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The screening of novel materials is an important topic in the field of materials science. Although traditional computational modeling, especially first-principles approaches, is a very useful and accurate tool to predict the properties of…

Computational Physics · Physics 2020-07-30 Marco Fronzi , Mutaz Abu Ghazaleh , Olexandr Isayev , David A. Winkler , Joe Shapter , Michael J. Ford

Building effective machine learning (ML) workflows to address complex tasks is a primary focus of the Automatic ML (AutoML) community and a critical step toward achieving artificial general intelligence (AGI). Recently, the integration of…

Machine Learning · Computer Science 2024-12-30 Yang Gu , Hengyu You , Jian Cao , Muran Yu , Haoran Fan , Shiyou Qian

Dynamical systems that evolve continuously over time are ubiquitous throughout science and engineering. Machine learning (ML) provides data-driven approaches to model and predict the dynamics of such systems. A core issue with this approach…

Machine Learning · Computer Science 2023-11-23 Aditi S. Krishnapriyan , Alejandro F. Queiruga , N. Benjamin Erichson , Michael W. Mahoney

Material discovery is a critical area of research with the potential to revolutionize various fields, including carbon capture, renewable energy, and electronics. However, the immense scale of the chemical space makes it challenging to…

Machine Learning · Computer Science 2024-11-01 Anuroop Sriram , Benjamin Kurt Miller , Ricky T. Q. Chen , Brandon M. Wood

Rational design of compounds with specific properties requires conceptual understanding and fast evaluation of molecular properties throughout chemical compound space (CCS) -- the huge set of all potentially stable molecules. Recent…

Chemical Physics · Physics 2019-12-02 O. Anatole von Lilienfeld , Klaus-Robert Müller , Alexandre Tkatchenko

Machine learning plays an increasingly important role in many areas of chemistry and materials science, e.g. to predict materials properties, to accelerate simulations, to design new materials, and to predict synthesis routes of new…

Machine learning models can assist with metamaterials design by approximating computationally expensive simulators or solving inverse design problems. However, past work has usually relied on black box deep neural networks, whose reasoning…

Machine Learning · Computer Science 2022-10-04 Zhi Chen , Alexander Ogren , Chiara Daraio , L. Catherine Brinson , Cynthia Rudin

Since the surge of data in materials science research and the advancement in machine learning methods, an increasing number of researchers are introducing machine learning techniques into the next generation of materials discovery, ranging…

Soft Condensed Matter · Physics 2024-08-12 Maya M. Martirossyan , Hongjin Du , Julia Dshemuchadse , Chrisy Xiyu Du

Machine learning interatomic potentials have revolutionized complex materials design by enabling rapid exploration of material configurational spaces via crystal structure prediction with ab initio accuracy. However, critical challenges…

Quality control is an essential operation in manufacturing, ensuring products meet the necessary standards of quality, safety, and reliability. Traditional methods, such as visual inspections, measurements, and statistical techniques, help…

Signal Processing · Electrical Eng. & Systems 2026-03-13 Sukumaran Rajasekaran , Ebru Turanoglu Bekar , Kanika Gandhi , Sabino Francesco Roselli , Mohan Rajashekarappa

Nanophotonics finds ever broadening applications requiring complex component designs with a large number of parameters to be simultaneously optimized. Recent methodologies employing optimization algorithms commonly focus on a single design…

Thanks to the recent advances in processing speed and data acquisition and storage, machine learning (ML) is penetrating every facet of our lives, and transforming research in many areas in a fundamental manner. Wireless communications is…

Information Theory · Computer Science 2019-04-30 Deniz Gunduz , Paul de Kerret , Nicholas D. Sidiropoulos , David Gesbert , Chandra Murthy , Mihaela van der Schaar

At the intersection of machine learning and quantum computing, Quantum Machine Learning (QML) has the potential of accelerating data analysis, especially for quantum data, with applications for quantum materials, biochemistry, and…

Quantum Physics · Physics 2023-03-17 M. Cerezo , Guillaume Verdon , Hsin-Yuan Huang , Lukasz Cincio , Patrick J. Coles

Recently there has been an ever-increasing trend in the use of machine learning (ML) and artificial intelligence (AI) methods by the materials science, condensed matter physics, and chemistry communities. This perspective article identifies…

Materials Science · Physics 2020-03-20 Brian DeCost , Jason Hattrick-Simpers , Zachary Trautt , Aaron Kusne , Eva Campo , Martin Green

Machine learning (ML) entered the field of computational micromagnetics only recently. The main objective of these new approaches is the automatization of solutions of parameter-dependent problems in micromagnetism such as fast response…

Computational Physics · Physics 2021-07-15 Sebastian Schaffer , Norbert J. Mauser , Thomas Schrefl , Dieter Suess , Lukas Exl

A main driver behind the digitization of industry and society is the belief that data-driven model building and decision making can contribute to higher degrees of automation and more informed decisions. Building such models from data often…

With an exponential rise in the popularity and availability of additive manufacturing (AM), a large focus has been directed toward research in this topic's movement, while trying to distinguish themselves from similar works by simply adding…

Materials Science · Physics 2022-02-08 Mia Carrola , Amir Asadi , Han Zhang , Dimitrios G. Papageorgiou , Emiliano Bilotti , Hilmar Koerner

Molecule design is a fundamental problem in molecular science and has critical applications in a variety of areas, such as drug discovery, material science, etc. However, due to the large searching space, it is impossible for human experts…

Machine Learning · Computer Science 2022-03-29 Yuanqi Du , Tianfan Fu , Jimeng Sun , Shengchao Liu

Machine learning (ML) plays an increasingly important role in both online and offline event reconstruction and identification at CMS experiment. A variety of ML techniques are used to improve the identification of physics objects. Dedicated…

High Energy Physics - Experiment · Physics 2026-02-10 Uttiya Sarkar

Materials informatics offers a promising pathway towards rational materials design, replacing the current trial-and-error approach and accelerating the development of new functional materials. Through the use of sophisticated data analysis…

Materials Science · Physics 2018-05-17 Cormac Toher , Corey Oses , Stefano Curtarolo