Image de couverture de livre

The Infinite-Dimensional Nature of Spectroscopy and Why Models Succeed, Fail, and Mislead

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Michelucci, Umberto, Venturini, Francesca
Format:	Preprint
Publié:	2026
Sujets:	Machine Learning Materials Science Artificial Intelligence
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Deep Learning Domain Adaptation to Understand Physico-Chemical Processes from Fluorescence Spectroscopy Small Datasets: Application to Ageing of Olive Oil
par: Michelucci, Umberto, et autres
Publié: (2024)

New Statistical Framework for Extreme Error Probability in High-Stakes Domains for Reliable Machine Learning
par: Michelucci, Umberto, et autres
Publié: (2025)

Best Practices for Machine Learning Experimentation in Scientific Applications
par: Michelucci, Umberto, et autres
Publié: (2025)

Understanding Adversarial Transfer: Why Representation-Space Attacks Fail Where Data-Space Attacks Succeed
par: Gupta, Isha, et autres
Publié: (2025)

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

Tensor Completion for Surrogate Modeling of Material Property Prediction
par: Pakala, Shaan, et autres
Publié: (2025)

WyckoffDiff -- A Generative Diffusion Model for Crystal Symmetry
par: Kelvinius, Filip Ekström, et autres
Publié: (2025)

Vector Field Oriented Diffusion Model for Crystal Material Generation
par: Klipfel, Astrid, et autres
Publié: (2023)

MOFGPT: Generative Design of Metal-Organic Frameworks using Language Models
par: Badrinarayanan, Srivathsan, et autres
Publié: (2025)

LLM-Fusion: A Novel Multimodal Fusion Model for Accelerated Material Discovery
par: Boyar, Onur, et autres
Publié: (2025)

BOOM: Benchmarking Out-Of-distribution Molecular Property Predictions of Machine Learning Models
par: Antoniuk, Evan R., et autres
Publié: (2025)

Zatom-1: Towards a Multimodal Foundation Model for 3D Molecules and Materials
par: Morehead, Alex, et autres
Publié: (2026)

FlowLLM: Flow Matching for Material Generation with Large Language Models as Base Distributions
par: Sriram, Anuroop, et autres
Publié: (2024)

MatterTune: An Integrated, User-Friendly Platform for Fine-Tuning Atomistic Foundation Models to Accelerate Materials Simulation and Discovery
par: Kong, Lingyu, et autres
Publié: (2025)

SpectraFormer: an Attention-Based Raman Unmixing Tool for Accessing the Graphene Buffer-Layer Signature on SiC
par: Poteryayev, Dmitriy, et autres
Publié: (2026)

Can LLMs Predict Polymer Physics Just by Reading Synthesis and Processing Prose?
par: Liu, Yuchu, et autres
Publié: (2026)

AI Meets Plasticity: A Comprehensive Survey
par: Bakhshan, Hadi, et autres
Publié: (2026)

Constrained Diffusion for Accelerated Structure Relaxation of Inorganic Solids with Point Defects
par: Cui, Jingyi, et autres
Publié: (2026)

What drives performance in molecular MPNNs? An operator-level factorial benchmark
par: Jiao, Panyu, et autres
Publié: (2026)

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

AI scientists produce results without reasoning scientifically
par: Ríos-García, Martiño, et autres
Publié: (2026)

Meta-LegNet: A Transferable and Interpretable Framework for Surface Adsorption Prediction via Self-Defined Adsorption-Environment Learning
par: Li, Yifan, et autres
Publié: (2026)

A fully differentiable framework for training proxy Exchange Correlation Functionals for periodic systems
par: Singh, Rakshit Kumar, et autres
Publié: (2026)

Supervised Pretraining for Material Property Prediction
par: Rahman, Chowdhury Mohammad Abid, et autres
Publié: (2025)

OmniXAS: A Universal Deep-Learning Framework for Materials X-ray Absorption Spectra
par: Kharel, Shubha R., et autres
Publié: (2024)

LLM Meets Diffusion: A Hybrid Framework for Crystal Material Generation
par: Khastagir, Subhojyoti, et autres
Publié: (2025)

Benchmarking large language models for materials synthesis: the case of atomic layer deposition
par: Yanguas-Gil, Angel, et autres
Publié: (2024)

Machine Learned Force Fields: Fundamentals, its reach, and challenges
par: Vital, Carlos A., et autres
Publié: (2025)

XxaCT-NN: Structure Agnostic Multimodal Learning for Materials Science
par: Subramanian, Jithendaraa, et autres
Publié: (2025)

Energy & Force Regression on DFT Trajectories is Not Enough for Universal Machine Learning Interatomic Potentials
par: Miret, Santiago, et autres
Publié: (2025)

EGMOF: Efficient Generation of Metal-Organic Frameworks Using a Hybrid Diffusion-Transformer Architecture
par: Han, Seunghee, et autres
Publié: (2025)

CAST: Cross Attention based multimodal fusion of Structure and Text for materials property prediction
par: Lee, Jaewan, et autres
Publié: (2025)

MechProNet: Machine Learning Prediction of Mechanical Properties in Metal Additive Manufacturing
par: Akbari, Parand, et autres
Publié: (2022)

Molecular topological deep learning for polymer property prediction
par: Shen, Cong, et autres
Publié: (2024)

Autonomous Multi-objective Alloy Design through Simulation-guided Optimization
par: Yang, Penghui, et autres
Publié: (2025)

Interaction Topological Transformer for Multiscale Learning in Porous Materials
par: Chen, Dong, et autres
Publié: (2025)

Steering an Active Learning Workflow Towards Novel Materials Discovery via Queue Prioritization
par: Schwarting, Marcus, et autres
Publié: (2025)

Machine Learning - Driven Materials Discovery: Unlocking Next-Generation Functional Materials - A review
par: Nematov, Dilshod, et autres
Publié: (2025)

Beyond designer's knowledge: Generating materials design hypotheses via large language models
par: Liu, Quanliang, et autres
Publié: (2024)

AFSD-Physics: Exploring the governing equations of temperature evolution during additive friction stir deposition by a human-AI teaming approach
par: Shi, Tony, et autres
Publié: (2024)