Machine-learned molecular mechanics force fields from large-scale quantum chemical data von Takaba, Kenichiro, Friedman, Anika J, Cavender, Chapin E, Behara, Pavan Kumar, Pulido, Iván, Henry, Michael M, MacDermott-Opeskin, Hugo, Iacovella, Christopher R, Nagle, Arnav M, Payne, Alexander Matthew, Shirts, Michael R, Mobley, David L, Chodera, John D, Wang, Yuanqing

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On the design space between molecular mechanics and machine learning force fields von Wang, Yuanqing, Takaba, Kenichiro, Chen, Michael S, Wieder, Marcus, Xu, Yuzhi, Zhu, Tong, Zhang, John Z. H, Nagle, Arnav, Yu, Kuang, Wang, Xinyan, Cole, Daniel J, Rackers, Joshua A, Cho, Kyunghyun, Greener, Joe G, Eastman, Peter, Martiniani, Stefano, Tuckerman, Mark E

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Machine-learned molecular mechanics force field for the simulation of protein-ligand systems and beyond von Takaba, Kenichiro, Pulido, Iván, Behara, Pavan Kumar, Cavender, Chapin E, Friedman, Anika J, Henry, Michael M, Hugo MacDermott Opeskin, Iacovella, Christopher R, Nagle, Arnav M, Payne, Alexander Matthew, Shirts, Michael R, Mobley, David L, Chodera, John D, Wang, Yuanqing

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On the design space between molecular mechanics and machine learning force fields von Wang, Yuanqing, Takaba, Kenichiro, Chen, Michael S, Wieder, Marcus, Xu, Yuzhi, Zhu, Tong, Zhang, John Z H, Nagle, Arnav, Yu, Kuang, Wang, Xinyan, Cole, Daniel J, Rackers, Joshua A, Cho, Kyunghyun, Greener, Joe G, Eastman, Peter, Martiniani, Stefano, Tuckerman, Mark E

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