»ã±¨±êÌâ (Title)£ºOrbital-free density functional theory for molecular systems using deep learning£¨½áºÏÉî¶È½ø½¨µÄÎÞ¹ì·Ãܶȷºº¯ÀíÂÛ·Ö×Óϵͳ×êÑУ©

»ã±¨ÈË (Speaker)£ºÁõ³©£¨Î¢Èí×êÑÐÔº¿ÆѧÖÇÄÜÖÐÐÄ£©

»ã±¨¹¦·ò (Time)£º2024Äê9ÔÂ20ÈÕ(ÖÜÎå)16:00

»ã±¨µØÖ· (Place):ÌÚѶ»áÒéÊÒ£º417-230-544

Ô¼ÇëÈË (Inviter)£ºÀîÓÀÀÖ ¸±½ÌÊÚ

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ÌáÒª (Abstract)£º

Calculating molecular properties is the cornerstone for many vital industry problems, including drug discovery and material design. These properties are commonly solved by Kohn-Sham density functional theory (KSDFT), which still has a large cost scaling, while more scalable alternatives like orbital-free DFT (OFDFT) suffer from accuracy issues, especially for non-periodic molecular systems. In this work, we propose M-OFDFT, an OFDFT implementation that achieves the same level of accuracy as KSDFT on molecules, while maintaining the lower cost scaling. M-OFDFT uses a deep learning model to better approximate the kinetic energy density functional, which is the key component for OFDFT. We build the essential non-locality into the model, which is made affordable by the concise density representation as expansion coefficients under an atomic basis. With techniques to address unconventional learning challenges, M-OFDFT achieves a comparable accuracy to KSDFT on a wide range of molecules untouched by OFDFT before. More attractively, M-OFDFT extrapolates well to molecules much larger than those seen in training, which unleashes the appealing scaling of OFDFT for studying large molecules, representing an advancement of the accuracy-efficiency trade-off frontier in electronic structure methods.