Cover Image

Beyond the Black Box: An Interpretable Machine Learning Framework for Predicting Electronic Structure Microdescriptors and Structure-Performance Relationships in Fe-based Catalytic Systems

Fuente: arXiv

Saved in:

Bibliographic Details
Main Author:	Romiluyi, Oyinkansola
Format:	Preprint
Published:	2026
Subjects:	Chemical Physics Materials Science Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

TraceGrad: a Framework Learning Expressive SO(3)-equivariant Non-linear Representations for Electronic-Structure Hamiltonian Prediction
by: Yin, Shi, et al.
Published: (2024)

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

Geometric Deep Learning for Molecular Crystal Structure Prediction
by: Kilgour, Michael, et al.
Published: (2023)

XANE(3): An E(3)-Equivariant Graph Neural Network for Accurate Prediction of XANES Spectra from Atomic Structures
by: Grizzi, Vitor F., et al.
Published: (2026)

Deep Learning for GWP Prediction: A Framework Using PCA, Quantile Transformation, and Ensemble Modeling
by: Rajapriya, Navin, et al.
Published: (2024)

Active Learning for Generalizable Detonation Performance Prediction of Energetic Materials
by: Ullberg, R. Seaton, et al.
Published: (2026)

Manifold Diffusion for Structure Generation of Transition Metal Complexes
by: Schaufelberger, Luca, et al.
Published: (2026)

Equivariant Machine Learning Interatomic Potentials with Global Charge Redistribution
by: Maruf, Moin Uddin, et al.
Published: (2025)

A Cartesian Encoding Graph Neural Network for Crystal Structures Property Prediction: Application to Thermal Ellipsoid Estimation
by: Solé, Àlex, et al.
Published: (2025)

Many-body Expansion Based Machine Learning Models for Octahedral Transition Metal Complexes
by: Meyer, Ralf, et al.
Published: (2024)

Machine Learning Multiscale Interactions
by: Solé, Àlex, et al.
Published: (2026)

Cutting Through the Noise: On-the-fly Outlier Detection for Robust Training of Machine Learning Interatomic Potentials
by: Lam, Terry C. W., et al.
Published: (2026)

Transformers Discover Molecular Structure Without Graph Priors
by: Kreiman, Tobias, et al.
Published: (2025)

General-Purpose Models for the Chemical Sciences: LLMs and Beyond
by: Alampara, Nawaf, et al.
Published: (2025)

Leveraging Data Mining, Active Learning, and Domain Adaptation in a Multi-Stage, Machine Learning-Driven Approach for the Efficient Discovery of Advanced Acidic Oxygen Evolution Electrocatalysts
by: Ding, Rui, et al.
Published: (2024)

Learning simple heuristic rules for classifying materials based on chemical composition
by: Ma, Andrew, et al.
Published: (2025)

Machine learning frontier orbital energies of nanodiamonds
by: Kirschbaum, Thorren, et al.
Published: (2022)

Understanding and Mitigating Distribution Shifts For Machine Learning Force Fields
by: Kreiman, Tobias, et al.
Published: (2025)

Machine learning Hubbard parameters with equivariant neural networks
by: Uhrin, Martin, et al.
Published: (2024)

Identifying the Catalytic Descriptor of Single-Atom Catalysts in Nitrate Reduction Reaction: An Interpretable Machine-Learning Method
by: Zhu, Zhen, et al.
Published: (2025)

Hybrid Quantum--Classical Machine Learning Potential with Variational Quantum Circuits
by: Willow, Soohaeng Yoo, et al.
Published: (2025)

Orbital Transformers for Predicting Wavefunctions in Time-Dependent Density Functional Theory
by: Zhang, Xuan, et al.
Published: (2026)

Vibrational Fingerprints of Strained Polymers: A Spectroscopic Pathway to Mechanical State Prediction
by: Konrad, Julian, et al.
Published: (2025)

Accelerating Complex Materials Discovery with Universal Machine-Learning Potential-Driven Structure Prediction
by: An, Yuqi, et al.
Published: (2026)

Towards Fast, Specialized Machine Learning Force Fields: Distilling Foundation Models via Energy Hessians
by: Amin, Ishan, et al.
Published: (2025)

Building-Block Aware Generative Modeling for 3D Crystals of Metal Organic Frameworks
by: Duan, Chenru, et al.
Published: (2025)

Predicting band gap from chemical composition: A simple learned model for a material property with atypical statistics
by: Ma, Andrew, et al.
Published: (2025)

Transferable Learning of Reaction Pathways from Geometric Priors
by: Nam, Juno, et al.
Published: (2025)

Active Learning Enables Extrapolation in Molecular Generative Models
by: Antoniuk, Evan R., et al.
Published: (2025)

Learning the action for long-time-step simulations of molecular dynamics
by: Bigi, Filippo, et al.
Published: (2025)

Kernel Learning Assisted Synthesis Condition Exploration for Ternary Spinel
by: Liu, Yutong, et al.
Published: (2025)

Learning from the electronic structure of molecules across the periodic table
by: Kaniselvan, Manasa, et al.
Published: (2025)

Knowledge-Reuse Transfer Learning Methods in Molecular and Material Science
by: Chen, An, et al.
Published: (2024)

Learning Ordering in Crystalline Materials with Symmetry-Aware Graph Neural Networks
by: Peng, Jiayu, et al.
Published: (2024)

Understanding Machine Learning Paradigms through the Lens of Statistical Thermodynamics: A tutorial
by: Star, et al.
Published: (2024)

Machine learning interatomic potential can infer electrical response
by: Zhong, Peichen, et al.
Published: (2025)

Self-consistent Validation for Machine Learning Electronic Structure
by: Hu, Gengyuan, et al.
Published: (2024)

Structure to Property: Chemical Element Embeddings and a Deep Learning Approach for Accurate Prediction of Chemical Properties
by: Shermukhamedov, Shokirbek, et al.
Published: (2023)

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

Advancements in Molecular Property Prediction: A Survey of Single and Multimodal Approaches
by: Liyaqat, Tanya, et al.
Published: (2024)