Editing Molecular dynamics (section)

=== Machine Learning Force Fields ===
Machine Learning Force Fields] (MLFFs) represent one approach to modeling interatomic interactions in molecular dynamics simulations.<ref name="Unke_2021">{{cite journal | vauthors = Unke OT, Chmiela S, Sauceda HE, Gastegger M, Poltavsky I, Schütt KT, Tkatchenko A, Müller KR | title = Machine Learning Force Fields | journal = Chemical Reviews | volume = 121 | issue = 16 | pages = 10142–10186 | date = August 2021 | pmid = 33705118 | pmc = 8391964 | doi = 10.1021/acs.chemrev.0c01111 }}</ref> MLFFs can achieve accuracy close to that of [[Ab initio quantum chemistry methods|ab initio methods]]. Once trained, MLFFs are much faster than direct quantum mechanical calculations. MLFFs address the limitations of traditional force fields by learning complex potential energy surfaces directly from high-level quantum mechanical data. Several software packages now support MLFFs, including [[Vienna Ab initio Simulation Package|VASP]]<ref name="Hafner_2008">{{cite journal | vauthors = Hafner J | title = Ab-initio simulations of materials using VASP: Density-functional theory and beyond | journal = Journal of Computational Chemistry | volume = 29 | issue = 13 | pages = 2044–78 | date = October 2008 | pmid = 18623101 | doi = 10.1002/jcc.21057 }}</ref> and open-source libraries like DeePMD-kit<ref name = "Wang_2018">{{cite journal | vauthors = Wang H, Zhang L, Han J, Weinan E |title=DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics |journal=Computer Physics Communications |date=July 2018 |volume=228 |pages=178–184 |doi=10.1016/j.cpc.2018.03.016|arxiv=1712.03641 }}</ref><ref name="Zeng_2023">{{cite journal | vauthors = Zeng J, Zhang D, Lu D, Mo P, Li Z, Chen Y, Rynik M, Huang L, Li Z, Shi S, Wang Y, Ye H, Tuo P, Yang J, Ding Y, Li Y, Tisi D, Zeng Q, Bao H, Xia Y, Huang J, Muraoka K, Wang Y, Chang J, Yuan F, Bore SL, Cai C, Lin Y, Wang B, Xu J, Zhu JX, Luo C, Zhang Y, Goodall RE, Liang W, Singh AK, Yao S, Zhang J, Wentzcovitch R, Han J, Liu J, Jia W, York DM, E W, Car R, Zhang L, Wang H | title = DeePMD-kit v2: A software package for deep potential models | journal = The Journal of Chemical Physics | volume = 159 | issue = 5 | pages = | date = August 2023 | pmid = 37526163 | pmc = 10445636 | doi = 10.1063/5.0155600 }}</ref> and [https://schnetpack.readthedocs.io/en/latest/ SchNetPack].<ref name="Schütt_2019">{{cite journal | vauthors = Schütt KT, Kessel P, Gastegger M, Nicoli KA, Tkatchenko A, Müller KR | title = SchNetPack: A Deep Learning Toolbox For Atomistic Systems | journal = Journal of Chemical Theory and Computation | volume = 15 | issue = 1 | pages = 448–455 | date = January 2019 | pmid = 30481453 | doi = 10.1021/acs.jctc.8b00908 | arxiv = 1809.01072 }}</ref><ref name="Schütt_2023">{{cite journal | vauthors = Schütt KT, Hessmann SS, Gebauer NW, Lederer J, Gastegger M | title = SchNetPack 2.0: A neural network toolbox for atomistic machine learning | journal = The Journal of Chemical Physics | volume = 158 | issue = 14 | pages = 144801 | date = April 2023 | pmid = | doi = 10.1063/5.0138367  | arxiv = 2212.05517 }}</ref>