Abstract
Chemistry dynamics simulation is widely used in quantitative structure activity relationship QSAR, virtual screening, protein structure prediction, quantum chemistry, materials design, and property prediction, etc. This chapter explores the idea of integrating Long Short-Term Memory (LSTM) with chemistry dynamics simulations to enhance the performance of the simulation and improve its usability for research and education. The idea is successfully used to predict the location, energy, and Hessian of atoms in a H2O reaction system. The results demonstrate that the artificial neural network–based memory model successfully learns the desired features associated with the atomic trajectory and rapidly generates predictions that are in excellent agreement with the results from chemistry dynamics simulations. The accuracy of the prediction is better than expected.
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Acknowledgments
This work was supported by Dr. Hase research group and Chemdynm cluster at Texas Tech University, as well as the Industrial Internet Innovation and Development Project of China: Digital twin system for automobile welding and casting production lines and its application demonstration (TC9084DY).
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Wu, H., Lu, S., Eduardo, CD., Liang, J., She, J., Tan, X. (2021). Long Short-Term Memory in Chemistry Dynamics Simulation. In: Arabnia, H.R., Ferens, K., de la Fuente, D., Kozerenko, E.B., Olivas Varela, J.A., Tinetti, F.G. (eds) Advances in Artificial Intelligence and Applied Cognitive Computing. Transactions on Computational Science and Computational Intelligence. Springer, Cham. https://doi.org/10.1007/978-3-030-70296-0_9
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