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๐Ÿ” avoidance learning ๐Ÿ“‚ Physics
Showing 28154 results for "avoidance learning" in Physics
Physics Preprint PDF DOI

The Convergence Frontier: Integrating Machine Learning and High Performance Quantum Computing for Next-Generation Drug Discovery

Narjes Ansari, Cesar Feniou, Nicolai Gouraud, Daniele Loco, Siwar Badreddine, Baptiste Claudon, Felix Aviat, Marharyta Blazhynska, Kevin Gasperich, Guillaume Michel, Diata Traore, Corentin Villot, Thomas Ple, Olivier Adjoua, Louis Lagardere, Jean-Philip Piquemal ยท 2026

Integrating quantum mechanics into drug discovery marks a decisive shift from empirical trial-and-error toward quantitative precision. However, the prohibitive cost of ab initio molecular dynamics hasโ€ฆ

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Physics Preprint PDF DOI

Deep learning topological inference-guided $T_{cc}^{+}$ pole parameter extraction

Julius B. Pagayon, Klarence Tomas R. Cervantes, Denny Lane B. Sombillo ยท 2026

We perform a data-driven study of the doubly charmed tetraquark candidate $T_{cc}^+$. An ensemble of deep neural network classifiers, trained on synthetic amplitudes with controlled analytic structureโ€ฆ

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Physics Preprint PDF DOI

Fast stabilizer state preparation via AI-optimized graph decimation

Michael Doherty, Matteo Puviani, Jasmine Brewer, Gabriel Matos, David Amaro, Ben Criger, David T. Stephen ยท 2026

We propose a general method for preparing stabilizer states with reduced two-qubit gate count and depth compared to the state of the art. The method starts from a graph state representation of the staโ€ฆ

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Physics Preprint PDF DOI

Discovery of Bimodal Drift Rate Structure in FRB 20240114A: Evidence for Dual Emission Regions

Santosh Arron ยท 2026

We report the discovery of bimodal structure in the drift rate distribution of upward-drifting burst clusters from the hyperactive repeating fast radio burst FRB 20240114A. Using unsupervised machine โ€ฆ

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Physics Preprint PDF DOI

A 3D physico-chemical model of a pre-stellar core. II. Dynamic chemical evolution in a pre-stellar core model using tracer particles

S. S. Jensen, S. Spezzano, P. Caselli, T. Grassi, O. Sipila, T. Haugb{o}lle ยท 2026

This work explores the differences between static and dynamically evolving physico-chemical models of pre-stellar cores. A 3D MHD model of a pre-stellar core embedded in a dynamic star-forming cloud iโ€ฆ

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Physics Preprint PDF DOI

Interface-dependent Phase Transitions and Ultrafast Hydrogen Superionic Diffusion of H2O Ice

Pengfei Hou, Yumiao Tian, Zifeng Liu, Junwen Duan, Hanyu Liu, Xing Meng, Russell J. Hemley, Yanming Ma ยท 2026

High-pressure experiments using diamond anvils have revealed novel properties and phase behavior of H2O under extreme conditions. When contained in diamond-anvil cells, the H2O samples are usually in โ€ฆ

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Physics Preprint PDF DOI

DustNET: enabling machine learning and AI models of dusty plasmas

Zhehui Wang, Justin C. Burton, Niklas Dormagen, Cheng-Ran Du, Yan Feng, John E. Foster, Susan S. Glenn, Max Klein, Christina A. Knapek, Lorin Matthews, Andre Melzer, Edward Thomas, Chuji Wang, Jalaan Avritte, Shan Chang, Neeraj Chaubey, Pubuduni Ekanayaka, John A. Goree, Truell Hyde, Chen Liang, Zhuang Liu, Zhuang Ma, Ilya Nemenman, Elon Price, A. S. Schmitz, Mike Schwarz, Saikat C. Thakur, M. H. Thoma, Hubertus M. Thomas, L. Wimmer, Wei Yang, Zimu Yang, Xiaoman Zhang ยท 2026

Dusty plasmas are ubiquitous throughout the universe, spanning laboratory and industrial plasmas, fusion devices, planetary environments, cometary comae, and interstellar media. Despite decades of resโ€ฆ

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Physics Preprint PDF DOI

The role of polyelectrolyte brushes in tunable synaptic devices

Esli Diepenbroek, Leon A. Smook, Sissi de Beer ยท 2026

With the ever-increasing digitization of society, the development of materials with low-power memory storage -similar to synapses- is becoming more relevant. The field of iontronic artificial synapsesโ€ฆ

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Rapid Neural Network Prediction of Linear Block Copolymer Free Energies

Ian Chen, Alfredo Alexander-Katz ยท 2026

Free energies are fundamental quantities governing phase behavior and thermodynamic stability in polymer systems, yet their accurate computation often requires extensive simulations and post-processinโ€ฆ

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GPUMDkit: A User-Friendly Toolkit for GPUMD and NEP

Zihan Yan, Denan Li, Xin Wu, Zhoulin Liu, Chen Hua, Boyi Situ, Hao Yang, Shengjie Tang, Benrui Tang, Ziyang Wang, Shangzhao Yi, Huan Wang, Dian Huang, Ke Li, Qilin Guo, Zherui Chen, Ke Xu, Yanzhou Wang, Ziliang Wang, Gang Tang, Shi Liu, Zheyong Fan, Yizhou Zhu ยท 2026

Machine-learned interatomic potentials have revolutionized molecular dynamics simulations by providing quantum-mechanical accuracy at empirical-potential speeds. The graphics processing unit molecularโ€ฆ

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Low-dimensional geometry learning for turbulence prediction in optimized stellarators

Xishuo Wei, Handi Huang, Haotian Chen, Hongxuan Zhu, Zhe Bai, Samuel Williams, Zhihong Lin ยท 2026

The optimized stellarator is an attractive concept for which the averaged particle radial drift is zero, and the single particle loss can be significantly reduced. But for the reactor design, global pโ€ฆ

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Low-Loss Optical Nanofibers with Submicron Waist Diameters and Millimeter-Scale Waist Lengths

Guanghui Su, Timothy H. Nguyen, Balthazar Loglia, Aaron Weinstein, Hanbo Yang, Nami Uchida, Mariam Mchedlidze, Xuejian Wu ยท 2026

Optical nanofibers with subwavelength diameters generate strong evanescent fields, enabling efficient light-matter interactions for optical sensing, spectroscopy, and cold-atom experiments. We report โ€ฆ

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Physics Preprint PDF DOI

Reinforcement Learning for Fast and Robust Longitudinal Qubit Readout

Yiming Yu, Yuan Qiu, Xinyu Zhao, Ye-Hong Chen, Yan Xia ยท 2026

Longitudinal coupling offers a compelling pathway for quantum nondemolition (QND) readout, but pulse design is constrained by hardware limitations such as the coupling strength and the photon number rโ€ฆ

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Polarization Dynamics in Ferroelectrics: Insights Enabled by Machine Learning Molecular Dynamics

Dongyu Bai, Ri He, Junxian Liu, Liangzhi Kou ยท 2026

Ferroelectric materials with switchable spontaneous polarization underpin non-volatile memories, transistors, sensors, and emerging neuromorphic chips. Their performance and stability are governed by โ€ฆ

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Memory-enhanced quantum extreme learning machines for characterizing non-Markovian dynamics

Hajar Assil, Abderrahim El Allati, Gian Luca Giorgi ยท 2026

We use a Quantum Extreme Learning Machine for characterizing and estimating parameters of quantum dynamics generated by a tunable collision model. The input to the learning protocol consists of quantuโ€ฆ

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Stellar characterization with photometric colors from J-PLUS and 2MASS surveys

J. F. Aguilar, P. Cruz, E. Solano, P. R. T. Coelho, A.Ederoclite, V. M. Placco, P. Mas-Buitrago, A. Alvarez-Candal, A.J. Cenarro, D. Cristobal-Hornillos, C. Hernandez-Monteagudo, C. Lopez-Sanjuan, A. Marin-Franch, M. Moles, J. Varela, H. Vazquez Ramio, J. Alcaniz, R.A. Dupke, L. Sodre Jr, R.E. Angulo ยท 2026

Aims. We aim at deriving stellar atmospheric parameters based on the photometric data from the Javalambre Photometric Local Universe Survey (J-PLUS) in addition to near-infrared photometry from the Twโ€ฆ

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Compton-thick AGN in the NuSTAR Era. XI. Analyzing 11 CT-AGN Candidates Selected with Machine Learning

Ross Silver, Nuria Torres-Alba, Stefano Marchesi, Vittoria Gianolli, Isaiah Cox, Dhrubojyoti Sengupta, Indrani Pal, Marco Ajello, Xiurui Zhao, Kouser Imam, Anuvab Banerjee ยท 2026

This work discusses the broadband X-ray spectral analysis of 11 candidate heavily-obscured active galactic nuclei (AGN) selected based on their infrared and X-ray properties by a recently published maโ€ฆ

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Optimization-Embedded Active Multi-Fidelity Surrogate Learning for Multi-Condition Airfoil Shape Optimization

Isaac Robledo, Alberto Vilarino, Arnau Miro, Oriol Lehmkuhl, Carlos Sanmiguel Vila, Rodrigo Castellanos ยท 2026

Active multi-fidelity surrogate modeling is developed for multi-condition airfoil shape optimization to reduce high-fidelity CFD cost while retaining RANS-level accuracy. The framework couples a low-fโ€ฆ

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Accelerating Structure-Property Relationship Discovery with Multimodal Machine Learning and Self-Driving Microscopy

Jiawei Gong, Danqing Ma, Ralph Bulanadi, Robert Moore, Rama Vasudevan, Lianfeng Zhao, Yongtao Liu ยท 2026

Microscopy combined with local spectroscopy is widely used to correlate nanoscale structure with functional properties in materials, but conventional measurements rely heavily on human-selected sampliโ€ฆ

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Physics Preprint PDF DOI

Machine Learning Reconstruction of High-Dimensional Electronic Structure from Angle-Resolved Photoemission Spectroscopy

Yu Zhang, Yong Zhong, Nhat Huy Tran, Shuyi Li, Kyuho Lee, Yonghun Lee, Tiffany C. Wang, Harold Y. Hwang, Zhi-Xun Shen, Chunjing Jia ยท 2026

The emergent behavior of quantum materials is governed by their electronic structure, which can be experimentally probed by photoemission spectroscopy techniques that generate a four-dimensional datasโ€ฆ

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