- Applications of Machine Learning for Representing Interatomic ...🔍
- Applications of machine‐learning interatomic potentials for modeling ...🔍
- Machine|learned interatomic potential🔍
- Machine learning interatomic potential🔍
- Chapter 3. Applications of Machine Learning for Representing ...🔍
- Machine|learning interatomic potentials for materials science🔍
- Machine Learning Interatomic Potentials and Long|Range Physics🔍
- Towards foundation models for machine learning interatomic ...🔍
Applications of Machine Learning for Representing Interatomic ...
Applications of Machine Learning for Representing Interatomic ...
The application of machine learning to interatomic interaction has recently received a lot of attention from researchers in chemistry, materials science, ...
Applications of machine‐learning interatomic potentials for modeling ...
Specifically, crystalline materials are now widely explored using quantum mechanical (QM) calculation and density functional theory (DFT) with a ...
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 ...
Furthermore, the applications of MLIP in various fields are investigated, notably in phase-change memory materials, structure searching, ...
Applications of Machine Learning for Representing Interatomic ...
Machine learning focuses on prediction, based on known properties learned from training data. In computational materials science, ...
Chapter 3. Applications of Machine Learning for Representing ...
... The key idea is to develop machine-interatomic potentials which map a set of atomic environments onto the numerical values of energies, forces, stresses, ...
Machine-learning interatomic potentials for materials science
Many new functional forms of the potentials have been proposed to im- prove the accuracy of representing the chemical bonding in various classes of materials.
Machine Learning Interatomic Potentials and Long-Range Physics
Therefore, an MLIP model that aims to accurately represent an application space where polarization effects have significant contributions ...
Towards foundation models for machine learning interatomic ...
Taking machine learning interatomic potentials (MLIPs) as an example, we show that meta-learning techniques, a recent advancement from the ...
Chapter 3:Applications of Machine Learning for Representing ...
This chapter illustrates the approaches used in machine learning for constructing new interatomic potentials, and the improvements over the empirical force ...
Machine Learning Interatomic Potentials and Long-Range Physics
The rise of data-driven techniques, particularly simulations performed with machine learned interatomic potentials, has demonstrated the ...
Applications of machine‐learning interatomic potentials for modeling ...
As a result, there are high expectations for further applications of MLPs in the field of material science and industrial development. This ...
Machine learning for interatomic potential models - AIP Publishing
One of the most promising applications of machine learning to materials and molecular research is the development of fast and accurate ...
Evidential Deep Learning for Interatomic Potentials - arXiv
Machine learning interatomic potentials (MLIPs) have been widely used to facilitate large scale molecular simulations with ab initio level ...
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.
A machine learning interatomic potential for high entropy alloys
The fully trained ML model can achieve remarkable prediction precision (>0.92 R2) for atomic forces, comparable to the ab initio molecular dynamics (AIMD) ...
How to validate machine-learned interatomic potentials
Machine learning (ML) approaches enable large-scale atomistic simulations with near-quantum-mechanical accuracy.
Machine-learned interatomic potentials for alloys and alloy phase ...
The moment tensor potential (MTP) is another approach to learning quantum-mechanical potential energy surfaces. Due to the efficiency of its ...
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) ...
Application of machine learning potentials to predict grain boundary ...
Accurate interatomic potentials are in high demand for large-scale atomistic simulations of materials that are prohibitively expensive by ...