»ã±¨±êÌâ (Title)£ºQuantum Chemistry Meets Machine Learning: Autonomous Computational Workflow for Chemical Discovery£¨Á¿×Ó»¯Ñ§¸ÏÉÏ»úе½ø½¨£º»¯Ñ§·¢ÏÖµÄ×ÔÖ÷ÍÆË㹤×÷Á÷³Ì£©

»ã±¨ÈË (Speaker)£º¶Î³½È壨ÃÀ¹úÂéÊ¡Àí¹¤Ñ§Ôº£©

»ã±¨¹¦·ò (Time)£º2022Äê11ÔÂ8ÈÕ (Öܶþ) 9:30-11:30

»ã±¨µØÖ· (Place)£ºÌÚѶ»áÒé £¨»áÒéºÅ£º245-990-876£©

Ô¼ÇëÈË(Inviter)£ºÁõºÃºº ½ÌÊÚ

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»ã±¨ÌáÒª£ºAutomation has long been revolutionizing our modern society since the first industrial revolution and has the potential to provide sufficient productivity forces for revolution is ongoing in computational sciences. Quantum chemistry software and modern computers have developed to a stage where virtual high throughput screening (VHTS), i.e., running thousands of calculations in parallel, becomes possible. This provides great opportunities for developing automated workflows to utilize the increasing computing power to generate large-scale data sets. Together with machine learning (ML) models trained on these data sets as either surrogate function approximations or generative models, accelerated chemical discovery for functional molecules and materials are achieved. Current automation workflows, however, are far from perfect. Namely, they produce too many unfruitful results and suffer severely from method selection bias, especially on challenging chemical spaces such as transition metal chemistry. These problems limit the automated workflows from providing efficiency and accuracy needed for chemical discovery. In this seminar, we introduce intelligent ML-based decision-making models in automation workflows and showcase the potential of these ¡°smart¡± computational building blocks to be keys to autonomous chemical discovery.