Cover Image

AI-driven materials design: a mini-review

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Cheng, Mouyang, Fu, Chu-Liang, Okabe, Ryotaro, Chotrattanapituk, Abhijatmedhi, Boonkird, Artittaya, Hung, Nguyen Tuan, Li, Mingda
Format:	Preprint
Published:	2025
Subjects:	Materials Science Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Ensemble-Embedding Graph Neural Network for Direct Prediction of Optical Spectra from Crystal Structure
by: Hung, Nguyen Tuan, et al.
Published: (2024)

Large Language Model-Guided Prediction Toward Quantum Materials Synthesis
by: Okabe, Ryotaro, et al.
Published: (2024)

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

Universal Ensemble‐Embedding Graph Neural Network for Direct Prediction of Optical Spectra from Crystal Structures
by: Nguyen Tuan Hung, et al.
Published: (2024)

Universal Ensemble‐Embedding Graph Neural Network for Direct Prediction of Optical Spectra from Crystal Structures (Adv. Mater. 46/2024)
by: Nguyen Tuan Hung, et al.
Published: (2024)

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

Universal Magnetic Structure Prediction from Atomic Coordinates with Near-Experimental Accuracy
by: Chotrattanapituk, Abhijatmedhi, et al.
Published: (2026)

AI-Driven Defect Engineering for Advanced Thermoelectric Materials
by: Fu, Chu-Liang, et al.
Published: (2025)

Anomalous Neutron Nuclear-Magnetic Interference Spectroscopy
by: Fu, Chuliang, et al.
Published: (2024)

Tuning chiral anomaly signature in a Dirac semimetal via fast-ion implantation
by: Mandal, Manasi, et al.
Published: (2025)

AI‐Driven Defect Engineering for Advanced Thermoelectric Materials
by: Chu‐Liang Fu, et al.
Published: (2025)

Reinforcement learning-guided optimization of critical current in high-temperature superconductors
by: Cheng, Mouyang, et al.
Published: (2025)

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

Probing Non-Equilibrium Grain Boundary Dynamics with XPCS and Domain-Adaptive Machine Learning
by: Cheng, Mouyang, et al.
Published: (2026)

Quantum Theory of X-ray Photon Correlation Spectroscopy
by: Siriviboon, Phum, et al.
Published: (2024)

Machine learning for structure-guided materials and process design
by: Morand, Lukas, et al.
Published: (2023)

AI-guided inverse design and discovery of recyclable vitrimeric polymers
by: Zheng, Yiwen, et al.
Published: (2023)

Machine learning driven search of hydrogen storage materials
by: Banerjee, Tanumoy, et al.
Published: (2025)

Quantum Theory of Functionally Graded Materials
by: Landry, Michael J., et al.
Published: (2026)

Rapid Machine Learning-Driven Detection of Pesticides and Dyes Using Raman Spectroscopy
by: Binh, Quach Thi Thai, et al.
Published: (2025)

MEIDNet: Multimodal generative AI framework for inverse materials design
by: Babu, Anand, et al.
Published: (2026)

Assessing data-driven predictions of band gap and electrical conductivity for transparent conducting materials
by: Ottomano, Federico, et al.
Published: (2024)

Beyond designer's knowledge: Generating materials design hypotheses via large language models
by: Liu, Quanliang, et al.
Published: (2024)

A chemical language model for reticular materials design
by: Menon, Dhruv, et al.
Published: (2026)

Spatiotemporal Mapping of Anisotropic Thermal Transport in GaN Thin Films via Ultrafast X-ray Diffraction
by: Nguyen, Thanh, et al.
Published: (2025)

Universal crystal material property prediction via multi-view geometric fusion in graph transformers
by: Zhang, Liang, et al.
Published: (2025)

Structure-based out-of-distribution (OOD) materials property prediction: a benchmark study
by: Omee, Sadman Sadeed, et al.
Published: (2024)

Response Matching for generating materials and molecules
by: Cheng, Bingqing
Published: (2024)

Strain effect on optical properties and quantum weight of monolayer MnBi$_2$X$_4$ (X = Te, Se, S)
by: Hung, Nguyen Tuan, et al.
Published: (2025)

Real-time experiment-theory closed-loop interaction for autonomous materials science
by: Liang, Haotong, et al.
Published: (2024)

A physics-aware deep learning model for shear band formation around collapsing pores in shocked reactive materials
by: Cheng, Xinlun, et al.
Published: (2025)

Machine-learned models for magnetic materials
by: Leszczyński, Paweł, et al.
Published: (2023)

InvDesFlow-AL: active learning-based workflow for inverse design of functional materials
by: Han, Xiao-Qi, et al.
Published: (2025)

Broken neural scaling laws in materials science
by: Großmann, Max, et al.
Published: (2026)

Symbolic regression for defect interactions in 2D materials
by: Lazarev, Mikhail, et al.
Published: (2025)

Physics-based reward driven image analysis in microscopy
by: Barakati, Kamyar, et al.
Published: (2024)

Probing the limitations of multimodal language models for chemistry and materials research
by: Alampara, Nawaf, et al.
Published: (2024)

Inverse designing metamaterials with programmable nonlinear functional responses in graph space
by: Maurizi, Marco, et al.
Published: (2024)

Machine learning assisted screening of metal binary alloys for anode materials
by: Shi, Xingyue, et al.
Published: (2024)

Accurate, transferable, and verifiable machine-learned interatomic potentials for layered materials
by: Georgaras, Johnathan D., et al.
Published: (2025)