@article{(Open Science Index):https://publications.waset.org/pdf/10013557,
	  title     = {Accelerating Quantum Chemistry Calculations: Machine Learning for Efficient Evaluation of Electron-Repulsion Integrals},
	  author    = {Nishant Rodrigues and  Nicole Spanedda and  Chilukuri K. Mohan and  Arindam Chakraborty},
	  country	= {},
	  institution	= {},
	  abstract     = {A crucial objective in quantum chemistry is the computation of the energy levels of chemical systems. This task requires electron-repulsion integrals as inputs and the steep computational cost of evaluating these integrals poses a major numerical challenge in efficient implementation of quantum chemical software. This work presents a moment-based machine learning approach for the efficient evaluation of electron-repulsion integrals. These integrals were approximated using linear combinations of a small number of moments. Machine learning algorithms were applied to estimate the coefficients in the linear combination. A random forest approach was used to identify promising features using a recursive feature elimination approach, which performed best for learning the sign of each coefficient, but not the magnitude. A neural network with two hidden layers was then used to learn the coefficient magnitudes, along with an iterative feature masking approach to perform input vector compression, identifying a small subset of orbitals whose coefficients are sufficient for the quantum state energy computation. Finally, a small ensemble of neural networks (with a median rule for decision fusion) was shown to improve results when compared to a single network.},
	    journal   = {International Journal of Nuclear and Quantum Engineering},
	  volume    = {18},
	  number    = {3},
	  year      = {2024},
	  pages     = {48 - 53},
	  ee        = {https://publications.waset.org/pdf/10013557},
	  url   	= {https://publications.waset.org/vol/207},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 207, 2024},
	}