Imagen de Portada

PolySet: Restoring the Statistical Ensemble Nature of Polymers for Machine Learning

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autor principal:	Ferji, Khalid
Formato:	Preprint
Publicado:	2025
Materias:	Machine Learning Materials Science Artificial Intelligence
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Machine Learned Force Fields: Fundamentals, its reach, and challenges
por: Vital, Carlos A., et al.
Publicado: (2025)

BOOM: Benchmarking Out-Of-distribution Molecular Property Predictions of Machine Learning Models
por: Antoniuk, Evan R., et al.
Publicado: (2025)

POINT$^{2}$: A Polymer Informatics Training and Testing Database
por: Xu, Jiaxin, et al.
Publicado: (2025)

Energy & Force Regression on DFT Trajectories is Not Enough for Universal Machine Learning Interatomic Potentials
por: Miret, Santiago, et al.
Publicado: (2025)

CycleChemist: A Dual-Pronged Machine Learning Framework for Organic Photovoltaic Discovery
por: Lam, Hou Hei, et al.
Publicado: (2025)

MechProNet: Machine Learning Prediction of Mechanical Properties in Metal Additive Manufacturing
por: Akbari, Parand, et al.
Publicado: (2022)

Machine Learning - Driven Materials Discovery: Unlocking Next-Generation Functional Materials - A review
por: Nematov, Dilshod, et al.
Publicado: (2025)

Can LLMs Predict Polymer Physics Just by Reading Synthesis and Processing Prose?
por: Liu, Yuchu, et al.
Publicado: (2026)

The Infinite-Dimensional Nature of Spectroscopy and Why Models Succeed, Fail, and Mislead
por: Michelucci, Umberto, et al.
Publicado: (2026)

Machine Learning-Driven Optimization of TPMS Architected Materials Using Simulated Annealing
por: Mishra, Akshansh
Publicado: (2024)

Predicting Euler Characteristics and Constructing Topological Structure Using Machine Learning Techniques
por: Yu, Gyunghun, et al.
Publicado: (2026)

High-Throughput Computational Screening and Interpretable Machine Learning of Metal-organic Frameworks for Iodine Capture
por: Tan, Haoyi, et al.
Publicado: (2025)

Fine-Tuning Vision-Language Models for Multimodal Polymer Property Prediction
por: Vuong, An, et al.
Publicado: (2025)

Interaction Topological Transformer for Multiscale Learning in Porous Materials
por: Chen, Dong, et al.
Publicado: (2025)

XxaCT-NN: Structure Agnostic Multimodal Learning for Materials Science
por: Subramanian, Jithendaraa, et al.
Publicado: (2025)

Material Property Prediction with Element Attribute Knowledge Graphs and Multimodal Representation Learning
por: Huang, Chao, et al.
Publicado: (2024)

Steering an Active Learning Workflow Towards Novel Materials Discovery via Queue Prioritization
por: Schwarting, Marcus, et al.
Publicado: (2025)

Crack Path Prediction with Operator Learning using Discrete Particle System data Generation
por: Kiyani, Elham, et al.
Publicado: (2025)

AIMatDesign: Knowledge-Augmented Reinforcement Learning for Inverse Materials Design under Data Scarcity
por: Yu, Yeyong, et al.
Publicado: (2025)

OmniXAS: A Universal Deep-Learning Framework for Materials X-ray Absorption Spectra
por: Kharel, Shubha R., et al.
Publicado: (2024)

From Evaluation to Design: Using Potential Energy Surface Smoothness Metrics to Guide Machine Learning Interatomic Potential Architectures
por: Liu, Ryan, et al.
Publicado: (2026)

Meta-LegNet: A Transferable and Interpretable Framework for Surface Adsorption Prediction via Self-Defined Adsorption-Environment Learning
por: Li, Yifan, et al.
Publicado: (2026)

Machine Learning-Based Reward-Driven Tuning of Scanning Probe Microscopy: Towards Fully Automated Microscopy
por: Liu, Yu, et al.
Publicado: (2024)

GeoMFormer: A General Architecture for Geometric Molecular Representation Learning
por: Chen, Tianlang, et al.
Publicado: (2024)

Supervised Pretraining for Material Property Prediction
por: Rahman, Chowdhury Mohammad Abid, et al.
Publicado: (2025)

LLM Meets Diffusion: A Hybrid Framework for Crystal Material Generation
por: Khastagir, Subhojyoti, et al.
Publicado: (2025)

EGMOF: Efficient Generation of Metal-Organic Frameworks Using a Hybrid Diffusion-Transformer Architecture
por: Han, Seunghee, et al.
Publicado: (2025)

WyckoffDiff -- A Generative Diffusion Model for Crystal Symmetry
por: Kelvinius, Filip Ekström, et al.
Publicado: (2025)

CAST: Cross Attention based multimodal fusion of Structure and Text for materials property prediction
por: Lee, Jaewan, et al.
Publicado: (2025)

Tensor Completion for Surrogate Modeling of Material Property Prediction
por: Pakala, Shaan, et al.
Publicado: (2025)

LLM-Fusion: A Novel Multimodal Fusion Model for Accelerated Material Discovery
por: Boyar, Onur, et al.
Publicado: (2025)

Autonomous Multi-objective Alloy Design through Simulation-guided Optimization
por: Yang, Penghui, et al.
Publicado: (2025)

MatterTune: An Integrated, User-Friendly Platform for Fine-Tuning Atomistic Foundation Models to Accelerate Materials Simulation and Discovery
por: Kong, Lingyu, et al.
Publicado: (2025)

MOFGPT: Generative Design of Metal-Organic Frameworks using Language Models
por: Badrinarayanan, Srivathsan, et al.
Publicado: (2025)

Global Stress Generation and Spatiotemporal Super-Resolution Physics-Informed Operator under Dynamic Loading for Two-Phase Random Materials
por: Xing, Tengfei, et al.
Publicado: (2025)

Enhancing Stress-Strain Predictions with Seq2Seq and Cross-Attention based on Small Punch Test
por: Yang, Zhengni, et al.
Publicado: (2025)

PriM: Principle-Inspired Material Discovery through Multi-Agent Collaboration
por: Lai, Zheyuan, et al.
Publicado: (2025)

Predicting Stress in Two-phase Random Materials and Super-Resolution Method for Stress Images by Embedding Physical Information
por: Xing, Tengfei, et al.
Publicado: (2025)

XDXD: End-to-end crystal structure determination with low resolution X-ray diffraction
por: Zhao, Jiale, et al.
Publicado: (2025)

DiffSyn: A Generative Diffusion Approach to Materials Synthesis Planning
por: Pan, Elton, et al.
Publicado: (2025)