Amp is an open-source package designed to easily bring machine-learning to atomistic calculations. This project is being developed at Brown University in the School of Engineering, primarily by Andrew Peterson and Alireza Khorshidi, and is released under the GNU General Public License. Amp allows for the modular representation of the potential energy surface, allowing the user to specify or create descriptor and regression methods. This project lives at: https://bitbucket.org/andrewpeterson/amp Documentation lives at: http://amp.readthedocs.org If you would like to compile a local version of the documentation, see the README file in the docs directory. *Corresponding author: andrew_peterson@brown.ed
We present our latest advancements of machine-learned potentials (MLPs) based on the neuroevolution ...
Designing molecules and materials with desired properties is an important prerequisite for advancing...
The data was interpreted in the following article: Machine Learning-Assisted High-Throughput Explor...
Amp is an open-source package designed to easily bring machine-learning to atomistic calculations. T...
<p>*Amp* is an open-source package designed to easily bring machine-learning to atomistic calculatio...
# Amp: Atomistic Machine-learning Package # *Amp* is an open-source package designed to easily bri...
# Amp: Atomistic Machine-learning Package # *Amp* is an open-source package designed to easily bri...
# Amp: Atomistic Machine-learning Potentials# Developed by Andrew Peterson & Alireza Khorshidi, Bro...
This program has been imported from the CPC Program Library held at Queen's University Belfast (1969...
Machine learning (ML) methods are being used in almost every conceivable area of electronic structur...
MLatom is a program package designed for computationally efficient simulations of atomistic systems ...
The use of computational algorithms, implemented on a computer, to extract information from data has...
<p>Neural provides machine-learning tools to accelerate and extend atomistic calculations. In versio...
Machine learning (ML) approximations to density functional theory (DFT) potential energy surfaces (P...
Machine-learning potentials (MLPs) trained on data from quantum-mechanics based first-principles met...
We present our latest advancements of machine-learned potentials (MLPs) based on the neuroevolution ...
Designing molecules and materials with desired properties is an important prerequisite for advancing...
The data was interpreted in the following article: Machine Learning-Assisted High-Throughput Explor...
Amp is an open-source package designed to easily bring machine-learning to atomistic calculations. T...
<p>*Amp* is an open-source package designed to easily bring machine-learning to atomistic calculatio...
# Amp: Atomistic Machine-learning Package # *Amp* is an open-source package designed to easily bri...
# Amp: Atomistic Machine-learning Package # *Amp* is an open-source package designed to easily bri...
# Amp: Atomistic Machine-learning Potentials# Developed by Andrew Peterson & Alireza Khorshidi, Bro...
This program has been imported from the CPC Program Library held at Queen's University Belfast (1969...
Machine learning (ML) methods are being used in almost every conceivable area of electronic structur...
MLatom is a program package designed for computationally efficient simulations of atomistic systems ...
The use of computational algorithms, implemented on a computer, to extract information from data has...
<p>Neural provides machine-learning tools to accelerate and extend atomistic calculations. In versio...
Machine learning (ML) approximations to density functional theory (DFT) potential energy surfaces (P...
Machine-learning potentials (MLPs) trained on data from quantum-mechanics based first-principles met...
We present our latest advancements of machine-learned potentials (MLPs) based on the neuroevolution ...
Designing molecules and materials with desired properties is an important prerequisite for advancing...
The data was interpreted in the following article: Machine Learning-Assisted High-Throughput Explor...