:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Kneiding, Hannes, Morán-González, Lucía, Kuriakose, Nishamol, Nova, Ainara, Balcells, David
Format:	Preprint
Published:	2026
Subjects:	Chemical Physics Materials Science Machine Learning
Online Access:	https://arxiv.org/abs/2604.11827
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Adaptive Pruning for Increased Robustness and Reduced Computational Overhead in Gaussian Process Accelerated Saddle Point Searches
by: Goswami, Rohit, et al.
Published: (2025)

An Explainable AI Model for Binary LJ Fluids
by: Hashmi, Israrul H, et al.
Published: (2025)

Artificial Intelligence Driven Workflow for Accelerating Design of Novel Photosensitizers
by: Wang, Hongyi, et al.
Published: (2025)

QT-Net: Rethinking Evaluation of AI Models in Atomic Chemical Space
by: Crespo, Pablo Martínez, et al.
Published: (2026)

Enhanced Climbing Image Nudged Elastic Band method with Hessian Eigenmode Alignment
by: Goswami, Rohit, et al.
Published: (2026)

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

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

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

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

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

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

The Open DAC 2025 Dataset for Sorbent Discovery in Direct Air Capture
by: Sriram, Anuroop, et al.
Published: (2025)

Reflections from the 2024 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry
by: Zimmermann, Yoel, et al.
Published: (2024)

CrystalREPA: Transferring Physical Priors from Universal MLIPs to Crystal Generative Models
by: Zhang, Chengqian, et al.
Published: (2026)

Action-Minimization Meets Generative Modeling: Efficient Transition Path Sampling with the Onsager-Machlup Functional
by: Raja, Sanjeev, et al.
Published: (2025)

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)

Reducing cross-sample prediction churn in scientific machine learning
by: Prastalo, Gordan, et al.
Published: (2026)

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

Better without U: Impact of Selective Hubbard U Correction on Foundational MLIPs
by: Warford, Thomas, et al.
Published: (2026)

Clever Materials: When Models Identify Good Materials for the Wrong Reasons
by: Jablonka, Kevin Maik
Published: (2026)

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

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

Model-free quantification of completeness, uncertainties, and outliers in atomistic machine learning using information theory
by: Schwalbe-Koda, Daniel, et al.
Published: (2024)

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

Comparing the latent features of universal machine-learning interatomic potentials
by: Chorna, Sofiia, et al.
Published: (2025)

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

Adapting OC20-trained EquiformerV2 Models for High-Entropy Materials
by: Clausen, Christian M., et al.
Published: (2024)

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

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

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)

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

Constructing accurate machine-learned potentials and performing highly efficient atomistic simulations to predict structural and thermal properties
by: Liu, Junlan, et al.
Published: (2024)

Interpolation and differentiation of alchemical degrees of freedom in machine learning interatomic potentials
by: Nam, Juno, et al.
Published: (2024)

Foundation Models for Discovery and Exploration in Chemical Space
by: Wadell, Alexius, et al.
Published: (2025)

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

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

Discovery of 2D Materials via Symmetry-Constrained Diffusion Model
by: Xu, Shihang, et al.
Published: (2024)

Multimodal machine learning with large language embedding model for polymer property prediction
by: Zhang, Tianren, et al.
Published: (2025)

PET-MAD, a lightweight universal interatomic potential for advanced materials modeling
by: Mazitov, Arslan, et al.
Published: (2025)