Add to ORKGA new approach to potential fitting using neural networks This item was submitted to Loughborough University's Institutional Repository by the/an author. Citation: BHOLOA, A., KENNY, S.D. and SMITH, R., 2007. A new approach to potential fitting using neural networks. Nuclear instruments and methods in physics research section B: Beam interactions with materials and atoms, 25
Existing applications of artificial neural networks in physics research and development have been an...
This article presents a new framework for fitting measured scientific data to a simple empirical for...
This book discusses neural computation, a network or circuit of biological neurons and relatedly, pa...
A methodology is presented for developing transferable empirical potential functions without followi...
<p>Neural provides machine-learning tools to accelerate and extend atomistic calculations. In versio...
We present a method for fitting neural networks to geometric and energetic data sets. We then apply ...
The Relativistic Mean Field (RMF) model with a small number of adjusted parameters is powerful tool ...
A central concern of molecular dynamics simulations is the potential energy surfaces that govern ato...
Abstract. The artificial neural networks (ANNs) have emerged with successful applications in nuclear...
Neural networks provide an alternative approach for the solution of complex non-linear data fitting ...
We solved Schrödinger equation with Cornell potential (Coulomb-plus-linear potential) by using neura...
We implement a method for constructing analytic interatomic potentials by fitting artificial neural ...
An accurate impact parameter determination in a heavy ion collision is crucial for almost all furthe...
In this contribution, we present a status report on the recent progress towards an analysis of nucle...
WOS: 000327829400019One of the fundamental ground-state properties of nuclei is binding energy. Arti...
Existing applications of artificial neural networks in physics research and development have been an...
This article presents a new framework for fitting measured scientific data to a simple empirical for...
This book discusses neural computation, a network or circuit of biological neurons and relatedly, pa...
A methodology is presented for developing transferable empirical potential functions without followi...
<p>Neural provides machine-learning tools to accelerate and extend atomistic calculations. In versio...
We present a method for fitting neural networks to geometric and energetic data sets. We then apply ...
The Relativistic Mean Field (RMF) model with a small number of adjusted parameters is powerful tool ...
A central concern of molecular dynamics simulations is the potential energy surfaces that govern ato...
Abstract. The artificial neural networks (ANNs) have emerged with successful applications in nuclear...
Neural networks provide an alternative approach for the solution of complex non-linear data fitting ...
We solved Schrödinger equation with Cornell potential (Coulomb-plus-linear potential) by using neura...
We implement a method for constructing analytic interatomic potentials by fitting artificial neural ...
An accurate impact parameter determination in a heavy ion collision is crucial for almost all furthe...
In this contribution, we present a status report on the recent progress towards an analysis of nucle...
WOS: 000327829400019One of the fundamental ground-state properties of nuclei is binding energy. Arti...
Existing applications of artificial neural networks in physics research and development have been an...
This article presents a new framework for fitting measured scientific data to a simple empirical for...
This book discusses neural computation, a network or circuit of biological neurons and relatedly, pa...