AIP Publishing has announced the first published articles of APL Machine Learning, one of the newest additions to its growing portfolio of fully Open Access journals. The new journal covers the use of machine learning (ML) and artificial intelligence to aid physicists, material scientists, engineers, chemists, and biologists in advancing scientific discovery as well as advances in materials, devices, and systems for the development of future ML technologies.
With the editorial support of that deep bench of luminaries, APL Machine Learning seeks to inform and influence the rapidly growing field of machine learning — a nascent scientific arena with seemingly limitless potential.
AIP Publishing also announced that it will waive article processing charges (APCs) for APL Machine Learning through 2023. The APL Machine Learning editorial team reflects the rich topical and geographic diversity of the field: It includes Dr. Mehonic (Department of Electronic and Electrical Engineering, University College London) and Associate Editors Dr. Shijing Sun (Energy & Materials Division, Toyota Research Institute), Dr. Yuchao Yang (School of Integrated Circuits, Peking University), Dr. Jie Xu (Argonne National Lab), and Dr. Jason K. Eshraghian (Department of Electrical and Computer Engineering, University of California, Santa Cruz).
APL Machine Learning features vibrant and timely research for two communities: researchers who use machine learning (ML) and data-driven approaches for physical sciences and related disciplines, and researchers from these disciplines who work on novel concepts, including materials, devices, systems, and algorithms relevant for the development of better ML and AI technologies. The journal also considers research that substantially describes quantitative models and theories, especially if the research is validated with experimental results.
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