Cover Image

Flexible Cutoff Learning: Optimizing Machine Learning Potentials After Training

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Oerder, Rick, Hamaekers, Jan
Format:	Preprint
Published:	2026
Subjects:	Materials Science Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Materium: An Autoregressive Approach for Material Generation
by: Dobberstein, Niklas, et al.
Published: (2025)

Gaussian Process Regression-based Knowledge Distillation Framework for Simultaneous Prediction of Physical and Mechanical Properties of Epoxy Polymers
by: S., Sindu B., et al.
Published: (2026)

Automated Machine Learning Pipeline: Large Language Models-Assisted Automated Dataset Generation for Training Machine-Learned Interatomic Potentials
by: Lahouari, Adam, et al.
Published: (2025)

Accelerating the Training and Improving the Reliability of Machine-Learned Interatomic Potentials for Strongly Anharmonic Materials through Active Learning
by: Kang, Kisung, et al.
Published: (2024)

Enhancing Machine Learning Potentials through Transfer Learning across Chemical Elements
by: Röcken, Sebastien, et al.
Published: (2025)

Self-Optimizing Machine Learning Potential Assisted Automated Workflow for Highly Efficient Complex Systems Material Design
by: Li, Jiaxiang, et al.
Published: (2025)

PFT: Phonon Fine-tuning for Machine Learned Interatomic Potentials
by: Koker, Teddy, et al.
Published: (2026)

NepTrain and NepTrainKit: Automated Active Learning and Visualization Toolkit for Neuroevolution Potentials
by: Chen, Chengbing, et al.
Published: (2025)

Maximizing Efficiency of Dataset Compression for Machine Learning Potentials With Information Theory
by: Yu, Benjamin, et al.
Published: (2025)

Hydrogen under Pressure as a Benchmark for Machine-Learning Interatomic Potentials
by: Bischoff, Thomas, et al.
Published: (2024)

Universal Machine Learning Interatomic Potentials are Ready for Solid Ion Conductors
by: Du, Hongwei, et al.
Published: (2025)

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)

Efficient and Accurate Spatial Mixing of Machine Learned Interatomic Potentials for Materials Science
by: Birks, Fraser, et al.
Published: (2025)

Model Accuracy and Data Heterogeneity Shape Uncertainty Quantification in Machine Learning Interatomic Potentials
by: Shuang, Fei, et al.
Published: (2025)

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

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

P-DRUM: Post-hoc Descriptor-based Residual Uncertainty Modeling for Machine Learning Potentials
by: Huang, Shih-Peng, et al.
Published: (2025)

Feature-based prediction of properties of cross-linked epoxy polymers by molecular dynamics and machine learning techniques
by: S., Sindu B., et al.
Published: (2023)

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

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

Accelerating CALPHAD-based Phase Diagram Predictions in Complex Alloys Using Universal Machine Learning Potentials: Opportunities and Challenges
by: Zhu, Siya, et al.
Published: (2024)

Neutron and X-ray Diffraction Reveal the Limits of Long-Range Machine Learning Potentials for Medium-Range Order in Silica Glass
by: Balantrapu, Sai Harshit, et al.
Published: (2026)

Curiosity Driven Exploration to Optimize Structure-Property Learning in Microscopy
by: Vatsavai, Aditya, et al.
Published: (2025)

When More Data Hurts: Optimizing Data Coverage While Mitigating Diversity Induced Underfitting in an Ultra-Fast Machine-Learned Potential
by: Gibson, Jason B., et al.
Published: (2024)

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

Training Variation of Physically-Informed Deep Learning Models
by: Lenau, Ashley, et al.
Published: (2025)

Acceleration of Atomistic NEGF: Algorithms, Parallelization, and Machine Learning
by: Luisier, Mathieu, et al.
Published: (2026)

Explainable Machine Learning for Oxygen Diffusion in Perovskites and Pyrochlores
by: Lu, Grace M., et al.
Published: (2025)

Machine Learning for Improved Density Functional Theory Thermodynamics
by: Simak, Sergei I., et al.
Published: (2025)

MOFSimBench: Evaluating Universal Machine Learning Interatomic Potentials In Metal--Organic Framework Molecular Modeling
by: Kraß, Hendrik, et al.
Published: (2025)

Boltzmann Reinforcement Learning for Noise resilience in Analog Ising Machines
by: Choudhary, Aditya, et al.
Published: (2026)

Thermodynamic Prediction Enabled by Automatic Dataset Building and Machine Learning
by: Liu, Juejing, et al.
Published: (2025)

PGNAA Spectral Classification of Aluminium and Copper Alloys with Machine Learning
by: Folz, Henrik, et al.
Published: (2024)

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

A Bayesian Committee Machine Potential for Oxygen-containing Organic Compounds
by: Kim, Seungwon, et al.
Published: (2024)

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

Lang2MLIP: End-to-End Language-to-Machine Learning Interatomic Potential Development with Autonomous Agentic Workflows
by: Li, Wenwen, et al.
Published: (2026)

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

Surface Stability Modeling with Universal Machine Learning Interatomic Potentials: A Comprehensive Cleavage Energy Benchmarking Study
by: Mehdizadeh, Ardavan, et al.
Published: (2025)