:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Natarajan, S. Kondati, Schneider, J., Pandey, N., Wellendorff, J., Smidstrup, S.
Format:	Preprint
Published:	2025
Subjects:	Materials Science Machine Learning
Online Access:	https://arxiv.org/abs/2505.01118
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Machine Learning Framework for Characterizing Processing-Structure Relationship in Block Copolymer Thin Films
by: Lamb, Bradley, et al.
Published: (2025)

Unsupervised Atomic Data Mining via Multi-Kernel Graph Autoencoders for Machine Learning Force Fields
by: Sun, Hong, et al.
Published: (2025)

Evaluating Universal Machine Learning Force Fields Against Experimental Measurements
by: Mannan, Sajid, et al.
Published: (2025)

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

Toward Multi-Fidelity Machine Learning Force Field for Cathode Materials
by: Dong, Guangyi, et al.
Published: (2025)

Leveraging Machine Learning Force Fields (MLFFs) to Simulate Large Atomistic Systems for Fidelity Improvement of Superconducting Qubits and Sensors
by: Smidstrup, Søren, et al.
Published: (2025)

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

Materials Learning Algorithms (MALA): Scalable Machine Learning for Electronic Structure Calculations in Large-Scale Atomistic Simulations
by: Cangi, Attila, et al.
Published: (2024)

Diffusion-based Generative Machine Learning Model for Predicting Crack Propagation in Aluminum Nitride at the Atomic Scale
by: Lu, Jiali, et al.
Published: (2026)

Machine-Learned Force Fields for Lattice Dynamics at Coupled-Cluster Level Accuracy
by: Schönbauer, Sita, et al.
Published: (2025)

Cross-Modal Characterization of Thin Film MoS$_2$ Using Generative Models
by: Moses, Isaiah A., et al.
Published: (2025)

Molecular Identification via Molecular Fingerprint extraction from Atomic Force Microscopy images
by: Lastre, Manuel González, et al.
Published: (2024)

Machine-Learned Atomic Cluster Expansion Potentials for Fast and Quantum-Accurate Thermal Simulations of Wurtzite AlN
by: Yang, Guang, et al.
Published: (2023)

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

Active Learning for Conditional Inverse Design with Crystal Generation and Foundation Atomic Models
by: Li, Zhuoyuan, et al.
Published: (2025)

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

LTAU-FF: Loss Trajectory Analysis for Uncertainty in Atomistic Force Fields
by: Vita, Joshua A., et al.
Published: (2024)

Predicting Battery Capacity Fade Using Probabilistic Machine Learning Models With and Without Pre-Trained Priors
by: Kenney, Michael J., et al.
Published: (2024)

Predicting and Accelerating Nanomaterials Synthesis Using Machine Learning Featurization
by: Price, Christopher C., et al.
Published: (2024)

Flow Matching for Accelerated Simulation of Atomic Transport in Crystalline Materials
by: Nam, Juno, et al.
Published: (2024)

Stability-Aware Training of Machine Learning Force Fields with Differentiable Boltzmann Estimators
by: Raja, Sanjeev, et al.
Published: (2024)

Machine Learning for Analyzing Atomic Force Microscopy (AFM) Images Generated from Polymer Blends
by: Paruchuri, Aanish, et al.
Published: (2024)

A Planning-and-Exploring Approach to Extreme-Mechanics Force Fields
by: Shi, Pengjie, et al.
Published: (2023)

Composite Material Design for Optimized Fracture Toughness Using Machine Learning
by: Jahromi, Mohammad Naqizadeh, et al.
Published: (2024)

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

Global Universal Scaling and Ultra-Small Parameterization in Machine Learning Interatomic Potentials with Super-Linearity
by: Hu, Yanxiao, et al.
Published: (2025)

Interpretable Multimodal Machine Learning Analysis of X-ray Absorption Near-Edge Spectra and Pair Distribution Functions
by: Narong, Tanaporn Na, et al.
Published: (2024)

Rapid Machine Learning-Driven Detection of Pesticides and Dyes Using Raman Spectroscopy
by: Binh, Quach Thi Thai, et al.
Published: (2025)

ADAPT: Lightweight, Long-Range Machine Learning Force Fields Without Graphs
by: Dramko, Evan, et al.
Published: (2025)

Reservoir Computing with Magnetic Thin Films
by: Dale, Matthew, et al.
Published: (2021)

Estimation of Electronic Band Gap Energy From Material Properties Using Machine Learning
by: Barad, Sagar Prakash, et al.
Published: (2024)

Accelerating Electron Dynamics Simulations through Machine Learned Time Propagators
by: Shah, Karan, et al.
Published: (2024)

Mixture of Experts Framework in Machine Learning Interatomic Potentials for Atomistic Simulations
by: Nascimento, Gabriel de Miranda, et al.
Published: (2026)

Generalizability of Graph Neural Network Force Fields for Predicting Solid-State Properties
by: Mohanty, Shaswat, et al.
Published: (2024)

Universal Magnetic Structure Prediction from Atomic Coordinates with Near-Experimental Accuracy
by: Chotrattanapituk, Abhijatmedhi, et al.
Published: (2026)

Machine Learning Time Propagators for Time-Dependent Density Functional Theory Simulations
by: Shah, Karan, et al.
Published: (2025)

Crystal Systems Classification of Phosphate-Based Cathode Materials Using Machine Learning for Lithium-Ion Battery
by: Yadav, Yogesh, et al.
Published: (2025)

Data-Efficient Indentation Size Effect Correction in Steels Using Machine Learning and Physics-Guided Augmentation
by: Karamov, Radmir, et al.
Published: (2026)

Machine Learning-Driven Crystal System Prediction for Perovskites Using Augmented X-ray Diffraction Data
by: Mathew, Ansu, et al.
Published: (2026)

Industrial-scale Prediction of Cement Clinker Phases using Machine Learning
by: Fayaz, Sheikh Junaid, et al.
Published: (2024)