Representation of Potential Energy Surfaces using Neural Networks

Umme Kulsum , Raza Imam , Mohd Abdullah Khan , Asra Ansari

Potential Energy Surfaces, Neural Networks, Morse Potential, Activation Function, Dimensional Curves

Deep learning is ideally suited for modelling nonlinear potential-energy surfaces, expressing quantum-mechanical interactions, and expanding chemical compound space research. Given the presence of hidden layers, neural networks do more effective predictive analyses as the neural network employs the multiple hidden layers to improve prediction accuracy. There is a requirement for precise potentials that can swiftly repeat high-quality results since the interactions in force fields are represented by a variety of different functions. In this work, we strive to investigate the representation of Potential Energy Surfaces, a crucial component of chemical dynamics, using neural networks. We developed neural network models that can be applied widely to fit one-dimensional data and two-dimensional potential energy surfaces separately. Our methodology concludes different key analytical outcomes as well as crucial future directions that aim to strengthen the potential of chemical dynamics and machine learning.

Published Paper   E-Certificate

"Representation of Potential Energy Surfaces using Neural Networks", IJEDR - INTERNATIONAL JOURNAL OF ENGINEERING DEVELOPMENT AND RESEARCH (www.IJEDR.org), ISSN:2321-9939, Vol.10, Issue 4, page no.32-42, November 2022, Available :https://rjwave.org/IJEDR/papers/IJEDR2204004.pdf

Volume 10 Issue 4, October-2022

Pages : 32-42

Paper Reg. ID: IJEDR_220217

Published Paper Id: IJEDR2204004

Research Area: Science & Technology

Country: Aligarh, UP, India

Published Paper PDF: https://rjwave.org/IJEDR/papers/IJEDR2204004

Published Paper URL: https://rjwave.org/IJEDR/viewpaperforall?paper=IJEDR2204004

DOI: http://doi.one/10.1729/Journal.31971

ISSN: 2321-9939 | IMPACT FACTOR: 9.37 Calculated By Google Scholar | ESTD YEAR: 2013

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.37 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Publisher: IJEDR (IJ Publication) Janvi Wave

This work is licensed under a Creative Commons Attribution 4.0 International License and The Open Definition