Countering adversarial perturbations in graphs using error correcting codes DOI
Saif Eddin Jabari

Physical review. E, Journal Year: 2024, Volume and Issue: 110(4)

Published: Oct. 15, 2024

We consider the problem of a graph subjected to adversarial perturbations, such as those arising from cyber attacks, where edges are covertly added or removed. The perturbations occur during transmission between sender and receiver. To counteract potential this study explores repetition coding scheme with sender-assigned noise majority voting on receiver's end rectify graph's structure. approach operates without prior knowledge attack's characteristics. analytically derive bound number repetitions needed satisfy probabilistic constraints quality reconstructed graph. method can accurately effectively decode Erdős-Rényi graphs that were nonrandom edge removal, namely, connected vertices highest eigenvector centrality, in addition random removal by attacker. is also effective against attacks large scale-free generated using Barabási-Albert model but require larger than correct graphs.

Language: Английский

Recent Advances in Machine Learning‐Assisted Multiscale Design of Energy Materials DOI Creative Commons
Bohayra Mortazavi

Advanced Energy Materials, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 10, 2024

Abstract This review highlights recent advances in machine learning (ML)‐assisted design of energy materials. Initially, ML algorithms were successfully applied to screen materials databases by establishing complex relationships between atomic structures and their resulting properties, thus accelerating the identification candidates with desirable properties. Recently, development highly accurate interatomic potentials generative models has not only improved robust prediction physical but also significantly accelerated discovery In past couple years, methods have enabled high‐precision first‐principles predictions electronic optical properties for large systems, providing unprecedented opportunities science. Furthermore, ML‐assisted microstructure reconstruction physics‐informed solutions partial differential equations facilitated understanding microstructure–property relationships. Most recently, seamless integration various platforms led emergence autonomous laboratories that combine quantum mechanical calculations, language models, experimental validations, fundamentally transforming traditional approach novel synthesis. While highlighting aforementioned advances, existing challenges are discussed. Ultimately, is expected fully integrate atomic‐scale simulations, reverse engineering, process optimization, device fabrication, empowering system design. will drive transformative innovations conversion, storage, harvesting technologies.

Language: Английский

Citations

17
Machine Learning Interatomic Potentials for Catalysis DOI Creative Commons

Deqi Tang,

Rangsiman Ketkaew, Sandra Luber

et al.

Chemistry - A European Journal, Journal Year: 2024, Volume and Issue: 30(60)

Published: Aug. 7, 2024

Atomistic modeling can provide valuable insights into the design of novel heterogeneous catalysts as needed nowadays in areas of, e. g., chemistry, materials science, and biology. Classical force fields ab initio calculations have been widely adopted molecular simulations. However, these methods usually suffer from drawbacks either low accuracy or high cost. Recently, development machine learning interatomic potentials (MLIPs) has become more popular they tackle problems question deliver rather accurate results at significantly lower computational In this review, atomistic catalytic systems with aid MLIPs is discussed, showcasing recently developed MLIP models selected applications for systems. We also highlight best practices challenges give an outlook future works on field catalysis.

Language: Английский

Citations

8
Interatomic Interaction Models for Magnetic Materials: Recent Advances DOI

Tatiana Kostiuchenko,

Alexander V. Shapeev,

Ivan S. Novikov

et al.

Chinese Physics Letters, Journal Year: 2024, Volume and Issue: 41(6), P. 066101 - 066101

Published: May 14, 2024

Abstract Atomistic modeling is a widely employed theoretical method of computational materials science. It has found particular utility in the study magnetic materials. Initially, empirical interatomic potentials or spin-polarized density functional theory (DFT) served as primary models for describing interactions atomistic simulations systems. Furthermore, recent years, new class known machine-learning (magnetic MLIPs) emerged. These MLIPs combine efficiency, terms CPU time, with accuracy DFT calculations. In this review, our focus lies on providing comprehensive summary interaction developed specifically investigating We also delve into various problem classes to which these can be applied. Finally, we offer insights future prospects model development exploration

Language: Английский

Citations

4
Topological interfacial states at phase boundaries in two-dimensional ferroelectric bismuth DOI
Wei Luo, Yang Zhong, Hongyu Yu

et al.

Physical review. B./Physical review. B, Journal Year: 2025, Volume and Issue: 111(7)

Published: Feb. 7, 2025

Language: Английский

Citations

0
Dynamics of ferroelectricity in monolayer AgCr2S4 calculated with a machine learning potential DOI

Junchi Wu,

Haoran Zhu, Xuanyi Li

et al.

Physical review. B./Physical review. B, Journal Year: 2025, Volume and Issue: 111(8)

Published: Feb. 18, 2025

Language: Английский

Citations

0
A practical guide to machine learning interatomic potentials – Status and future DOI
Ryan Jacobs,

Dane Morgan,

Siamak Attarian

et al.

Current Opinion in Solid State and Materials Science, Journal Year: 2025, Volume and Issue: 35, P. 101214 - 101214

Published: Feb. 26, 2025

Language: Английский

Citations

0
The Future of Catalysis: Applying Graph Neural Networks for Intelligent Catalyst Design DOI

Zhihao Wang,

Wentao Li, Siying Wang

et al.

Wiley Interdisciplinary Reviews Computational Molecular Science, Journal Year: 2025, Volume and Issue: 15(2)

Published: March 1, 2025

ABSTRACT With the increasing global demand for energy transition and environmental sustainability, catalysts play a vital role in mitigating climate change, as they facilitate over 90% of chemical material conversions. It is important to investigate complex structures properties enhanced performance, which artificial intelligence (AI) methods, especially graph neural networks (GNNs) could be useful. In this article, we explore cutting‐edge applications future potential GNNs intelligent catalyst design. The fundamental theories their practical catalytic simulation inverse design are first reviewed. We analyze critical roles accelerating screening, performance prediction, reaction pathway analysis, mechanism modeling. By leveraging convolution techniques accurately represent molecular structures, integrating symmetry constraints ensure physical consistency, applying generative models efficiently space, these approaches work synergistically enhance efficiency accuracy Furthermore, highlight high‐quality databases crucial catalysis research innovative application thermocatalysis, electrocatalysis, photocatalysis, biocatalysis. end, key directions advancing catalysis: dynamic frameworks real‐time conditions, hierarchical linking atomic details features, multi‐task interpretability mechanisms reveal pathways. believe advancements will significantly broaden science, paving way more efficient, accurate, sustainable methodologies.

Language: Английский

Citations

0
Actively trained magnetic moment tensor potentials for mechanical, dynamical, and thermal properties of paramagnetic CrN DOI

Alexey S. Kotykhov,

Max Hodapp, Christian Tantardini

et al.

Physical review. B./Physical review. B, Journal Year: 2025, Volume and Issue: 111(9)

Published: March 27, 2025

We present a protocol for automated fitting of magnetic moment tensor potential explicitly including moments in its functional form. For the this we use energies, forces, stresses, and forces (negative derivatives energies with respect to moments) configurations selected an active learning algorithm. These are computed using constrained density theory, which enables calculating their both equilibrium nonequilibrium (excited) states. test our on system B1-CrN demonstrate that automatically trained reproduces mechanical, dynamical, thermal properties paramagnetic state theory experiments.

Language: Английский

Citations

0
A predictive machine learning force-field framework for liquid electrolyte development DOI
Sheng Gong, Yumin Zhang, Zhenliang Mu

et al.

Nature Machine Intelligence, Journal Year: 2025, Volume and Issue: unknown

Published: April 1, 2025

Language: Английский

Citations

0
Simultaneous optimization of lattice and spin configurations in atomic scale simulation of magnetic materials DOI
Zhengtao Huang,

Teng Yang,

Hanxing Liu

et al.

Physical review. B./Physical review. B, Journal Year: 2025, Volume and Issue: 111(13)

Published: April 8, 2025

Language: Английский

Citations

0