Decoding the commutable first hyperpolarisability of keto–enol tautomer using DFT and AI-based soft computing method

ABSTRACT

Non-linear optical (NLO) parameter of a series of donor–acceptor substituted N-salicylidene-4-benzenesulfonylaniline (SBSA) derivatives with a contrast of first hyperpolarisability has been investigated using density functional-based theory. The derivative of SBSA with both the donor and acceptor substitution (NMe₂-SBSA-NO₂) exhibits the highest first hyperpolarisability ($\sim 3\times$10³ a.u in gas phase) value. Frequency and solvent polarity responsive behaviours of first hyperpolarisability are utilised to demonstrate the variation of NLO as switchable electrical parameter. Each of the studied compounds displays very high NLO response at frequency corresponding to the absorption maxima of their electronic state. Likewise, the increase of solvent polarity induces a substantial increase of first hyperpolarisability. The computation of NLO responsive first hyperpolarisability by varying the frequency and solvent dielectric constant within a wide domain is very tedious and time-consuming. To overcome the lacuna, we implemented machine learning tools such as artificial neural networks (ANNs), fuzzy logic (FL) and adaptive neuro-fuzzy inference system (ANFIS) to predict the computation data of the NMe₂-SBSA-NO₂ and moderately good results were obtained using ANFIS as soft computing tool.

KEYWORDS:

• DFT CAM-B3LYP
• hyper-Raleigh scattering (HRS)
• fuzzy logic (FL)
• artificial neural networks (ANNs)
• adaptive neuro-fuzzy inference system (ANFIS)

Acknowledgement

U. Mandal (ref. no. 188/CSIR-UGC NET June 2019) thanks to UGC and S.S.Samanta (ref. no. 09/599(0084)/2019-EMR-I) thanks to CSIR for their individual research fellowship. Departmental computational facilities from DST-FIST (Ref. No. SR/FST/CSI-235/2011) and UGC-SAP (Ref. No. F.5-9/2015/DRS-11 (SAP-11)) programmes are gratefully acknowledged. We are thankful to Prof. S. Roy, Dept. of Applied Math of Vidyasagar University for his help and suggestions to carry out the machine learning part of the article.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Authors’ contributions

All the authors (Usha Mandal, Shashanka Shekhar Samanta, Sourav Mandal, Suraj Barman, Hasibul Beg and Ajay Misra) made equal contributions while preparing the manuscript and approve the final manuscript. The manuscript was written by U. Mandal and A. Misra.

Availability of data

Raw data are available on request to the author.

Additional information

Funding

This work was supported by University Grants Commission: [Grant Number 188/(CSIR-UGC NET) June 2019].