:: Library Catalog

Copertina

Salvato in:

Dettagli Bibliografici
Autori principali:	Pei, Zongrui, Yin, Junqi, Zhang, Jiaxin
Natura:	Preprint
Pubblicazione:	2025
Soggetti:	Materials Science
Accesso online:	https://arxiv.org/abs/2504.14849
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Sustainable Materials Discovery in the Era of Artificial Intelligence
di: Mannan, Sajid, et al.
Pubblicazione: (2026)

Towards Computational Microscope of Chemical Order-Disorder via ML-Accelerated Monte Carlo Simulation
di: Zhou, Fanli, et al.
Pubblicazione: (2026)

LLMatDesign: Autonomous Materials Discovery with Large Language Models
di: Jia, Shuyi, et al.
Pubblicazione: (2024)

Challenges and Opportunities in Searching for Rashba-Dresselhaus Materials for Efficient Spin-Charge Interconversion at Room Temperature
di: Wang, Zixu, et al.
Pubblicazione: (2024)

Perovskite Nanocrystals as Emerging Single-Photon Emitters: Progress, Challenges, and Opportunities
di: Ryu, Jehyeok, et al.
Pubblicazione: (2025)

From Natural Language to Materials Discovery:The Materials Knowledge Navigation Agent
di: Zhuang, Genmao, et al.
Pubblicazione: (2026)

Large Language Models for Superconductor Discovery
di: Itani, Suman, et al.
Pubblicazione: (2025)

Enhancing Magnetocaloric Material Discovery: A Machine Learning Approach Using an Autogenerated Database by Large Language Models
di: Yuan, Jiaoyue, et al.
Pubblicazione: (2024)

Elemental Reactivity Maps for Materials Discovery
di: Inada, Yuki, et al.
Pubblicazione: (2024)

A Chemically Grounded Evaluation Framework for Generative Models in Materials Discovery
di: Veillon, Elohan, et al.
Pubblicazione: (2025)

Generative Chemical Language Models for Energetic Materials Discovery
di: Salij, Andrew, et al.
Pubblicazione: (2026)

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

Illustrating an Effective Workflow for Accelerated Materials Discovery
di: Mulukutla, Mrinalini, et al.
Pubblicazione: (2024)

AI-Driven Accelerated Discovery of Intercalation-type Cathode Materials for Magnesium Batteries
di: Chen, Wenjie, et al.
Pubblicazione: (2024)

TXL Fusion: A Hybrid Machine Learning Framework Integrating Chemical Heuristics and Large Language Models for Topological Materials Discovery
di: Ullah, Arif, et al.
Pubblicazione: (2025)

Bgolearn: a Unified Bayesian Optimization Framework for Accelerating Materials Discovery
di: Cao, Bin, et al.
Pubblicazione: (2026)

Enhancing Materials Discovery with Valence Constrained Design in Generative Modeling
di: Cheng, Mouyang, et al.
Pubblicazione: (2025)

A Geometry-Adaptive Deep Variational Framework for Phase Discovery in the Landau-Brazovskii Model
di: Xie, Yuchen, et al.
Pubblicazione: (2026)

Hierarchical Bayesian approach for adaptive integration of Bragg peaks in time-of-flight neutron scattering data
di: Reshniak, Viktor, et al.
Pubblicazione: (2024)

"DIVE" into Hydrogen Storage Materials Discovery with AI Agents
di: Zhang, Di, et al.
Pubblicazione: (2025)

A Synthesizability-Guided Pipeline for Materials Discovery
di: Prein, Thorben, et al.
Pubblicazione: (2025)

Role of Large Language Models and Retrieval-Augmented Generation for Accelerating Crystalline Material Discovery: A Systematic Review
di: Oche, Agada Joseph, et al.
Pubblicazione: (2025)

Structural Constraint Integration in Generative Model for Discovery of Quantum Material Candidates
di: Okabe, Ryotaro, et al.
Pubblicazione: (2024)

Discovery of 2D Materials via Symmetry-Constrained Diffusion Model
di: Xu, Shihang, et al.
Pubblicazione: (2024)

A Physics-Informed Chemical Rule for Topological Materials Discovery
di: Xu, Xinyu, et al.
Pubblicazione: (2026)

Agentic AI and Machine Learning for Accelerated Materials Discovery and Applications
di: Chen, Jihua, et al.
Pubblicazione: (2026)

Symmetry Relation Database and Its Application to Ferroelectric Materials Discovery
di: Zhu, Qiang, et al.
Pubblicazione: (2022)

Causal Discovery from Data Assisted by Large Language Models
di: Barakati, Kamyar, et al.
Pubblicazione: (2025)

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

Crystallographic Challenges in Microscopy of Multidomain Spinel Materials
di: Scherz, Ninon, et al.
Pubblicazione: (2026)

Decoratypes: An Extensible Crystal Taxonomy for Machine Learning-Guided Materials Discovery
di: Miller, Kyle D., et al.
Pubblicazione: (2025)

Artificial Intelligence and Generative Models for Materials Discovery -- A Review
di: Handoko, Albertus Denny, et al.
Pubblicazione: (2025)

Large Language Model-Driven Database for Thermoelectric Materials
di: Itani, Suman, et al.
Pubblicazione: (2024)

Reconstructing Materials Tetrahedron: Challenges in Materials Information Extraction
di: Hira, Kausik, et al.
Pubblicazione: (2023)

Progress and Prospects in Two-Dimensional Magnetism of van der Waals Materials
di: Ahn, Youngjun, et al.
Pubblicazione: (2024)

Revealing Nanostructures in High-Entropy Alloys via Machine-Learning Accelerated Scalable Monte Carlo Simulation
di: Liu, Xianglin, et al.
Pubblicazione: (2025)

A Universal Spin-Orbit-Coupled Hamiltonian Model for Accelerated Quantum Material Discovery
di: Zhong, Yang, et al.
Pubblicazione: (2025)

Materials Discovery With Quantum-Enhanced Machine Learning Algorithms
di: Graña, Ignacio F., et al.
Pubblicazione: (2025)

MADE: Benchmark Environments for Closed-Loop Materials Discovery
di: Malik, Shreshth A, et al.
Pubblicazione: (2026)

AI-accelerated Discovery of Altermagnetic Materials
di: Gao, Ze-Feng, et al.
Pubblicazione: (2023)