Exploring the Vibrant Interplay of Machine Learning and Physics
© The Physical Society of Japan
This article is on
JPSJ Special Topics: Machine Learning Physics (11 articles)
J. Phys. Soc. Jpn.
Vol. 94 No. 3
(2025)
.
This Journal of the Physical Society of Japan Special Topics edition explores how physics and machine learning complement each other and can solve unresolved problems in physics.
Machine learning techniques have grown rapidly in recent years, and have become indispensable in many fields, including physics. For example, neural networks effectively represent wave functions in many-body quantum systems, where they have been applied to detect phase transitions. Moreover, Monte Carlo sampling and molecular dynamics simulations can be made significantly faster using machine learning.
Conversely, fundamental concepts in physics such as the Langevin equation and symmetries have significantly contributed to the growth of machine learning.
Now, a Special Topics edition of the Journal of the Physical Society of Japan presents 11 review articles exploring the interplay of physics and machine learning. The first part of the issue explores the applications of machine learning in physics.
Nomura and Imada demonstrate how artificial neural networks, such as restricted Boltzmann machines, represent wave functions in many-body quantum systems, shedding light on high-temperature superconductivity and quantum spin liquid.
Bayo, Çivitcioğlu, Webb, Honecker, and Römer review the recent advancements in the application of machine learning techniques like deep neural networks for detecting phase transitions—a fundamental challenge in condensed matter physics.
Mochizuki and Miyajima explore the application of machine learning image recognition techniques for detecting Berezinskii-Kosterlitz-Thouless transitions, which are notoriously difficult using traditional statistical methods.
Nakamura presents a comprehensive review of Gaussian process regression—a probabilistic machine learning technique that does not rely on neural networks, using magnetic properties as a case study.
Yamaji discusses how machine learning techniques can be used for analyzing experimental data, focusing on the application of neural networks in studying self-energy spectroscopy, which is critical for understanding concepts of superconductivity.
Tomiya highlights the potential of neural networks to accelerate quantum chromodynamic Monte Carlo simulations, demonstrating how machine learning can reduce computational costs while improving accuracy.
Transformer models are foundational to the recent advancements in large-language models. Hammad and Nojiri review the application of attention-based Transformer networks in the identification of heavy-boosted particles in high-energy colliders.
Furthermore, the second part of the issue focuses on how physics can advance machine learning.
Hirono presents a review of diffusion models, which are widely used in image, audio, and video generation, investigating their similarities to the diffusion process described by the Langevin equation.
Approximating a given probability distribution with another distribution is a standard technique in physics. Tanaka reviews generative diffusion models, which automate this procedure, from probabilistic and group theoretical perspectives.
Kamata and Fukushima expand on this review by examining the connection between stochastic quantization and diffusion models.
Takahashi demonstrates how the replica method from statistical mechanics can be used to analyze high-dimensional variable selection in machine learning.
This Special Edition thus offers a comprehensive overview of the vibrant intersections between physics and machine learning. As these domains continue to progress together, they hold great potential for addressing some of the most significant scientific challenges of the era.
JPSJ Special Topics: Machine Learning Physics (11 articles)
J. Phys. Soc. Jpn.
Vol. 94 No. 3
(2025)
.
Share this topic
Fields
Related Articles
-
Excitonic Insulators: Challenges in Realizing a Theoretically Predicted State of Matter
Electron states in condensed matter
Electronic transport in condensed matter
2025-3-3
The realization of an excitonic insulator can help in the establishment of a new electronic state in condensed matter physics, one that has the potential to exhibit novel electric, magnetic, and optical responses beyond those of conventional materials.
-
Traffic Signal Optimization using Quantum Annealing
Mathematical methods, classical and quantum physics, relativity, gravitation, numerical simulation, computational modeling
2025-2-25
This paper introduces a QUBO model for traffic signal optimization on real-world maps. Using D-Wave quantum annealing and SUMO simulations, the model reduces vehicle waiting times, but underperforms classical solvers.
-
Hyperuniform and Multifractal States in Bosonic Quasicrystalline Systems
Statistical physics and thermodynamics
Structure and mechanical and thermal properties in condensed matter
2025-2-10
Quantum states can be categorized as hyperuniform or multifractal based on electronic characteristics. This study demonstrates that bosonic quasicrystalline systems exhibit hyperuniform or multifractal quantum states.
-
Exploring Materials without Data Exposure: A Bayesian Optimizer using Secure Computation
Cross-disciplinary physics and related areas of science and technology
Measurement, instrumentation, and techniques
2025-2-6
Secure computation allows the manipulation of material data without exposing them, thereby offering an alternative to traditional open/closed data management. We recently reported the development of an application that performs Bayesian optimization using secure computation.
-
Triangular Lattice Magnet GdGa2: Spin Cycloids and Skyrmions
Cross-disciplinary physics and related areas of science and technology
Electronic transport in condensed matter
Magnetic properties in condensed matter
2025-2-3
Careful measurements were conducted on the hexagonal magnet GdGa2 to reveal the experimental signatures of ultrasmall spin cycloids and of a potential Néel-type skyrmion lattice phase induced by a magnetic field.