Traditional Chinese medicine(TCM)has shown remarkable potential for treating liver fibrosis.In this study,to identify novel TCMs with antifibrotic properties,we used a technique called high-throughput sequencing-based...Traditional Chinese medicine(TCM)has shown remarkable potential for treating liver fibrosis.In this study,to identify novel TCMs with antifibrotic properties,we used a technique called high-throughput sequencing-based high-throughput screening(HTS2),which is based on RNA-mediated oligonucleotide annealing,selection,and ligation followed by sequencing.This technology achieved parallel and quantitative analysis of gene expression in response to thousands of drug treatments[1].展开更多
In two-dimensional(2D)layer-stacked materials,the twist angle between layers provides extensive freedom to explore novel physics and engineer remarkable thermal transport properties.We discovered that the cross-plane ...In two-dimensional(2D)layer-stacked materials,the twist angle between layers provides extensive freedom to explore novel physics and engineer remarkable thermal transport properties.We discovered that the cross-plane thermal conductivity of multilayer graphene can be effectively controlled by arranging the layers with two specific twist angles in a defined sequence.Disorderly aperiodic twisted graphene layers lead to the localization of phonons,substantially reducing the cross-plane thermal transport via the interference of coherent phonons.Weemployed non-equilibrium molecular dynamics simulations combined with machine learning approach,to study heat transport in the two-angle disordered multilayer stacks,and identified within the constrained structural space the optimal stacking sequence that can minimize the cross-plane thermal conductivity.Compared to pristine graphite,the optimized structure can reduce thermal conductivity by up to 80%.Through analysis of phonon transport properties across different structures,we revealed the underlying physical mechanism of phonon localization.展开更多
Designing metallic glasses in silico is a major challenge in materials science given their disordered atomic structure and the vast compositional space to explore.Here,wetackle this challenge by finding optimal compos...Designing metallic glasses in silico is a major challenge in materials science given their disordered atomic structure and the vast compositional space to explore.Here,wetackle this challenge by finding optimal compositions for target mechanical properties.We apply Bayesian exploration for the CuZrAl composition,a paradigmatic metallic glass known for its good glass forming ability.We exploit an automated loop with an online database,a Bayesian optimization algorithm,and molecular dynamics simulations.From the ubiquitous 50/50 CuZr starting point,we map the composition landscape,changing the ratio of elements and adding aluminum,to characterize the yield stress and the shear modulus.This approach demonstrates with relatively modest effort that the system has an optimal composition window for the yield stress around aluminum concentration cAl=15%and zirconium concentration cZr=30%.We also explore several cooling rates(“process parameters”)and find that the best mechanical properties for a composition result from being most affected by the cooling procedure.Our Bayesian approach paves the novel way for the design of metallic glasses with“small data”,with an eye toward both future in silico design and experimental applications exploiting this toolbox.展开更多
文摘Traditional Chinese medicine(TCM)has shown remarkable potential for treating liver fibrosis.In this study,to identify novel TCMs with antifibrotic properties,we used a technique called high-throughput sequencing-based high-throughput screening(HTS2),which is based on RNA-mediated oligonucleotide annealing,selection,and ligation followed by sequencing.This technology achieved parallel and quantitative analysis of gene expression in response to thousands of drug treatments[1].
基金supported by National Key Research and Development Program of China(No.2023YFB4603801)National Natural Science Foundation of China(Nos.52176173,21FAA02809)+3 种基金Guangdong Innovative and Entrepreneurial Research Team Program(2021ZT09L227)Guangdong Basic and Applied Basic Research Foundation(Nos.2020A1515110192,2022A1515010710,2023B1515040023)Fundamental Research Funds for the Central Universities(22hytd08)The calculations reported were partially performed on resources provided by the Guangdong Provincial Key Laboratory of Magnetoelectric Physics and Devices(No.2022B1212010008).
文摘In two-dimensional(2D)layer-stacked materials,the twist angle between layers provides extensive freedom to explore novel physics and engineer remarkable thermal transport properties.We discovered that the cross-plane thermal conductivity of multilayer graphene can be effectively controlled by arranging the layers with two specific twist angles in a defined sequence.Disorderly aperiodic twisted graphene layers lead to the localization of phonons,substantially reducing the cross-plane thermal transport via the interference of coherent phonons.Weemployed non-equilibrium molecular dynamics simulations combined with machine learning approach,to study heat transport in the two-angle disordered multilayer stacks,and identified within the constrained structural space the optimal stacking sequence that can minimize the cross-plane thermal conductivity.Compared to pristine graphite,the optimized structure can reduce thermal conductivity by up to 80%.Through analysis of phonon transport properties across different structures,we revealed the underlying physical mechanism of phonon localization.
基金supported by the European Union Horizon 2020 research and innovation program under grant agreement no.857470 and from the European Regional Development Fund via the Foundation for Polish Science International Research Agenda PLUS program grant No.MAB PLUS/2018/8support from the Academy of Finland(361245 and 317464)+4 种基金from the Finnish Cultural Foundation.S.B.acknowledges support from the National Science Center in Poland through the SONATA BIS grant DEC-2023/50/E/ST3/00569from the Foundation for Polish Science in Poland through the FIRST TEAM FENG.02.02-IP.05-0177/23 projectsupport from the FinnCERES flagship(151830423)Business Finland(211835,211909,and 211989)Future Makers programs.The authors acknowledge the computational resources provided by the Aalto University School of Science“Science-IT”project.
文摘Designing metallic glasses in silico is a major challenge in materials science given their disordered atomic structure and the vast compositional space to explore.Here,wetackle this challenge by finding optimal compositions for target mechanical properties.We apply Bayesian exploration for the CuZrAl composition,a paradigmatic metallic glass known for its good glass forming ability.We exploit an automated loop with an online database,a Bayesian optimization algorithm,and molecular dynamics simulations.From the ubiquitous 50/50 CuZr starting point,we map the composition landscape,changing the ratio of elements and adding aluminum,to characterize the yield stress and the shear modulus.This approach demonstrates with relatively modest effort that the system has an optimal composition window for the yield stress around aluminum concentration cAl=15%and zirconium concentration cZr=30%.We also explore several cooling rates(“process parameters”)and find that the best mechanical properties for a composition result from being most affected by the cooling procedure.Our Bayesian approach paves the novel way for the design of metallic glasses with“small data”,with an eye toward both future in silico design and experimental applications exploiting this toolbox.
