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您的检索式：作者名="Lisa Hamada"
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| 1 | AI powered, automated discovery of polymer membranes for carbon capture显示文摘The generation of molecules with artificial intelligence(AI)or,more specifically,machine learning(ML),is poised to revolutionize materials discovery.Potential applications range from development of potent drugs to efficient carbon capture and separation technologies.However,existing computational discovery frameworks for polymer membranes lack automated training data creation,generative design,and physical performance validation at meso-scale where complex properties of amorphous materials emerge.The methodological gaps are less relevant to the ML design of individual molecules such as the monomers which constitute the building blocks of polymers.Here,we report automated discovery of complex materials through inverse molecular design which is informed by meso-scale target features and process figures-of-merit.We have explored the multi-scale discovery regime by computationally generating and validating hundreds of polymer candidates designed for application in post-combustion carbon dioxide filtration.Specifically,we have validated each discovery step,from training dataset creation,via graph-based generative design of optimized monomer units,to molecular dynamics simulation of gas permeation through the polymer membranes.For the latter,we have devised a representative elementary volume(REV)enabling permeability simulations at about 1000×the volume of an individual,ML-generated monomer,obtaining quantitative agreement.The discovery-to-validation time per polymer candidate is on the order of 100 h using one CPU and one GPU,offering a computational screening alternative prior to lab validation. | Ronaldo Giro Hsianghan Hsu Akihiro Kishimoto Toshiyuki Hama Rodrigo F.Neumann Binquan Luan Seiji Takeda Lisa Hamada Mathias B.Steiner | 2023 | npj Computational Materials2023，，1： | 0 |
| 2 | Accelerating material design with the generative toolkit for scientific discovery显示文摘With the growing availability of data within various scientific domains,generative models hold enormous potential to accelerate scientific discovery.They harness powerful representations learned from datasets to speed up the formulation of novel hypotheses with the potential to impact material discovery broadly.We present the Generative Toolkit for Scientific Discovery(GT4SD).This extensible open-source library enables scientists,developers,and researchers to train and use state-of-the-art generative models to accelerate scientific discovery focused on organic material design. | Matteo Manica Jannis Born Joris Cadow Dimitrios Christofidellis Ashish Dave Dean Clarke Yves Gaetan Nana Teukam Giorgio Giannone Samuel C.Hoffman Matthew Buchan Vijil Chenthamarakshan Timothy Donovan Hsiang Han Hsu Federico Zipoli Oliver Schilter Akihiro Kishimoto Lisa Hamada Inkit Padhi Karl Wehden Lauren McHugh Alexy Khrabrov Payel Das Seiji Takeda John R.Smith | 2023 | npj Computational Materials2023，，1： | 0 |
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