Machine|learning interatomic potentials

Machine-learned interatomic potential - Wikipedia

Machine-learned interatomic potentials (MLIPs), or simply machine learning potentials (MLPs), are interatomic potentials constructed by machine learning ...

Machine learning interatomic potential: Bridge the gap between ...

Summary. Machine learning interatomic potential (MLIP) overcomes the challenges of high computational costs in density-functional theory and the ...

Machine Learning Interatomic Potentials and Long-Range Physics

We aim to provide a pointed discussion to support the development of machine learning-based interatomic potentials for systems where contributions from only ...

Recent advances and outstanding challenges for machine learning ...

Machine learning interatomic potentials (MLIPs) enable materials simulations at extended length and time scales with near-ab initio accuracy ...

Performance Assessment of Universal Machine Learning ...

Machine learning interatomic potentials (MLIPs) are one of the main techniques in the materials science toolbox, able to bridge ab initio ...

Machine-learning interatomic potentials for materials science

ML potentials have emerged as a powerful new tool for materials modeling and new materials discovery. When used within the boundaries of ...

Geometry-enhanced pretraining on interatomic potentials - Nature

Machine learning interatomic potentials (MLIPs) describe the interactions between atoms in materials and molecules by learning them from a ...

Machine-learning interatomic potentials for materials science - arXiv

The new potentials are constructed using machine-learning (ML) methods and a massive reference database generated by quantum-mechanical calculations.

How to validate machine-learned interatomic potentials

Machine learning means extracting information from large datasets—in this case, from quantum-mechanical energies and forces. An ML potential is, ...

Machine-learned interatomic potentials: Recent developments and ...

Recognizing the innovative character of machine learning approaches to generate interatomic potentials, the field started to expand rapidly ...

Machine Learning Interatomic Potentials and Accessible Databases

Machine Learning Interatomic Potentials and Accessible Databases · September 9, 2024 - September 11, 2024 · Registration deadline: August 14, 2024 · Location: ...

[2310.13756] Learning Interatomic Potentials at Multiple Scales - arXiv

Machine learning interatomic potentials (MLIPs), in particular recent equivariant neural networks, are much more broadly applicable than ...

Daniel Schwalbe Koda: Machine learning for interatomic potentials

This video was recorded as part of the 4th IKZ - FAIRmat winter school, a hybrid event, online and on-site in Berlin, January 23 -25, 2023.

Machine learning for interatomic potential models - AIP Publishing

The development of interatomic potential models falls into a branch of machine learning known as supervised machine learning, which is reviewed ...

Simple machine-learned interatomic potentials for complex alloys

Developing data-driven machine-learning interatomic potential for materials containing many elements becomes increasingly challenging due to ...

Machine-learning interatomic potentials enable first-principles ...

One of the ultimate goals of computational modeling in condensed matter is to be able to accurately compute materials properties with minimal empirical ...

Atomic Machine Learning Potentials - Fritz Haber Institute

In contrast, machine learned interatomic potentials (MLIP) offer an alternative route forward as they show great promise of providing both sufficient accuracy ...

Machine Learning Interatomic Potentials for Catalysis - Tang

Atomistic modeling can provide insights into the design of novel catalysts in modern industries of chemistry, materials science, ...

Benchmarking machine learning interatomic potentials via phonon ...

Here, we benchmark popular MLIPs using the anharmonic vibrational Hamiltonian of ThO 2 in the fluorite crystal structure.

Theory and PracticeMachine Learning Interatomic Potentials - CECAM

Machine Learning Interatomic Potentials: Theory and Practice ... Organisers. Machine learning interatomic potentials (ML-IPs) have now established themselves as a ...