1. Case, D.; Aktulga, H.; Belfon, K.; Ben-Shalom, I.; Berryman, J.; Brozell, S.; Cerutti, D.; Cheatham, T.; Cisneros, G.; Cruzeiro, V.; Darden, T.; Forouzesh, N.; Giambaşu, G.; Giese, T.; Gilson, M.; Gohlke, H.; Goetz, A.; Harris, J.; Izadi, S.; Izmailov, S.; Kasavajhala, K.; Kaymak, M.; King, E.; Kovalenko, A.; Kurtzman, T.; Lee, T.; Li, P.; Lin, C.; Liu, J.; Luchko, T.; Luo, R.; Machado, M.; Man, V.; Manathunga, M.; Merz, K.; Miao, Y.; Mikhailovskii, O.; Monard, G.; Nguyen, H.; O’Hearn, K.; Onufriev, A.; Pan, F.; Pantano, S.; Qi, R.; Rahnamoun, A.; Roe, D.; Roitberg, A.; Sagui, C.; Schott-Verdugo, S.; Shajan, A.; Shen, J.; Simmerling, C.; Skrynnikov, N.; Smith, J.; Swails, J.; Walker, R.; Wang, J.; Wang, J.; Wei, H.; Wu, X.; Wu, Y.; Xiong, Y.; Xue, Y.; York, D.; Zhao, S.; Zhu, Q.; Kollman, P. Amber 2023; University of California: San Francisco, 2023.
2. Towards exact molecular dynamics simulations with machine-learned force fields
3. Machine Learning Force Fields
4. Machine learning of accurate energy-conserving molecular force fields
5. sGDML: Constructing accurate and data efficient molecular force fields using machine learning