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

850+ Avg. views/article annually
92 Days avg. time to acceptance^‡
36 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:

MechanoEngineering

Publishing open research at the intersection of mechanics and emerging technologies in the digital-AI era

First Issue Now Online

APL Computational Physics

Showcasing the transformative impact of computation across all physical science

First Issue Now Online

Journal of Applied Physics

Significant results in cutting-edge applied physics

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Applied Physics Letters

Shaping the future of applied physics for over 60 years

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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

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Applied Physics Reviews

High-impact research and authoritative reviews 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: First International Conference on Applied Data Science and Smart Systems
	Preface: IV International Scientific Forum on Computer and Energy Sciences (WFCES II 2022)

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Highlighted Research

Digitizing images of electrical-circuit schematics

Electrical-circuit schematics are a foundational tool in electrical engineering. A method that can automatically digitalize them is desirable since a knowledge base of such schematics could preserve their functional information as well as provide a database that one can mine to predict more operationally efficient electrical circuits using data analytics and machine learning.

Efficiency of machine learning optimizers and meta-optimization for nanophotonic inverse design tasks

The success of deep learning has driven the proliferation and refinement of numerous non-convex optimization algorithms. Despite this growing array of options, the field of nanophotonic inverse design continues to rely heavily on quasi-Newton optimizers such as L-BFGS and basic momentum-based methods such as Adam. A systematic survey of these and other algorithms in the nanophotonics context remains lacking.

In-memory and in-sensor reservoir computing with memristive devices

Despite the significant progress made in deep learning on digital computers, their energy consumption and computational speed still fall short of meeting the standards for brain-like computing. To address these limitations, reservoir computing (RC) has been gaining increasing attention across communities of electronic devices, computing systems, and machine learning, notably with its in-memory or in-sensor implementation on the hardware–software co-design.

A grain boundary embrittlement genome for substitutional cubic alloys

Grain boundary chemistry plays a critical role for the properties of metals and alloys, yet there is a lack of consistent datasets for alloy design and development. With the advent of artificial intelligence and machine learning in materials science, open materials models and datasets can be used to overcome such challenges.

Combining computational screening and machine learning to explore MOFs and COFs for methane purification

Metal-organic frameworks (MOFs) and covalent organic frameworks (COFs) have great potential to be used as porous adsorbents and membranes to achieve high-performance methane purification. Although the continuous increase in the number and diversity of MOFs and COFs is a great opportunity for the discovery of novel adsorbents and membranes with superior performances, evaluating such a vast number of materials in the quickest and most effective manner requires the development of computational approaches.

BubbleID: A deep learning framework for bubble interface dynamics analysis

This paper presents BubbleID, a sophisticated deep learning architecture designed to comprehensively identify both static and dynamic attributes of bubbles within sequences of boiling images. By amalgamating segmentation powered by Mask R-CNN with SORT-based tracking techniques, the framework is capable of analyzing each bubble's location, dimensions, interface shape, and velocity over its lifetime and capturing dynamic events such as bubble departure.

First-principles-based machine learning interatomic potential for molecular dynamics simulations of 2D lateral MoS2/WS2 heterostructures

Understanding the mechanical and thermodynamic properties of transition-metal dichalcogenides (TMDs) and their heterostructures is pivotal for advancing the development of flexible semiconductor devices, and molecular dynamics (MD) simulation is widely applied to study these properties. However, current uncertainties persist regarding the efficacy of empirical potentials in MD simulations to accurately describe the intricate performance of complex interfaces within heterostructures. This study addresses these challenges by developing an interatomic potential based on deep neural networks and first-principles calculations.

Comparing machine learning potentials for water: Kernel-based regression and Behler–Parrinello neural networks

In this paper, we investigate the performance of different machine learning potentials (MLPs) in predicting key thermodynamic properties of water using RPBE + D3. Specifically, we scrutinize kernel-based regression and high-dimensional neural networks trained on a highly accurate dataset consisting of about 1500 structures, as well as a smaller dataset, about half the size, obtained using only on-the-fly learning.

Molecular hypergraph neural networks

Graph neural networks (GNNs) have demonstrated promising performance across various chemistry-related tasks. However, conventional graphs only model the pairwise connectivity in molecules, failing to adequately represent higher order connections, such as multi-center bonds and conjugated structures.

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*Data from the 2024 Journal Citation Reports® Science Edition (Clarivate, 2025).
†CiteScore™ 2024 for AIP Publishing Journals Calculated by Scopus
‡Publication speeds vary depending on article type