Machine|learning models of matter beyond interatomic potentials

Machine-learning models of matter beyond interatomic potentials

Combining electronic structure calculations and machine learning (ML) techniques has become a common approach in the atomistic modeling of ...

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

Machine Learning Interatomic Potential (MLIP) overcomes the challenges of high computational costs in density-functional theory and the relatively low accuracy

Beyond potentials: Integrated machine learning models for materials

Over the past decade, interatomic potentials based on machine learning (ML) techniques have become an indispensable tool in the atomic-scale ...

Beyond 3D-Machine Learning Interatomic Potentials: Meet 4D ...

We have introduced a concept of 4D-spacetime atomistic AI models that learn how the molecule changes in time. We demonstrate that this ...

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

Machine Learning Interatomic Potential (MLIP) overcomes the challenges of high computational costs in density-functional theory and the relatively low accuracy.

Extending machine learning beyond interatomic potentials for ...

Machine learning (ML) is becoming a method of choice for modelling complex chemical processes and materials. ML provides a surrogate model trained on a ...

Machine Learning Interatomic Potentials and Long-Range Physics

Ulocal refers to the short-range system energies and is typically inferred using a machine learning model trained on local features. Dispersion ...

(PDF) Beyond potentials: Integrated machine learning models for ...

PDF | Over the past decade, interatomic potentials based on machine learning (ML) techniques have become an indispensable tool in the atomic-scale.

New framework applies machine learning to atomistic modeling

Northwestern University researchers have developed a new framework using machine learning that improves the accuracy of interatomic ...

Machine learning for interatomic potential models - AIP Publishing

The use of supervised machine learning to develop fast and accurate interatomic potential models is transforming molecular and materials research.

the rise of machine learning interatomic potentials - RSC Publishing

Since the birth of the concept of machine learning interatomic potentials (MLIPs) in 2007, a growing interest has been developed in the ...

Machine-learning interatomic potentials for materials science

A third class of potentials is introduced, in which an ML model is coupled with a physics-based potential to improve the transferability to ...

Machine Learning for Molecular Properties: Going Beyond ...

Machine Learning for Molecular Properties: Going Beyond Interatomic Potentials ... modeling chemical processes and materials. Generally, ML provides a ...

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 potentials for materials science

A third class of potentials is introduced, in which an ML model is coupled with a physics-based potential to improve the transferability to unknown atomic ...

Machine‐learning‐based interatomic potentials for advanced ...

Interatomic potential is essential for classical molecular dynamics. The advancements made in machine learning (ML) have enabled the development ...

Performance Assessment of Universal Machine Learning ... - arXiv

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

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 ...

Daniel Schwalbe Koda: Machine learning for interatomic potentials

Boris Kozinsky - Uncertainty-aware machine learning models of many-body atomic interactions. Institute for Pure & Applied Mathematics (IPAM) ...

Machine Learning Interatomic Potentials and Long-Range Physics

Advances in machine learned interatomic potentials (MLIPs), such as those using neural networks, have resulted in short-range models that ...