How to validate machine|learned interatomic potentials

How to validate machine-learned interatomic potentials

We review the basic principles behind ML potentials and their validation for atomic-scale material modeling.

[2211.12484] How to validate machine-learned interatomic potentials

Title:How to validate machine-learned interatomic potentials ... Abstract:Machine learning (ML) approaches enable large-scale atomistic ...

How to validate machine-learned interatomic potentials - PubMed

Machine learning (ML) approaches enable large-scale atomistic simulations with near-quantum-mechanical accuracy.

How to validate machine-learned interatomic potentials

Machine-learning models are increasingly used to predict properties of atoms in chemical systems. There have been major advances in developing descriptors and ...

Machine Learning Interatomic Potentials and Long-Range Physics

... machine learned interatomic potentials (MLIPs). (6−8) MLIPs can be ... Machine Learning Force Fields: Construction, Validation, and Outlook.

Validation workflow for machine learning interatomic potentials for ...

The findings in this work support the need for a robust validation procedure for ML-based interatomic potentials due to their data-driven nature ...

Assessing the accuracy of machine learning interatomic potentials in ...

In atomistic modeling, machine learning interatomic potential (MLIP) has emerged as a powerful technique for studying alloy materials.

Machine-learned interatomic potentials by active learning - Nature

Our active learning scheme consists of an unsupervised machine learning (ML) scheme coupled with a Bayesian optimization technique that ...

MorrowChem/how-to-validate-potentials - GitHub

Some tutorial-style examples for validating machine-learned interatomic potentials - MorrowChem/how-to-validate-potentials.

Indirect learning and physically guided validation of interatomic ...

Machine learning (ML) based interatomic potentials are emerging tools for material simulations, but require a trade-off between accuracy and ...

Machine-learned interatomic potentials for transition metal ... - Nature

Machine Learned Interatomic Potentials (MLIPs) combine the predictive power of Density Functional Theory (DFT) with the speed and scaling of ...

Validation of machine-learned interatomic potentials via temperature ...

Abstract page for arXiv paper 2303.02519: Validation of machine-learned interatomic potentials via temperature-dependent electron thermal ...

A Genetic Algorithm Trained Machine-Learned Interatomic Potential ...

(8−11) Improvements can be attributed to the use of machine learning (ML) methodologies (e.g., neural networks (NN)) to approximate the ...

Advancing Machine-Learned Interatomic Potentials - YouTube

IMA Data Science Seminar Speaker: Yangshuai Wang, University of British Columbia "Advancing Machine-Learned Interatomic Potentials: ...

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

Ab Initio Molecular Dynamics (AIMD) Sampling: AIMD simulations to explore the configurational space. The temperature of the simulation ...

How to validate machine-learned interatomic potentials - a-z.lu

Machine learning (ML) approaches enable large-scale atomistic simulations with near-quantum-mechanical accuracy. With the growing availability of these ...

Indirect learning and physically guided validation of interatomic ...

This work shows how one can use one ML potential model to train another: an accurate, but more computationally expensive model is used to generate reference ...

Benchmarking machine learning interatomic potentials via phonon ...

Specifically, machine learning interatomic potentials (MLIPs) can accurately reproduce first-principles data at a cost similar to that of conventional ...

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.

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 ...