Copertina

MEIDNet: Multimodal generative AI framework for inverse materials design

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Babu, Anand, Gouvêa, Rogério Almeida, Vandergheynst, Pierre, Rignanese, Gian-Marco
Natura:	Preprint
Pubblicazione:	2026
Soggetti:	Materials Science Artificial Intelligence Machine Learning Computational Physics
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials
di: Gouvêa, Rogério Almeida, et al.
Pubblicazione: (2026)

Towards Automated Discovery: A Review of Generative Models, Multimodal Learning and Closed-Loop Workflows in Inverse Materials Design
di: Babu, Anand, et al.
Pubblicazione: (2026)

SciQu: Accelerating Materials Properties Prediction with Automated Literature Mining for Self-Driving Laboratories
di: Babu, Anand
Pubblicazione: (2024)

Combining feature-based approaches with graph neural networks and symbolic regression for synergistic performance and interpretability
di: Gouvêa, Rogério Almeida, et al.
Pubblicazione: (2025)

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

UniGenX: a unified generative foundation model that couples sequence, structure and function to accelerate scientific design across proteins, molecules and materials
di: Zhang, Gongbo, et al.
Pubblicazione: (2025)

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

LEAP: A closed-loop framework for perovskite precursor additive discovery
di: Wang, Xin-De, et al.
Pubblicazione: (2026)

Pushing the Pareto front of band gap and permittivity: ML-guided search for dielectric materials
di: Riebesell, Janosh, et al.
Pubblicazione: (2024)

Spiers Memorial Lecture: How to do impactful research in artificial intelligence for chemistry and materials science
di: Cheng, Austin, et al.
Pubblicazione: (2024)

Perovskite-R1: a domain-specialized large language model for intelligent discovery of precursor additives and experimental design
di: Wang, Xin-De, et al.
Pubblicazione: (2025)

AI-driven inverse design of materials: Past, present and future
di: Han, Xiao-Qi, et al.
Pubblicazione: (2024)

InvDesFlow: An AI-driven materials inverse design workflow to explore possible high-temperature superconductors
di: Han, Xiao-Qi, et al.
Pubblicazione: (2024)

From Symmetry to Stability: Structural and Electronic Transformation in Cs$_2$KInI$_6$
di: Bakhsh, Mohammad, et al.
Pubblicazione: (2026)

Zatom-1: Towards a Multimodal Foundation Model for 3D Molecules and Materials
di: Morehead, Alex, et al.
Pubblicazione: (2026)

A fully differentiable framework for training proxy Exchange Correlation Functionals for periodic systems
di: Singh, Rakshit Kumar, et al.
Pubblicazione: (2026)

Benchmarking large language models for materials synthesis: the case of atomic layer deposition
di: Yanguas-Gil, Angel, et al.
Pubblicazione: (2024)

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

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

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

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

AI Meets Plasticity: A Comprehensive Survey
di: Bakhshan, Hadi, et al.
Pubblicazione: (2026)

AI scientists produce results without reasoning scientifically
di: Ríos-García, Martiño, et al.
Pubblicazione: (2026)

AI-ready design of realistic 2D materials and interfaces with Mat3ra-2D
di: Biryukov, Vsevolod, et al.
Pubblicazione: (2026)

MatterGen: a generative model for inorganic materials design
di: Zeni, Claudio, et al.
Pubblicazione: (2023)

Overcoming systematic softening in universal machine learning interatomic potentials by fine-tuning
di: Deng, Bowen, et al.
Pubblicazione: (2024)

Learning Kinetic Monte Carlo stochastic dynamics with Deep Generative Adversarial Networks
di: Lanzoni, Daniele, et al.
Pubblicazione: (2025)

Performance of AI agents based on reasoning language models on ALD process optimization tasks
di: Yanguas-Gil, Angel
Pubblicazione: (2026)

A generative material transformer using Wyckoff representation
di: De Breuck, Pierre-Paul, et al.
Pubblicazione: (2025)

Autonomous Materials Exploration by Integrating Automated Phase Identification and AI-Assisted Human Reasoning
di: Chang, Ming-Chiang, et al.
Pubblicazione: (2026)

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

Physics-Informed Graph Neural Networks to Reconstruct Local Fields Considering Finite Strain Hyperelasticity
di: Garban, Manuel Ricardo Guevara, et al.
Pubblicazione: (2025)

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

Enhancing the Scalability and Applicability of Kohn-Sham Hamiltonians for Molecular Systems
di: Li, Yunyang, et al.
Pubblicazione: (2025)

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

COFAP: A Universal Framework for COFs Adsorption Prediction through Designed Multi-Modal Extraction and Cross-Modal Synergy
di: Li, Zihan, et al.
Pubblicazione: (2025)

Evaluating Large Language Models in Scientific Discovery
di: Song, Zhangde, et al.
Pubblicazione: (2025)

Are large language models superhuman chemists?
di: Mirza, Adrian, et al.
Pubblicazione: (2024)

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

Accelerating Scientific Discovery with Autonomous Goal-evolving Agents
di: Du, Yuanqi, et al.
Pubblicazione: (2025)