Explore Machine Learning With AIP Publishing

Discover our extensive collection of journals, conference proceedings, and books that span Machine Learning and its intersections with other disciplines.

As your partner in publishing, whether you’re just beginning your journey or have years of experience, we’re here to provide you with the best home for your work and the tools and resources you need to elevate and amplify your research.

Featured Journals

Journals dedicated to research in Machine Learning:

APL Machine Learning

Machine Learning for Applied Physics
Applied Physics for Machine Learning  

1115+ Avg. views/article annually
112 Days avg. time to acceptance
37 Days avg. time to first decision
Topics covered: Machine learning, artificial intelligence, neural network dynamics, data mining, signal processing, robotics
Article types: Articles, Reviews, Perspectives, Tutorials
Access type: Gold open access
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Interdisciplinary Journals

Journals at the intersection of Machine Learning and related disciplines:

Journal of Applied Physics

Significant results in cutting-edge applied physics

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Applied Physics Letters

Shaping the future of applied physics

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The Journal of Chemical Physics

The most cited in chemical physics

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AIP Advances

A peer-reviewed, open access journal covering all areas of the physical sciences

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APL Quantum

Bridging fundamental quantum research with technological applications

First Issue Now Online

Applied Physics Reviews

High impact results in applied physics

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Chaos

An Interdisciplinary Journal of Nonlinear Science

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More Journals to Explore

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Featured AIP Conference Proceedings

AIP Conference Proceedings has been a trusted publishing partner for more than 40 years, delivering fast, affordable, and versatile publishing for maximum exposure of your meeting’s key research. Below are just a few of the Proceedings that are rooted in Machine Learning research.    

	Preface: Second International Conference on Computing and Communication Networks (ICCCN 2022)
	Preface: IV International Scientific Forum on Computer and Energy Sciences (WFCES II 2022)
	Preface: 11th Int’l Conference on Mathematical Modeling in Physical Sciences

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Highlighted Research

Deep language models for interpretative and predictive materials science

Machine learning (ML) has emerged as an indispensable methodology to describe, discover, and predict complex physical phenomena that efficiently help us learn underlying functional rules, especially in cases when conventional modeling approaches cannot be applied.

In-memory computing with emerging memory devices: Status and outlook

In-memory computing (IMC) has emerged as a new computing paradigm able to alleviate or suppress the memory bottleneck, which is the major concern for energy efficiency and latency in modern digital computing. While the IMC concept is simple and promising, the details of its implementation cover a broad range of problems and solutions, including various memory technologies, circuit topologies, and programming/processing algorithms.

Resistance transient dynamics in switchable perovskite memristors

Memristor devices have been investigated for their properties of resistive modulation that can be used in data storage and brain-like computation elements as artificial synapses and neurons. Memristors are characterized by an onset of high current values under applied voltage that produces a transition to a low resistance state or successively to different stable states of increasing conductivity that implement synaptic weights.

A machine learning framework for elastic constants predictions in multi-principal element alloys

On the one hand, multi-principal element alloys (MPEAs) have created a paradigm shift in alloy design due to large compositional space, whereas on the other, they have presented enormous computational challenges for theory-based materials design, especially density functional theory (DFT), which is inherently computationally expensive even for traditional dilute alloys.

DyFraNet: Forecasting and backcasting dynamic fracture mechanics in space and time using a 2D-to-3D deep neural network

The dynamics of material failure is a critical phenomenon relevant to a range of scientific and engineering fields, from healthcare to structural materials. We propose a specially designed deep neural network, DyFraNet, which can predict dynamic fracture behaviors by identifying a complete history of fracture propagation—from the onset of cracking, as a crack grows through the material, modeled as a series of frames evolving over time and dependent on each other.

Machine learning guided optimal composition selection of niobium alloys for high temperature applications

Nickel- and cobalt-based superalloys are commonly used as turbine materials for high-temperature applications. However, their maximum operating temperature is limited to about 1100 °C.

Physics-constrained 3D convolutional neural networks for electrodynamics

We present a physics-constrained neural network (PCNN) approach to solving Maxwell’s equations for the electromagnetic fields of intense relativistic charged particle beams.

Materials cartography: A forward-looking perspective on materials representation and devising better maps

Machine learning (ML) is gaining popularity as a tool for materials scientists to accelerate computation, automate data analysis, and predict materials properties. The representation of input material features is critical to the accuracy, interpretability, and generalizability of data-driven models for scientific research.

Machine learning assisted interpretation of creep and fatigue life in titanium alloys

Making reliable predictions of the mechanical behavior of alloys with a prolonged service life is beneficial for many structural applications.

Brains and bytes: Trends in neuromorphic technology

The term “neuromorphic” was originally introduced by Mead in the late 1980s,¹ referring to devices and systems that imitated certain elements of biological neural systems. However, today the interpretation of the term has diverged across different research communities.

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*Data from the 2023 Journal Citation Reports® Science Edition (Clarivate, 2024).
†CiteScore™ 2023 for AIP Publishing Journals Calculated by Scopus