Jing Wei, Xuan Chu, Xiangyu Sun et al. · InfoMat · 2019
Abstract Traditional methods of discovering new materials, such as the empirical trial and error method and the density functional theory (DFT)‐based method, are unable to keep pace with the development of materials science today due to their long development cycles, low efficiency, and high costs. Accordingly, due to its low computational cost and short development cycle, machine learning is coupled with powerful data processing and high prediction performance and is being widely used in material detection, material analysis, and material design. In this article, we discuss the basic operational procedures in analyzing material properties via machine learning, summarize recent applications of machine learning algorithms to several mature fields in materials science, and discuss the improvements that are required for wide‐ranging application.
Cited by 958Open access
Tim Mueller, A. Gilad Kusne, Rampi Ramprasad · Reviews in computational chemistry · 2016
This chapter addresses the role that data-driven approaches, especially machine learning methods, are expected to play in materials research in the immediate future. Machine learning, an important part of artificial intelligence, has made monumental contributions to areas outside materials science, ranging from commerce to gaming to search engines to drug design. Machine learning algorithms can be separated into two broad classes: supervised and unsupervised learning. The chapter first provides the necessary mathematical background to allow a materials researcher entering this field to use these methods most effectively. It then presents an assortment of examples of recent machine learning applications within materials science. The chapter also discusses a range of emerging efforts, including high-throughput phase diagram and crystal structure determination methods, accelerated prediction of materials properties, development of interatomic potentials and functionals for accelerating materials simulations, and efficient and low-cost methods for materials process control.
Cited by 293
Kenneth M. Merz, Yee Siew Choong, Zoe Cournia et al. · Journal of Chemical Information and Modeling · 2024
ADVERTISEMENT RETURN TO ISSUEEditorialNEXTEditorial: Machine Learning in Materials ScienceKenneth M. Merz*Kenneth M. MerzDepartment of Chemistry, Michigan State University, Lansing 48824, Michigan, United States* (Email: [email protected]).More by Kenneth M. Merzhttps://orcid.org/0000-0001-9139-5893, Yee Siew Choong*Yee Siew ChoongInstitute for Research in Molecular Medicine (INFORMM), Universiti Sains Malaysia, Minden 11800, Penang, Malaysia* (Email: [email protected]).More by Yee Siew Choonghttps://orcid.org/0000-0001-5067-2073, Zoe Cournia*Zoe CourniaBiomedical Research Foundation, Academy of Athens, Athens 11527, Greece* (Email: [email protected]).More by Zoe Courniahttps://orcid.org/0000-0001-9287-364X, Olexandr Isayev*Olexandr IsayevDepartment of Chemistry, Mellon College of Science, Carnegie Mellon University, Pittsburgh 15213, Pennsylvania, United States* (Email: [email protected]).More by Olexandr Isayev, Thereza A. Soares*Thereza A. SoaresDepartamento de Química, Faculdade de Filosofia, Ciências e Letras de Ribeirão Preto, Universidade de São Paulo, Ribeirão Preto 14040-901, Brazil* (Email: [email protected]).More by Thereza A. Soareshttps://orcid.org/0000-0002-5891-6906, Guo-Wei Wei*Guo-Wei WeiDepartment of Mathematics, Michigan State University, Lansing 48824, Michigan, United States* (Email: [email protected]).More by Guo-Wei Weihttps://orcid.org/0000-0002-5781-2937, and Feng Zhu*Feng ZhuCollege of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China* (Email: [email protected]).More by Feng Zhuhttps://orcid.org/0000-0001-8069-0053Cite this: J. Chem. Inf. Model. 2024, 64, 10, 3959–3960Publication Date (Web):May 27, 2024Publication History Received26 April 2024Published online27 May 2024Published inissue 27 May 2024https://pubs.acs.org/doi/10.1021/acs.jcim.4c00727https://doi.org/10.1021/acs.jcim.4c00727editorialACS PublicationsCopyright © Published 2024 by American Chemical Society. This publication is available under these Terms of Use. Request reuse permissions This publication is free to access through this site. Learn MoreArticle Views-Altmetric-Citations-LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InRedditEmail PDF (897 KB) Get e-AlertscloseSUBJECTS:Algorithms,Biomaterials,Machine learning,Materials,Materials science Get e-Alerts
Cited by 13Open access
Xiaoting Zhong, Brian Gallagher, Shusen Liu et al. · npj Computational Materials · 2022
Cited by 302Open access
Zhao, Yinghan · KITopen · 2024
Der zunehmende Einsatz datengesteuerter Verfahren in der Materialwissenschaft, einschließlich des maschinellen Lernens, hat zur Werkstoffinformatik geführt und birgt ein großes Potenzial für die schnellere Entdeckung und Entwicklung fortschrittlicher Materialien. Das maschinelle Lernen wird derzeit in der Materialwissenschaft intensiv erforscht und zu seinen Anwendungen gehören die Entdeckung und das Screening neuer Materialien, die Beschleunigung teurer Computersimulationen und die Verbesserung der Analyse großer Mengen komplexer Charakterisierungsdaten. Das Ziel dieser Arbeit besteht darin, grundlegende Konzepte zusammenzufassen und einen einheitlichen Arbeitsablauf für den Einsatz von maschinellem Lernen in der Materialwissenschaft zu präsentieren. Um die Vorteile und Effektivität des maschinellen Lernens zu veranschaulichen, werden drei Studien durchgeführt. Diese dienen als praktische Anwendungsbeispiele und decken die häufigsten Aspekte im Prozess der Materialforschung ab: Experimente, Charakterisierung und Simulation. Für die Experimente wird in dieser Arbeit ein Optimierungsalgorithmus verwendet, um die experimentellen Parameter für den Sintersyntheseprozess eines Elektrolytmaterials in einer Festkörperbatterie zu bestimmen, mit dem Ziel, die Zeit zu reduzieren, die benötigt wird, um die optimalen experimentellen Parameter zu finden. Diese Methodik wird auch erweitert, um die am besten geeigneten Parameter für die Erzeugung simulierter digitaler poröser Mikrostrukturen zu finden. Für die Simulation wird ein auf einem (künstlichen) neuronalen Netzwerk basierendes Modell verwendet, um schnell Prozess-Struktur-Eigenschafts-Beziehungen für poröse Membranmikrostrukturen vorhersagen zu können. Das Modell nutzt insbesondere seine Fähigkeit, Repräsentationen zu lernen, um automatisch strukturelle Informationen zu extrahieren. Zur Charakterisierung werden in dieser Arbeit verschiedene Methoden des maschinellen Lernens zur Analyse von Flugzeit-Sekundärionen-Massenspektrometriedaten von Lithiumverbindungen untersucht. Es ist möglich, nicht nur eine schnelle Klassifizierung zur Identifizierung von Zusammensetzungen zu erstellen, sondern auch interpretierbare chemische Informationen auf der Grundlage der Bedeutung der Merkmale des Modells zu erfassen. Diese Studien sind typische Beispiele, die den Nutzen der Anwendung von Methoden des maschinellen Lernens in der Materialforschung verdeutlichen, und die vorgestellten Ideen können auf andere Szenarien erweitert werden. Darüber hinaus werden die Erfahrungen und Überlegungen, die bei diesen Studien gewonnen wurden, erörtert und Anmerkungen für die künftige Forschung in diesem Bereich gemacht.
Cited by 0Open access
Nicholas Wagner, James M. Rondinelli · Frontiers in Materials · 2016
Materials scientists are increasingly adopting the use of machine learning tools to discover hidden trends in data and make predictions. Applying concepts from data science without foreknowledge of their limitations and the unique qualities of materials data, however, could lead to errant conclusions. The differences that exist between various kinds of experimental and calculated data require careful choices of data processing and machine learning methods. Here, we outline potential pitfalls involved in using machine learning without robust protocols. We address some problems of overfitting to training data using decision trees as an example, rational descriptor selection in the field of perovskites, and preserving physical interpretability in the application of dimensionality reducing techniques such as principal component analysis. We show how proceeding without the guidance of domain knowledge can lead to both quantitatively and qualitatively incorrect predictive models.
Cited by 182Open access
Pengcheng Xu, Xiaobo Ji, Minjie Li et al. · npj Computational Materials · 2023
Abstract This review discussed the dilemma of small data faced by materials machine learning. First, we analyzed the limitations brought by small data. Then, the workflow of materials machine learning has been introduced. Next, the methods of dealing with small data were introduced, including data extraction from publications, materials database construction, high-throughput computations and experiments from the data source level; modeling algorithms for small data and imbalanced learning from the algorithm level; active learning and transfer learning from the machine learning strategy level. Finally, the future directions for small data machine learning in materials science were proposed.
Cited by 588Open access
Joshua Stuckner, S. Mohadeseh Taheri-Mousavi, James E. Saal · AM&P Technical Articles · 2024
Abstract This article provides a brief overview of the many ways that artificial intelligence and machine learning are being used for materials and manufacturing research. Several case studies show how the discovery, development, and deployment of novel materials are being dramatically accelerated through automation and data-driven models.
Cited by 0
Frontiers research topics · 2020
Cited by 7
Sae Dieb, Koji Tsuda · 2018
In materials design and discovery processes, optimal experimental design (OED) algorithms are getting more popular. OED is often modeled as an optimization of a black-box function. In this chapter, we introduce two machine learning-based approaches for OED: Bayesian optimization (BO) and Monte Carlo tree search (MCTS). BO is based on a relatively complex machine learning model and has been proven effective in a number of materials design problems. MCTS is a simpler and more efficient approach that showed significant success in the computer Go game. We discuss existing OED applications in materials science and discuss future directions.
Cited by 16Open access
Kevin Maik Jablonka, Daniele Ongari, Seyed Mohamad Moosavi et al. · Chemical Reviews · 2020
By combining metal nodes with organic linkers we can potentially synthesize millions of possible metal-organic frameworks (MOFs). The fact that we have so many materials opens many exciting avenues but also create new challenges. We simply have too many materials to be processed using conventional, brute force, methods. In this review, we show that having so many materials allows us to use big-data methods as a powerful technique to study these materials and to discover complex correlations. The first part of the review gives an introduction to the principles of big-data science. We show how to select appropriate training sets, survey approaches that are used to represent these materials in feature space, and review different learning architectures, as well as evaluation and interpretation strategies. In the second part, we review how the different approaches of machine learning have been applied to porous materials. In particular, we discuss applications in the field of gas storage and separation, the stability of these materials, their electronic properties, and their synthesis. Given the increasing interest of the scientific community in machine learning, we expect this list to rapidly expand in the coming years.
Cited by 512Open access
Andrew L. Ferguson · Journal of Physics Condensed Matter · 2017
In many branches of materials science it is now routine to generate data sets of such large size and dimensionality that conventional methods of analysis fail. Paradigms and tools from data science and machine learning can provide scalable approaches to identify and extract trends and patterns within voluminous data sets, perform guided traversals of high-dimensional phase spaces, and furnish data-driven strategies for inverse materials design. This topical review provides an accessible introduction to machine learning tools in the context of soft and biological materials by 'de-jargonizing' data science terminology, presenting a taxonomy of machine learning techniques, and surveying the mathematical underpinnings and software implementations of popular tools, including principal component analysis, independent component analysis, diffusion maps, support vector machines, and relative entropy. We present illustrative examples of machine learning applications in soft matter, including inverse design of self-assembling materials, nonlinear learning of protein folding landscapes, high-throughput antimicrobial peptide design, and data-driven materials design engines. We close with an outlook on the challenges and opportunities for the field.
Cited by 183
Morgan, Dane, Jacobs, Ryan · Figshare · 2020
This is supporting information for the publication "Opportunities and Challenges for Machine Learning in Materials Science", by Dane Morgan and Ryan Jacobs, to be published in Annual Reviews of Materials Research (2020).<br>This supporting information contains detailed lists of available ML-centric software packages, a catalogue of recent review papers and a short discussion of journals publishing similar studies.
Cited by 0Open access
Anton O. Oliynyk, Jillian M. Buriak · Chemistry of Materials · 2019
ADVERTISEMENT RETURN TO ISSUEPREVEditorialNEXTVirtual Issue on Machine-Learning Discoveries in Materials ScienceAnton O. OliynykAnton O. OliynykManhattan College, Riverdale, New York, United StatesMore by Anton O. Oliynykhttp://orcid.org/0000-0003-0732-7340 and Jillian M. BuriakJillian M. BuriakUniversity of Alberta, Edmonton, Alberta, CanadaMore by Jillian M. Buriakhttp://orcid.org/0000-0002-9567-4328Cite this: Chem. Mater. 2019, 31, 20, 8243–8247Publication Date (Web):October 22, 2019Publication History Published online22 October 2019Published inissue 22 October 2019https://doi.org/10.1021/acs.chemmater.9b03854Copyright © 2019 American Chemical SocietyRIGHTS & PERMISSIONSArticle Views5153Altmetric-Citations17LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InReddit PDF (2 MB) Get e-AlertsSUBJECTS:Electrical conductivity,Machine learning,Materials,Optimization,Thermoelectric materials Get e-Alerts
Cited by 27
Yue Liu, Zhengwei Yang, Xinxin Zou et al. · National Science Review · 2023
Data-driven machine learning (ML) is widely employed in the analysis of materials structure-activity relationships, performance optimization and materials design due to its superior ability to reveal latent data patterns and make accurate prediction. However, because of the laborious process of materials data acquisition, ML models encounter the issue of the mismatch between a high dimension of feature space and a small sample size (for traditional ML models) or the mismatch between model parameters and sample size (for deep-learning models), usually resulting in terrible performance. Here, we review the efforts for tackling this issue via feature reduction, sample augmentation and specific ML approaches, and show that the balance between the number of samples and features or model parameters should attract great attention during data quantity governance. Following this, we propose a synergistic data quantity governance flow with the incorporation of materials domain knowledge. After summarizing the approaches to incorporating materials domain knowledge into the process of ML, we provide examples of incorporating domain knowledge into governance schemes to demonstrate the advantages of the approach and applications. The work paves the way for obtaining the required high-quality data to accelerate materials design and discovery based on ML.
Cited by 165Open access
Huber, Norbert, Kalidindi, Surya R., Klusemann, Benjamin et al. · tub.dok (Hamburg University of Technology) · 2022
Cited by 0
Rituraj Jain, Sitesh Kumar Singh, Kamal Upreti et al. · 2026
The increase of artificial intelligence (AI) and machine learning (ML) in the discovery, design, and optimization of new materials is causing a rapid acceleration of the field of materials science. This chapter addresses the principles for how artificial intelligence and machine learning can enable the predictive modeling, high-throughput screening, and smart production of polymers, alloys, ceramics, and nanomaterials. Emphasized are some of the techniques, such as hybrid approaches to artificial intelligence and physics, generative models, and reinforcement learning. Some important problems in the chapter are spoken about: lack of standards, incoherence of data, and unfeasibility of explaining the models. Along with that, this also attempts to investigate how the advantages of upcoming malware such as quantum computing, edge synthetic intelligence, and open information facilities can reinforce the research and innovation in the forthcoming age of materials.
Cited by 0
Morgan, Dane, Jacobs, Ryan · Figshare · 2020
This is supporting information for the publication "Opportunities and Challenges for Machine Learning in Materials Science", by Dane Morgan and Ryan Jacobs, to be published in Annual Reviews of Materials Research (2020).<br>This supporting information contains detailed lists of available ML-centric software packages, a catalogue of recent review papers and a short discussion of journals publishing similar studies.
Cited by 0Open access
Dane Morgan, Ryan Jacobs · Annual Review of Materials Research · 2020
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.
Cited by 424Open access
Megumi Iwayama, Stephen Wu, Chang Liu et al. · ChemRxiv · 2022
In recent years, there has been a rapid growth in the use of machine learning in materials science. Conventionally, a trained predictive model describes a scalar output variable, such as thermodynamic, electronic, or mechanical properties, as a function of input descriptors that vectorize the compositional or structural features of any given material, such as molecules, chemical compositions, or crystalline systems. In machine learning of materials data, on the other hand, the output variable is often given as a function. For example, when predicting the optical absorption spectrum of a molecule, the output variable is a spectral function defined in the wavelength domain. Alternatively, in predicting the microstructure of a polymer nanocomposite, the output variable is given as an image from an electron microscope, which can be represented as a two- or three-dimensional function in the image coordinate system. In this study, we considered two unified frameworks to handle such multidimensional or functional output regressions, which are applicable to a wide range of predictive analyses in materials science. The first approach employs generative adversarial networks, which are known to exhibit outstanding performance in various computer vision tasks such as image generation, style transfer, and video generation. We also present another type of statistical modelling inspired by a statistical methodology referred to as functional data analysis. This is an extension of kernel regression to deal with functional outputs, and its simple mathematical structure makes it an effective modelling even for given data in limited supply. We demonstrate the proposed method through several case studies in materials science.
Cited by 0Open access