Cover Image

Universal Machine Learning Potentials under Pressure

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Loew, Antoine, Schmidt, Jonathan, Botti, Silvana, Marques, Miguel A. L.
Format:	Preprint
Published:	2025
Subjects:	Materials Science
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Universal Machine Learning Potential for Systems with Reduced Dimensionality
by: Benedini, Giulio, et al.
Published: (2025)

Universal Machine Learning Interatomic Potentials are Ready for Phonons
by: Loew, Antoine, et al.
Published: (2024)

A non-orthogonal representation of the chemical space
by: Cerqueira, Tiago F. T., et al.
Published: (2024)

AI-Driven Expansion and Application of the Alexandria Database
by: Cavignac, Théo, et al.
Published: (2025)

Intrinsic Point Defects and Frenkel Pair Formation in Photovoltaic Absorber Zn$_3$P$_2$: Regulating $p$-type Conductivity through Growth and Annealing Conditions
by: Kawashima, Nico, et al.
Published: (2026)

Generative AI for Crystal Structures: A Review
by: De Breuck, Pierre-Paul, et al.
Published: (2025)

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

Accelerating point defect photo-emission calculations with machine learning interatomic potentials
by: Sharma, Kartikeya, et al.
Published: (2025)

Favorable band alignment for photocatalysis at the strontium germanate interface with silicon
by: Rauch, Tomáš, et al.
Published: (2022)

Functional and Density-Driven Errors in Density Functional Theory: Quantum Monte Carlo Benchmarks for Solids
by: Aouina, Ayoub, et al.
Published: (2026)

Machine-Learning-enabled ab initio study of quantum phase transitions in SrTiO$_3$
by: Schmidt, Jonathan, et al.
Published: (2025)

Optical selection rules in hexagonal Ge polytypes and their lifting by symmetry perturbation
by: Keller, Martin, et al.
Published: (2026)

Benchmarking Universal Machine Learning Interatomic Potentials on Elemental Systems
by: Tahmasbi, Hossein, et al.
Published: (2025)

Stability and Structure of Binary Metal Hydrides under Pressure, Electrochemical Potential and Combined Pressure-Electrochemistry
by: Phuthi, Mgcini Keith, et al.
Published: (2025)

High-throughput study of electrical conductivity in ordered metals
by: da Silva, Thalis H. B., et al.
Published: (2026)

Optimizing Cross-Domain Transfer for Universal Machine Learning Interatomic Potentials
by: Kim, Jaesun, et al.
Published: (2025)

Benchmarking Universal Machine Learning Interatomic Potentials for Elastic Property Prediction
by: Gao, Pengfei, et al.
Published: (2025)

High-Pressure Inelastic Neutron Spectroscopy: A true test of Machine-Learned Interatomic Potential energy landscapes
by: Armstrong, Jeff, et al.
Published: (2026)

Real-time simulations of laser-induced electron excitations in crystalline ZnO
by: Chen, Xiao, et al.
Published: (2025)

Accelerating High-Throughput Phonon Calculations via Machine Learning Universal Potentials
by: Lee, Huiju, et al.
Published: (2024)

Bias in Universal Machine-Learned Interatomic Potentials and its Effects on Fine-Tuning
by: Wong, Nicolas, et al.
Published: (2026)

A "Magnetic" Machine Learning Interatomic Potential for Nickel
by: Gong, Xiaoguo, et al.
Published: (2023)

Beyond Scaling: Chemical Intuition as Emergent Ability of Universal Machine Learning Interatomic Potentials
by: Hattori, Shinnosuke, et al.
Published: (2025)

Machine Learning a Universal Harmonic Interatomic Potential for Predicting Phonons in Crystalline Solids
by: Lee, Huiju, et al.
Published: (2024)

Machine learning the derivative discontinuity of density-functional theory
by: Gedeon, Johannes, et al.
Published: (2021)

Uni2D: A Universal Machine Learning Interatomic Potential for Two-Dimensional Materials
by: Wang, Haidi, et al.
Published: (2025)

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

Uncertainty Quantification for Misspecified Machine Learned Interatomic Potentials
by: Perez, Danny, et al.
Published: (2025)

A Lightweight Universal Machine-Learning Interatomic Potential via Knowledge Distillation for Scalable Atomistic Simulations
by: Oh, Sangmin, et al.
Published: (2026)

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

Expanding Universal Machine Learning Interatomic Potentials to 97 Elements Towards Nuclear Applications
by: Kuroda, Naoya, et al.
Published: (2026)

Symplectic Spin-Lattice Dynamics with Machine-Learning Potentials
by: Huang, Zhengtao, et al.
Published: (2025)

Classical and Machine Learning Interatomic Potentials for BCC Vanadium
by: Wang, Rui, et al.
Published: (2022)

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

Benchmarking Universal Machine-Learned Interatomic Potentials for High-Temperature Metal-Organic Framework Chemistry
by: Edwards, Connor W., et al.
Published: (2026)

Evaluating Mechanical Property Prediction across Material Classes using Molecular Dynamics Simulations with Universal Machine-Learned Interatomic Potentials
by: Stracke, Konstantin, et al.
Published: (2025)

Benchmarking CHGNet Universal Machine Learning Interatomic Potential Against DFT and EXAFS: Case of Layered WS2 and MoS2
by: Žguns, Pjotrs, et al.
Published: (2025)

Li-P-S Electrolyte Materials as a Benchmark for Machine-Learned Interatomic Potentials
by: Fragapane, Natascia L., et al.
Published: (2025)

A Thermodynamic Constraint for the Electronic Structure of Fe-Ni Alloys at Room And High-Temperature
by: Paras, Jonathan, et al.
Published: (2024)

Evidence of Ordering in Cu-Ni Alloys from Experimental Electronic Entropy Measurements
by: Paras, Jonathan, et al.
Published: (2023)