Cover Image

Overcoming systematic softening in universal machine learning interatomic potentials by fine-tuning

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Deng, Bowen, Choi, Yunyeong, Zhong, Peichen, Riebesell, Janosh, Anand, Shashwat, Li, Zhuohan, Jun, KyuJung, Persson, Kristin A., Ceder, Gerbrand
Format:	Preprint
Published:	2024
Subjects:	Materials Science Artificial Intelligence Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Modeling phase transformations in Mn-rich disordered rocksalt cathodes with machine learning interatomic potentials
by: Zhong, Peichen, et al.
Published: (2025)

Cross-functional transferability in universal machine learning interatomic potentials
by: Huang, Xu, et al.
Published: (2025)

Origin of Enhanced Performance when Mn-Rich Rocksalt Cathodes transform to $δ$-DRX
by: Anand, Shashwat, et al.
Published: (2025)

Hierarchical high-throughput screening of alkaline-stable lithium-ion conductors combining machine learning and first-principles calculations
by: Li, Zhuohan, et al.
Published: (2025)

Effect of cation-disorder on lithium transport in halide superionic conductors
by: Zhong, Peichen, et al.
Published: (2024)

Mechanisms of alkali ionic transport in amorphous oxyhalides solid state conductors
by: Binci, Luca, et al.
Published: (2026)

Crystal structure prediction with host-guided inpainting generation and foundation potentials
by: Zhong, Peichen, et al.
Published: (2025)

Matbench Discovery -- A framework to evaluate machine learning crystal stability predictions
by: Riebesell, Janosh, et al.
Published: (2023)

Revealing Dynamic Evolution of the Anode‐Electrolyte Interphase in All‐Solid‐State Batteries with Excellent Cyclability
by: Se Young Kim, et al.
Published: (2024)

A Foundational Potential Energy Surface Dataset for Materials
by: Kaplan, Aaron D., et al.
Published: (2025)

Deep learning of experimental electrochemistry for battery cathodes across diverse compositions
by: Zhong, Peichen, et al.
Published: (2023)

MP-ALOE: An r2SCAN dataset for universal machine learning interatomic potentials
by: Kuner, Matthew C., et al.
Published: (2025)

An efficient forgetting-aware fine-tuning framework for pretrained universal machine-learning interatomic potentials
by: Kim, Jisu, et al.
Published: (2025)

Voltage Mining for (De)lithiation-stabilized Cathodes and a Machine Learning Model for Li-ion Cathode Voltage
by: Li, Haoming Howard, et al.
Published: (2024)

A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials
by: Kim, Dongjin, et al.
Published: (2025)

Li+/H+ exchange in solid-state oxide Li-ion conductors
by: Li, Zhuohan, et al.
Published: (2025)

TorchSim: An efficient atomistic simulation engine in PyTorch
by: Cohen, Orion, et al.
Published: (2025)

Ion correlations explain kinetic selectivity in diffusion-limited solid state synthesis reactions
by: Karan, Vir, et al.
Published: (2025)

Degradation Mechanism of Phosphate‐Based Li‐NASICON Conductors in Alkaline Environment
by: Benjamin X. Lam, et al.
Published: (2024)

Machine-Learning-Guided Insights into Solid-Electrolyte Interphase Conductivity: Are Amorphous Lithium Fluorophosphates the Key?
by: Zhong, Peichen, et al.
Published: (2025)

Fine-tuning of universal machine-learning interatomic potentials for 2D high-entropy alloys
by: Zhou, Chun, et al.
Published: (2026)

CASCADE: Cumulative Agentic Skill Creation through Autonomous Development and Evolution
by: Huang, Xu, et al.
Published: (2025)

Crystallographic Challenges in Microscopy of Multidomain Spinel Materials
by: Scherz, Ninon, et al.
Published: (2026)

An AI-ready fine-tuning framework for accurate machine-learning interatomic potentials in solid-solid battery interfaces
by: Liu, Xiaoqing, et al.
Published: (2026)

A practical guide to machine learning interatomic potentials -- Status and future
by: Jacobs, Ryan, et al.
Published: (2025)

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

The Interplay Between Electron Localization, Magnetic Order, and Jahn-Teller Distortion that Dictates LiMnO$_2$ Phase Stability
by: Kam, Ronald L., et al.
Published: (2024)

DistMLIP: A Distributed Inference Platform for Machine Learning Interatomic Potentials
by: Han, Kevin, et al.
Published: (2025)

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

Systematic assessment of various universal machine‐learning interatomic potentials
by: Haochen Yu, et al.
Published: (2024)

Systematic assessment of various universal machine-learning interatomic potentials
by: Yu, Haochen, et al.
Published: (2024)

Screening of material defects using universal machine-learning interatomic potentials
by: Berger, Ethan, et al.
Published: (2025)

Alkali‐Ion‐Assisted Activation of ε‐VOPO4 as a Cathode Material for Mg‐Ion Batteries
by: Dogancan Sari, et al.
Published: (2024)

Overcoming sampling limitations using machine-learned interatomic potentials: the case of water-in-salt electrolytes
by: Brugnoli, Luca, et al.
Published: (2026)

Thermodynamic accessibility of Li-Mn-Ti-O cation disordered rock-salt phases
by: Kam, Ronald L., et al.
Published: (2026)

Machine learning interatomic potentials for lithium battery electrolyte design
by: Gunwook Nam, et al.
Published: (2026)

LLaMP: Large Language Model Made Powerful for High-fidelity Materials Knowledge Retrieval and Distillation
by: Chiang, Yuan, et al.
Published: (2024)

Knowing when to trust machine-learned interatomic potentials
by: Mehdi, Shams, et al.
Published: (2026)

Platonic representation of foundation machine learning interatomic potentials
by: Li, Zhenzhu, et al.
Published: (2025)

Cartesian atomic moment machine learning interatomic potentials
by: Wen, Mingjian, et al.
Published: (2024)