Copertina

CLOUD: A Scalable and Physics-Informed Foundation Model for Crystal Representation Learning

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Xu, Changwen, Zhu, Shang, Viswanathan, Venkatasubramanian
Natura:	Preprint
Pubblicazione:	2025
Soggetti:	Materials Science Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

DREAMS: Density Functional Theory Based Research Engine for Agentic Materials Simulation
di: Wang, Ziqi, et al.
Pubblicazione: (2025)

Active Learning for Conditional Inverse Design with Crystal Generation and Foundation Atomic Models
di: Li, Zhuoyuan, et al.
Pubblicazione: (2025)

Training Variation of Physically-Informed Deep Learning Models
di: Lenau, Ashley, et al.
Pubblicazione: (2025)

Universally Converging Representations of Matter Across Scientific Foundation Models
di: Edamadaka, Sathya, et al.
Pubblicazione: (2025)

A Foundation Model for Material Fracture Prediction
di: Marcato, Agnese, et al.
Pubblicazione: (2025)

Foundation Models for Discovery and Exploration in Chemical Space
di: Wadell, Alexius, et al.
Pubblicazione: (2025)

CrysAtom: Distributed Representation of Atoms for Crystal Property Prediction
di: Mukherjee, Shrimon, et al.
Pubblicazione: (2024)

Tokenization for Molecular Foundation Models
di: Wadell, Alexius, et al.
Pubblicazione: (2024)

CrystalICL: Enabling In-Context Learning for Crystal Generation
di: Wang, Ruobing, et al.
Pubblicazione: (2025)

Fundamental Limits of Dissociative Electrochemical Ammonia Synthesis via Electrodeposited Metals
di: Azumah, Victor, et al.
Pubblicazione: (2025)

SymmCD: Symmetry-Preserving Crystal Generation with Diffusion Models
di: Levy, Daniel, et al.
Pubblicazione: (2025)

Physics-Informed Gaussian Process Regression for the Constitutive Modeling of Concrete: A Data-Driven Improvement to Phenomenological Models
di: Li, Chenyang, et al.
Pubblicazione: (2026)

Learning ORDER-Aware Multimodal Representations for Composite Materials Design
di: Li, Xinyao, et al.
Pubblicazione: (2026)

Foundation-Model Surrogates Enable Data-Efficient Active Learning for Materials Discovery
di: Hu, Jeffrey, et al.
Pubblicazione: (2026)

Physics-Informed Neural Networks for Electrical Circuit Analysis: Applications in Dielectric Material Modeling
di: Taj, Reyhaneh
Pubblicazione: (2024)

On-Demand Growth of Semiconductor Heterostructures Guided by Physics-Informed Machine Learning
di: Shen, Chao, et al.
Pubblicazione: (2024)

Physics-Informed Machine Learning for Steel Development: A Computational Framework and CCT Diagram Modelling
di: Hedström, Peter, et al.
Pubblicazione: (2025)

Unifying Polymer Modeling and Design via a Conformation-Centric Generative Foundation Model
di: Wang, Fanmeng, et al.
Pubblicazione: (2025)

MatLLMSearch: Crystal Structure Discovery with Evolution-Guided Large Language Models
di: Gan, Jingru, et al.
Pubblicazione: (2025)

Why Physics Still Matters: Improving Machine Learning Prediction of Material Properties with Phonon-Informed Datasets
di: Benítez, Pol, et al.
Pubblicazione: (2025)

Generalizable Prediction Model of Molten Salt Mixture Density with Chemistry-Informed Transfer Learning
di: Barra, Julian, et al.
Pubblicazione: (2024)

Physics-Informed Gaussian Process Classification for Constraint-Aware Alloy Design
di: Hardcastle, Christofer, et al.
Pubblicazione: (2025)

Multimodal Foundation Models for Material Property Prediction and Discovery
di: Moro, Viggo, et al.
Pubblicazione: (2023)

LeMat-Traj: A Scalable and Unified Dataset of Materials Trajectories for Atomistic Modeling
di: Ramlaoui, Ali, et al.
Pubblicazione: (2025)

Bridging Text and Crystal Structures: Literature-driven Contrastive Learning for Materials Science
di: Suzuki, Yuta, et al.
Pubblicazione: (2025)

A Foundation Model for Non-Destructive Defect Identification from Vibrational Spectra
di: Cheng, Mouyang, et al.
Pubblicazione: (2025)

OXtal: An All-Atom Diffusion Model for Organic Crystal Structure Prediction
di: Jin, Emily, et al.
Pubblicazione: (2025)

ContinuouSP: Generative Model for Crystal Structure Prediction with Invariance and Continuity
di: Tone, Yuji, et al.
Pubblicazione: (2025)

Generative Inverse Design of Crystal Structures via Diffusion Models with Transformers
di: Takahara, Izumi, et al.
Pubblicazione: (2024)

MolCrystalFlow: Molecular Crystal Structure Prediction via Flow Matching
di: Zeng, Cheng, et al.
Pubblicazione: (2026)

Generative Design of Crystal Structures by Point Cloud Representations and Diffusion Model
di: Li, Zhelin, et al.
Pubblicazione: (2024)

OrgFlow: Generative Modeling of Organic Crystal Structures from Molecular Graphs
di: Vahediahmar, Mohammadmahdi, et al.
Pubblicazione: (2026)

Materials Learning Algorithms (MALA): Scalable Machine Learning for Electronic Structure Calculations in Large-Scale Atomistic Simulations
di: Cangi, Attila, et al.
Pubblicazione: (2024)

Identifying Constitutive Parameters for Complex Hyperelastic Materials using Physics-Informed Neural Networks
di: Song, Siyuan, et al.
Pubblicazione: (2023)

PolyMicros: Bootstrapping a Foundation Model for Polycrystalline Material Structure
di: Buzzy, Michael, et al.
Pubblicazione: (2025)

CrystalREPA: Transferring Physical Priors from Universal MLIPs to Crystal Generative Models
di: Zhang, Chengqian, et al.
Pubblicazione: (2026)

Orb: A Fast, Scalable Neural Network Potential
di: Neumann, Mark, et al.
Pubblicazione: (2024)

Crystal Systems Classification of Phosphate-Based Cathode Materials Using Machine Learning for Lithium-Ion Battery
di: Yadav, Yogesh, et al.
Pubblicazione: (2025)

Machine Learning-Driven Crystal System Prediction for Perovskites Using Augmented X-ray Diffraction Data
di: Mathew, Ansu, et al.
Pubblicazione: (2026)

Equivariant Diffusion for Crystal Structure Prediction
di: Lin, Peijia, et al.
Pubblicazione: (2025)