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Everything about Machine-learned interatomic potential
Interatomic potentials constructed by machine learning programs
Machine-learned interatomic potentials (MLIPs), or simply machine learning potentials (MLPs), are interatomic potentials constructed using machine learning. Beginning in the 1990s, researchers have employed such programs to construct interatomic potentials by mapping atomic structures to their potential energies. These potentials are referred to as MLIPs or MLPs. Wikipedia →
🧭 Start here: a 6-step reading path
- Read the overview: Machine-learned interatomic potentialA 5-minute orientation
- Skim a recent paper: Proton Transport in Perfluorinated Ionomer Simulated by Machine-Learned Interatomic PotentialInfluential work since 2022
- Read the landmark paper: Machine-learned interatomic potentials for alloys and alloy phase diagramsCited 143 times
- Go sideways: Interatomic potentialA closely related topic
- Go sideways: Machine learning in physicsA closely related topic
📖 Definitions
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🏆 Landmark papers (most cited)
Machine-learned interatomic potentials for alloys and alloy phase diagrams
How to validate machine-learned interatomic potentials
A machine-learned interatomic potential for silica and its relation to empirical models
Unraveling the crystallization kinetics of the Ge2Sb2Te5 phase change compound with a machine-learned interatomic potential
Simple machine-learned interatomic potentials for complex alloys
Experimentally Driven Automated Machine-Learned Interatomic Potential for a Refractory Oxide
Machine-learned interatomic potentials: Recent developments and prospective applications
Gold–Thiolate Nanocluster Dynamics and Intercluster Reactions Enabled by a Machine Learned Interatomic Potential
🆕 Influential since 2022
Proton Transport in Perfluorinated Ionomer Simulated by Machine-Learned Interatomic Potential
Transferability of Data Sets between Machine-Learned Interatomic Potential Algorithms
Machine learned interatomic potential for dispersion strengthened plasma facing components
Machine learned interatomic potentials using random features
Constructing and Evaluating Machine-Learned Interatomic Potentials for Li-Based Disordered Rocksalts
📚 Books worth reading
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