MELVILLE, N.Y., Feb. 14, 2023 — AIP Publishing is thrilled to announce 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.
“The theme of applying machine learning for scientific discovery has found its way across most journals within the AIP Publishing portfolio, not only through individual papers but also through dedicated collections, special issues, and roadmaps. The time is ripe for launching a new open access, specialized journal to serve the community, and I have the great privilege of being the first Editor-in-Chief of APL Machine Learning,” said Dr. Adnan Mehonic. “I am equally privileged to work with an outstanding, diverse team of Associate Editors and Editorial Advisory Board members recognized for their contributions to the field.”
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.
“These are exciting times, not just for AIP Publishing and the editors and staff of APL Machine Learning, but also for the field itself,” said Dr. Penelope Lewis, Chief Publishing Officer at AIP Publishing. “It’s an honor to have Dr. Mehonic at the helm of this journal as it begins its journey. APL Machine Learning brings readers and researchers all the benefits of being a fully open access journal — such as greater visibility and increased citations — and we look forward to seeing its impact on the research and development of novel materials, functionality, and systems in the coming years.”
The following articles are now available as part of the first issue of APL Machine Learning:
“Editorial: Welcome to APL Machine Learning,” from Dr. Mehonic, welcomes readers to APL Machine Learning and lays out the mission and drive of this exciting new journal.
“Deep language models for interpretative and predictive materials science,” from Yiwen Hu and Markus J. Buehler, discusses the challenges and opportunities presented by deep language models in materials science and outlines how tools such as ChatGPT and DALL·E can drive materials discovery.
“In-memory computing with emerging memory devices: Status and outlook,” from P. Mannocci, M. Farronato, N. Lepri, L. Cattaneo, A. Glukhov, Z. Sun, and D. Ielmini, aims to provide an orientation map across the wide topic of in-memory computing, a new computing paradigm able to alleviate or suppress memory bottlenecks to improve energy efficiency and latency.
“Benchmarking energy consumption and latency for neuromorphic computing in condensed matter and particle physics,” from Dominique J. Kösters, Bryan A. Kortman, Irem Boybat, Elena Ferro, Sagar Dolas, Roberto Ruiz de Austri, Johan Kwisthout, Hans Hilgenkamp, Theo Rasing, Heike Riel, Abu Sebastian, Sascha Caron, and Johan H. Mentink, presents a methodology for measuring the energy cost and compute time for inference tasks with artificial neural networks on conventional hardware.
“Machine learning assisted interpretation of creep and fatigue life in titanium alloys,” from Sucheta Swetlana, Ashish Rout, and Abhishek Kumar Singh, proposes an interpretable machine learning approach to predict fatigue life cycles and creep rupture life in titanium-based alloys.
“Artificial neural network-based streamline tracing strategy applied to hypersonic waverider design,” from Anagha G. Rao, Umesh Siddharth, and Srisha M. V. Rao, provides a novel strategy where an artificial neural network is trained to directly predict streamlines to design high-performance waveriders and intakes.
“Impact of analog memory device failure on in-memory computing inference accuracy,” from Ning Li, Hsinyu Tsai, Vijay Narayanan, and Malte Rasch, studies the impact of failed non-volatile memory devices on the analog in-memory computing accuracy of various networks.
“An efficiency-driven, correlation-based feature elimination strategy for small datasets,” from Carolin A. Rickert, Manuel Henkel, and Oliver Lieleg, introduces a new algorithm that is model-independent, does not require an output label, and is applicable to all kinds of correlation topographies within a dataset — making it a potentially beneficial preprocessing tool.
AIP Publishing is also pleased to note 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).
About APL Machine Learning
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.
About AIP Publishing
AIP Publishing’s mission is to advance, promote, and serve the physical sciences for the benefit of humanity by breaking barriers to open, equitable research communication and empowering researchers to accelerate global progress. AIP Publishing is a wholly owned not-for-profit subsidiary of the American Institute of Physics (AIP) and supports the charitable, scientific, and educational purposes of AIP through scholarly publishing activities on its behalf and on behalf of our publishing partners.