumptions, has smaller time complexity, and requires less effort in initial hyperparameter selection. We also raise awareness about the benefits of using GP with anisotropic kernels in future materials optimization campaigns.
Covid is giving us many lessons among which one must be to realize that this is the time to act for sustainable future. The smart world around us has made it inevitable to have an alarming situation regarding the uncontrolled growth of waste products such as plastic and electronic wastes. Both are immense threats to the health of human, wildlife and environment, that eventually affect the societal and economic structures as evident from recent Covid-crisis. The proper management of these wastes and innovating ideas for new sustainable technologies are the need of the hour. Circular economy act with green technology (green economy) is the way to tackle this challenge. Current perspective presents the overview of the scenario regarding these burgeoning issues and demonstrates some measures that are taken or being considered to depend on to come out of them.
Nowadays, the research on materials science is rapidly entering a phase of data‐driven age. Machine learning, one of the most powerful data‐driven methods, have been being applied to materials discovery and performances prediction with undoubtedly tremendous application foreground. Herein, the challenges and current progress of machine learning are summarized in materials science, the design strategies are classified and highlighted, and possible perspectives are proposed for the future development. It is hoped this review can provide important scientific guidance for innovating materials science and technology via machine learning in the future.
Graphene nanoribbons (GNRs) have recently emerged as promising candidates for channel materials in future nanoelectronic devices due to their exceptional electronic, thermal, and mechanical properties and chemical inertness. However, the adoption of GNRs in commercial technologies is currently hampered by materials science and integration challenges pertaining to synthesis and devices. In this Review, we present an overview of the current status of challenges, recent breakthroughs toward overcoming these challenges, and possible future directions for the field of GNR electronics. We motivate the need for exploration of scalable synthetic techniques that yield atomically precise, placed, registered, and oriented GNRs on CMOS-compatible substrates and stimulate ideas for contact and dielectric engineering to realize experimental performance close to theoretically predicted metrics. We also briefly discuss unconventional device architectures that could be experimentally investigated to harness the maximum potential of GNRs in future spintronic and quantum information technologies.
Cardiovascular diseases (CVD) are the leading global threat to human health. The clinical application of vascular stents improved the survival rates and quality of life for patients with cardiovascular diseases. However, despite the benefits stents bring to patients, there are still notable complications such as thrombosis and in-stent restenosis (ISR). Surface modification techniques represent an effective strategy to enhance the clinical efficacy of vascular stents and reduce complications. This paper reviews the development strategies of vascular stents based on surface functional coating technologies aimed at addressing the limitations in clinical application, including the inhibition of intimal hyperplasia, promotion of re-endothelialization. These strategies have improved endothelial repair and inhibited vascular remodeling, thereby promoting vascular healing post-stent implantation. However, the pathological microenvironment of target vessels and the lipid plaques are key pathological factors in the development of atherosclerosis (AS) and impaired vascular repair after percutaneous coronary intervention (PCI). Therefore, restoring normal physiological environment and removing the plaques are also treatment focuses after PCI for promoting vascular repair. Unfortunately, research in this area is limited. This paper reviews the advancements in vascular stents based on surface engineering technologies over the past decade, providing guidance for the development of stents.
Materials of engineering and construction. Mechanics of materials, Biology (General)