Copertina

Machine Learning - Driven Materials Discovery: Unlocking Next-Generation Functional Materials - A review

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Nematov, Dilshod, Hojamberdiev, Mirabbos
Natura:	Preprint
Pubblicazione:	2025
Soggetti:	Materials Science Artificial Intelligence Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Analysis of the Optical Properties and Electronic Structure of Semiconductors of the Cu2NiXS4 (X = Si, Ge, Sn) Family as New Promising Materials for Optoelectronic Devices
di: Nematov, Dilshod
Pubblicazione: (2026)

A Review on Improving PSC Performance through Charge Carrier Management: Where We Stand and What's Next?
di: Nematov, Dilshod
Pubblicazione: (2025)

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

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

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

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

Are LLMs Ready for Real-World Materials Discovery?
di: Miret, Santiago, et al.
Pubblicazione: (2024)

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

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

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

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

Vector Field Oriented Diffusion Model for Crystal Material Generation
di: Klipfel, Astrid, et al.
Pubblicazione: (2023)

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

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

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

Hierarchical Multi-agent Large Language Model Reasoning for Autonomous Functional Materials Discovery
di: Rothfarb, Samuel, et al.
Pubblicazione: (2025)

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

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

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

FlowLLM: Flow Matching for Material Generation with Large Language Models as Base Distributions
di: Sriram, Anuroop, et al.
Pubblicazione: (2024)

LLEMA: Evolutionary Search with LLMs for Multi-Objective Materials Discovery
di: Abhyankar, Nikhil, et al.
Pubblicazione: (2025)

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

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

FlowMM: Generating Materials with Riemannian Flow Matching
di: Miller, Benjamin Kurt, et al.
Pubblicazione: (2024)

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

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

Tuning Optoelectronic Properties and Photoelectrochemical Performance of \b{eta}-TaON via Vanadium Doping
di: Hojamberdiev, Mirabbos, et al.
Pubblicazione: (2026)

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

A Materials Map Integrating Experimental and Computational Data via Graph-Based Machine Learning for Enhanced Materials Discovery
di: Hashimoto, Yusuke, et al.
Pubblicazione: (2025)

Predicting Grain Growth in Polycrystalline Materials Using Deep Learning Time Series Models
di: Younes, Eliane, et al.
Pubblicazione: (2025)

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

Evaluating the Performance and Robustness of LLMs in Materials Science Q&A and Property Predictions
di: Wang, Hongchen, et al.
Pubblicazione: (2024)

PolySet: Restoring the Statistical Ensemble Nature of Polymers for Machine Learning
di: Ferji, Khalid
Pubblicazione: (2025)

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

Automated Extraction of Material Properties using LLM-based AI Agents
di: Ghosh, Subham, et al.
Pubblicazione: (2025)

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

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

Bridging Theory and Experiment in Materials Discovery: Machine-Learning-Assisted Prediction of Synthesizable Structures
di: Xin, Yu, et al.
Pubblicazione: (2025)

Advancing Universal Deep Learning for Electronic-Structure Hamiltonian Prediction of Materials
di: Yin, Shi, et al.
Pubblicazione: (2025)

Measurements with Noise: Bayesian Optimization for Co-optimizing Noise and Property Discovery in Automated Experiments
di: Slautin, Boris N., et al.
Pubblicazione: (2024)