arXiv Machine Learning

Writhe-Based Polymer Link Classification Using Machine Learning

arXiv:2607. 20657v1 Announce Type: cross Abstract: Unique and rapid classification of knots and links is an open mathematical problem that is relevant to a range of (bio)physical systems, including polymer melts, DNA, and proteins.

arXiv AI

1d ago

Periodic Topological Deep Learning for Polymer Design and Discovery

arXiv:2605. 26833v2 Announce Type: replace-cross Abstract: Polymers underpin applications across energy, healthcare, and materials science, yet their vast chemical space makes systematic discovery challenging.

By Yasharth Yadav, Tze Kwang Gerald Er, Atsushi Goto, Kelin Xia

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Hugging Face Trending Papers

Jun 4

$p$-adic Bi-Filtrations for Topological Machine Learning on Genomic Sequences

We introduce pVR, a topological machine learning framework for alignment-free genomic sequence classification that combines $p$-adic numbers with topological data analysis. Each DNA sequence is encoded along two complementary axes: a $p$-adic distance on $k$-mer prefixes, which captures hierarchical positional structure, and a compositional $L_1$ distance on $k$-mer frequencies, which captures local sequence content.

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arXiv Machine Learning

Jun 5

$p$-adic Bi-Filtrations for Topological Machine Learning on Genomic Sequences

arXiv:2606. 06117v1 Announce Type: cross Abstract: We introduce pVR, a topological machine learning framework for alignment-free genomic sequence classification that combines $p$-adic numbers with topological data analysis.

By Tirtharaj Dash, Gunja Sachdeva

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arXiv AI

Jul 7

Inelastic Constitutive Kolmogorov-Arnold Networks: A generalized framework for automated discovery of interpretable inelastic material models

arXiv:2602. 17750v2 Announce Type: replace-cross Abstract: A key problem of solid mechanics is the identification of the constitutive law of a material, that is, the relation between strain history and stress.

By Chenyi Ji, Kian P. Abdolazizi, Hagen Holthusen, Christian J. Cyron, Kevin Linka

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arXiv Machine Learning

Jul 31

Topological Data Analysis combined with Machine Learning for Predicting Permeability of Porous Media

arXiv:2605. 17581v2 Announce Type: replace-cross Abstract: Flow in porous media is difficult to address using standard analytical or numerical methods due to its complexity.

By Ebru Dagdelen, Catherin Neena Lalu, Aakash Karlekar, Manav Arora, Matthew Illingworth, Jonathan Jaquette, Linda Cummings, Lou Kondic

arXiv AI

Jul 29

Dual-Level Atomic and Coordination Geometry Learning for Crystal Property Prediction Using Graph Neural Networks

arXiv:2607. 24818v1 Announce Type: cross Abstract: Accurate prediction of crystal properties remains a key challenge in computational materials science.

By Sanjay Chakraborty

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arXiv Machine Learning

Jun 15

Traditional machine learning vs. deep learning from dynamic graph representations of proteins' 3D folds in the task of protein structure classification

arXiv:2605. 29228v2 Announce Type: replace Abstract: Protein structure classification (PSC) uses supervised learning to predict a protein's CATH/SCOP(e) class from the protein's sequence or 3D structural feature(s).

By Aydin Wells, Francis A. Gatsi, Aaron Striegel, Tijana Milenkovi\'c

arXiv AI

1d ago

Empowering Polymeric Materials Discovery by Artificial Intelligence

arXiv:2606. 20753v2 Announce Type: replace-cross Abstract: Polymeric materials underpin modern technologies spanning energy storage, microelectronics, healthcare and sustainable manufacturing.

By Chenyao Ma, Linda Zhang, Yuheng Chen, Wei Du, Shangwen Fang, Zihao Jiang, Chuanyu Liu, Xinyu Ma, Rui Su, Gang Wang, Muyao Yu, Dong Zhong, Jie Zhu, Weibo Gong, Huan Gu, Limin Li, Chen Shen, Rui Wu, Zhenghao Wu, Kan Xu, Min Zhou, Donglin He, Xiayun Huang, Shan Jiang, Pengfei Ou, Jiayu Peng, Yuwei Zhang, Jie Zhao, Di Zhang, Piao Ma, Zhenghao Li, Hao Li

agents

arXiv Machine Learning

Jun 16

Learning Topological Representations for Molecular Dynamics

arXiv:2606. 14737v1 Announce Type: cross Abstract: Molecular dynamics (MD) simulations generate trajectories in a high-dimensional configuration space whose analysis critically depends on molecular descriptors, typically handcrafted observables or learned kinetic embeddings.

By Dominik Geng, Florian Graf, Martin Uray, Roland Kwitt

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arXiv Machine Learning

Jun 4

SPLIT-PINN: Separable Probability Learning Technique via Physics-Informed Neural Networks for High-Dimensional Probabilistic Modeling

arXiv:2606. 04000v1 Announce Type: cross Abstract: We present a probabilistic modeling framework for incorporating small-scale spatial heterogeneity into macroscopic descriptions of material behavior for polycrystalline metallic materials.

By Pouria Behnoudfar, Deekshith Naidu Ponnana, Noah J. Schmelzer, Janith Wanni, George T. Gray III, Dan J. Thoma, Curt A. Bronkhorst, Nan Chen, Wenxiao Pan

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arXiv Machine Learning

Jun 11

From Persistence to Survival: Hypothesis Testing, Effect Sizes and Vectorisation for Topological Features

arXiv:2606. 11911v1 Announce Type: cross Abstract: Persistence diagrams are common representations in topological data analysis, but they do not naturally live in a vector space, and the statistical tools developed for comparing them have largely evolved separately from those used for downstream prediction.

By Juliette Murris, Bernadette Stolz, Karsten Borgwardt

benchmarks

arXiv Machine Learning

Jul 8

Multimodal Molecular Representation Learning with Graph Neural Networks, Deep & Cross Networks, and SMILES Embeddings

arXiv:2607. 05736v1 Announce Type: new Abstract: Molecular property prediction often relies on isolated data modalities, where continuous 3D graph neural networks (GNNs) struggle to efficiently capture long-range topological dependencies and exact macroscopic heuristics.

By Qiwei Han, Chi Zhou, Ruobing Wang, Zheng Ma

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