Retinitis pigmentosa(RP)is a group of heredofamilial retinal diseases which is characterized by night blindness and progressive visual field loss.This study aims to study the treatment effect of Lycium barbarum(LB)on ...Retinitis pigmentosa(RP)is a group of heredofamilial retinal diseases which is characterized by night blindness and progressive visual field loss.This study aims to study the treatment effect of Lycium barbarum(LB)on retinal functions and structure of RP patients.The study is a double-masked randomized controlled trial.RP subjects received scheduled eye examination including visual acuity(VA),Humphrey field analysis(HFA),ganzfeld flash electroretinogram(ffERG)and optical coherence tomography(OCT).The suitable subjects were randomly allocated to either LB-treatment or placebo groups with the supply of LB or placebo for 12 months.There were total 41 RP subjects(22 in LB group and 19 in placebo group)completed the 12 months intervention.The compliance rates for LB and placebo groups were 89.8%±12.5%and 85.3%±7.7%respectively.As compared with placebo group,there were no deteriorations of both high and low contrast VA in LB group(P<0.01).In addition,certain improvements of scotopic rod response and photopic cone response of ffERG were obtained in LB group(P<0.05).In the OCT measurement,an obvious thinning of macular thickness was observed in placebo group but not found in LB group(P<0.05).However,there were no changes found in the sensitivity of central visual field between two groups.Our results confirm that the 12-month LB treatment for RP patients had neuroprotective effect on retina and is believed to delay or minimize the deterioration of visual function in RP.展开更多
We introduce an unsupervised machine learning(ML)based technique for the identification and characterization of microstructures in three-dimensional(3D)samples obtained from molecular dynamics simulations,particle tra...We introduce an unsupervised machine learning(ML)based technique for the identification and characterization of microstructures in three-dimensional(3D)samples obtained from molecular dynamics simulations,particle tracking data,or experiments.Our technique combines topology classification,image processing,and clustering algorithms,and can handle a wide range of microstructure types including grains in polycrystalline materials,voids in porous systems,and structures from self/directed assembly in soft-matter complex solutions.Our technique does not require a priori microstructure description of the target system and is insensitive to disorder such as extended defects in polycrystals arising from line and plane defects.We demonstrate quantitively that our technique provides unbiased microstructural information such as precise quantification of grains and their size distributions in 3D polycrystalline samples,characterizes features such as voids and porosity in 3D polymeric samples and micellar size distribution in 3D complex fluids.To demonstrate the efficacy of our ML approach,we benchmark it against a diverse set of synthetic data samples representing nanocrystalline metals,polymers and complex fluids as well as experimentally published characterization data.Our technique is computationally efficient and provides a way to quickly identify,track,and quantify complex microstructural features that impact the observed material behavior.展开更多
The problem of phase retrieval underlies various imaging methods from astronomy to nanoscale imaging.Traditional phase retrieval methods are iterative and are therefore computationally expensive.Deep learning(DL)model...The problem of phase retrieval underlies various imaging methods from astronomy to nanoscale imaging.Traditional phase retrieval methods are iterative and are therefore computationally expensive.Deep learning(DL)models have been developed to either provide learned priors or completely replace phase retrieval.However,such models require vast amounts of labeled data,which can only be obtained through simulation or performing computationally prohibitive phase retrieval on experimental datasets.Using 3D X-ray Bragg coherent diffraction imaging(BCDI)as a representative technique,we demonstrate AutoPhaseNN,a DL-based approach which learns to solve the phase problem without labeled data.By incorporating the imaging physics into the DL model during training,AutoPhaseNN learns to invert 3D BCDI data in a single shot without ever being shown real space images.Once trained,AutoPhaseNN can be effectively used in the 3D BCDI data inversion about 100×faster than iterative phase retrieval methods while providing comparable image quality.展开更多
We introduce Crystal Edge Graph Attention Neural Network (CEGANN) workflow that uses graph attention-based architecture tolearn unique feature representations and perform classification of materials across multiple sc...We introduce Crystal Edge Graph Attention Neural Network (CEGANN) workflow that uses graph attention-based architecture tolearn unique feature representations and perform classification of materials across multiple scales (from atomic to mesoscale) anddiverse classes ranging from metals, oxides, non-metals to hierarchical materials such as zeolites and semi-ordered mesophases.CEGANN can classify based on a global, structure-level representation such as space group and dimensionality (e.g., bulk, 2D,clusters, etc.). Using representative materials such as polycrystals and zeolites, we demonstrate its transferability in performing localatom-level classification tasks, such as grain boundary identification and other heterointerfaces. CEGANN classifies in (thermal)noisy dynamical environments as demonstrated for representative zeolite nucleation and growth from an amorphous mixture.Finally, we use CEGANN to classify multicomponent systems with thermal noise and compositional diversity. Overall, our approachis material agnostic and allows for multiscale feature classification ranging from atomic-scale crystals to heterointerfaces tomicroscale grain boundaries.展开更多
Upgrades to advanced scientific user facilities such as next-generation x-ray light sources,nanoscience centers,and neutron facilities are revolutionizing our understanding of materials across the spectrum of the phys...Upgrades to advanced scientific user facilities such as next-generation x-ray light sources,nanoscience centers,and neutron facilities are revolutionizing our understanding of materials across the spectrum of the physical sciences,from life sciences to microelectronics.However,these facility and instrument upgrades come with a significant increase in complexity.Driven by more exacting scientific needs,instruments and experiments become more intricate each year.This increased operational complexity makes it ever more challenging for domain scientists to design experiments that effectively leverage the capabilities of and operate on these advanced instruments.Large language models(LLMs)can perform complex information retrieval,assist in knowledge-intensive tasks across applications,and provide guidance on tool usage.Using x-ray light sources,leadership computing,and nanoscience centers as representative examples,we describe preliminary experiments with a Context-Aware Language Model for Science(CALMS)to assist scientists with instrument operations and complex experimentation.With the ability to retrieve relevant information from facility documentation,CALMS can answer simple questions on scientific capabilities and other operational procedures.With the ability to interface with software tools and experimental hardware,CALMS can conversationally operate scientific instruments.By making information more accessible and acting on user needs,LLMs could expand and diversify scientific facilities’users and accelerate scientific output.展开更多
Material properties share an intrinsic relationship with their structural attributes,making inverse design approaches crucial for discovering new materials with desired functionalities.Reinforcement Learning(RL)approa...Material properties share an intrinsic relationship with their structural attributes,making inverse design approaches crucial for discovering new materials with desired functionalities.Reinforcement Learning(RL)approaches are emerging as powerful inverse design tools,often functioning in discrete action spaces.This constrains their application in materials design problems,which involve continuous search spaces.Here,we introduce an RL-based framework CASTING(Continuous Action Space Tree Search for inverse design),that employs a decision tree-based Monte Carlo Tree Search(MCTS)algorithm with continuous space adaptation through modified policies and sampling.Using representative examples like Silver(Ag)for metals,Carbon(C)for covalent systems,and multicomponent systems such as graphane,boron nitride,and complex correlated oxides,we showcase its accuracy,convergence speed,and scalability in materials discovery and design.Furthermore,with the inverse design of super-hard Carbon phases,we demonstrate CASTING’s utility in discovering metastable phases tailored to user-defined target properties and preferences.展开更多
Silica is an abundant and technologically attractive material.Due to the structural complexities of silica polymorphs coupled with subtle differences in Si–O bonding characteristics,the development of accurate models...Silica is an abundant and technologically attractive material.Due to the structural complexities of silica polymorphs coupled with subtle differences in Si–O bonding characteristics,the development of accurate models to predict the structure,energetics and properties of silica polymorphs remain challenging.Current models for silica range from computationally efficient Buckingham formalisms(BKS,CHIK,Soules)to reactive(ReaxFF)and more recent machine-learned potentials that are flexible but computationally costly.Here,we introduce an improved formalism and parameterization of BKS model via a multireward reinforcement learning(RL)using an experimental training dataset.Our model concurrently captures the structure,energetics,density,equation of state,and elastic constants of quartz(equilibrium)as well as 20 other metastable silica polymorphs.We also assess its ability in capturing amorphous properties and highlight the limitations of the BKS-type functional forms in simultaneously capturing crystal and amorphous properties.We demonstrate ways to improve model flexibility and introduce a flexible formalism,machine-learned ML-BKS,that outperforms existing empirical models and is on-par with the recently developed 50 to 100 times more expensive Gaussian approximation potential(GAP)in capturing the experimental structure and properties of silica polymorphs and amorphous silica.展开更多
基金Health and Medical Research Fund(01121876)and PolyU Internal Grants(G-YBBS,G-YBGS,Z-0GF).
文摘Retinitis pigmentosa(RP)is a group of heredofamilial retinal diseases which is characterized by night blindness and progressive visual field loss.This study aims to study the treatment effect of Lycium barbarum(LB)on retinal functions and structure of RP patients.The study is a double-masked randomized controlled trial.RP subjects received scheduled eye examination including visual acuity(VA),Humphrey field analysis(HFA),ganzfeld flash electroretinogram(ffERG)and optical coherence tomography(OCT).The suitable subjects were randomly allocated to either LB-treatment or placebo groups with the supply of LB or placebo for 12 months.There were total 41 RP subjects(22 in LB group and 19 in placebo group)completed the 12 months intervention.The compliance rates for LB and placebo groups were 89.8%±12.5%and 85.3%±7.7%respectively.As compared with placebo group,there were no deteriorations of both high and low contrast VA in LB group(P<0.01).In addition,certain improvements of scotopic rod response and photopic cone response of ffERG were obtained in LB group(P<0.05).In the OCT measurement,an obvious thinning of macular thickness was observed in placebo group but not found in LB group(P<0.05).However,there were no changes found in the sensitivity of central visual field between two groups.Our results confirm that the 12-month LB treatment for RP patients had neuroprotective effect on retina and is believed to delay or minimize the deterioration of visual function in RP.
基金Use of the Center for Nanoscale Materials was supported by the U.S.Department of Energy,Office of Science,Office of Basic Energy Sciences,under Contract No.DEAC02-06CH11357This research used resources of the Argonne Leadership Computing Facility at Argonne National Laboratory,which is supported by the Office of Science of the U.S.Department of Energy under contract DE-AC02-06CH11357+1 种基金This research used resources of the National Energy Research Scientific Computing Centera DOE Office of Science User Facility supported by the Office of Science of the U.S.Department of Energy under Contract No.DEAC02-05CH11231.
文摘We introduce an unsupervised machine learning(ML)based technique for the identification and characterization of microstructures in three-dimensional(3D)samples obtained from molecular dynamics simulations,particle tracking data,or experiments.Our technique combines topology classification,image processing,and clustering algorithms,and can handle a wide range of microstructure types including grains in polycrystalline materials,voids in porous systems,and structures from self/directed assembly in soft-matter complex solutions.Our technique does not require a priori microstructure description of the target system and is insensitive to disorder such as extended defects in polycrystals arising from line and plane defects.We demonstrate quantitively that our technique provides unbiased microstructural information such as precise quantification of grains and their size distributions in 3D polycrystalline samples,characterizes features such as voids and porosity in 3D polymeric samples and micellar size distribution in 3D complex fluids.To demonstrate the efficacy of our ML approach,we benchmark it against a diverse set of synthetic data samples representing nanocrystalline metals,polymers and complex fluids as well as experimentally published characterization data.Our technique is computationally efficient and provides a way to quickly identify,track,and quantify complex microstructural features that impact the observed material behavior.
基金This work was performed,in part,at the Advanced Photon Source,a U.S.Department of Energy(DOE)Office of Science User Facility,operated for the DOE Office of Science by Argonne National Laboratory under Contract No.DE-AC02-06CH11357This research used resources of the Argonne Leadership Computing Facility,which is a DOE Office of Science User Facility supported under Contract DE-AC02-06CH11357+2 种基金This work was supported by the U.S.Department of Energy,Office of Science,Office of Basic Energy Sciences Data,Artificial Intelligence and Machine Learning at DOE Scientific User Facilities program under Award Number 34532M.J.C.acknowledges partial support from Argonne LDRD 2021-0090-AutoPtycho:Autonomous,Sparse-sampled Ptychographic ImagingY.Y.acknowledges partial support from Argonne LDRD 2021-0315-Scalable DL-based 3D X-ray nanoscale imaging enabled by AI accelerators.
文摘The problem of phase retrieval underlies various imaging methods from astronomy to nanoscale imaging.Traditional phase retrieval methods are iterative and are therefore computationally expensive.Deep learning(DL)models have been developed to either provide learned priors or completely replace phase retrieval.However,such models require vast amounts of labeled data,which can only be obtained through simulation or performing computationally prohibitive phase retrieval on experimental datasets.Using 3D X-ray Bragg coherent diffraction imaging(BCDI)as a representative technique,we demonstrate AutoPhaseNN,a DL-based approach which learns to solve the phase problem without labeled data.By incorporating the imaging physics into the DL model during training,AutoPhaseNN learns to invert 3D BCDI data in a single shot without ever being shown real space images.Once trained,AutoPhaseNN can be effectively used in the 3D BCDI data inversion about 100×faster than iterative phase retrieval methods while providing comparable image quality.
基金The authors acknowledge support from the US Department of Energy through BES award DE-SC0021201This material is based on work supported by the DOE,Office of Science,BES Data,Artificial Intelligence and Machine Learning at DOE Scientific User Facilities programme(MLExchange).Use of the Center for Nanoscale Materials,an Office of Science user facility,was supported by the US Department of Energy,Office of Science,Office of Basic Energy Sciences,under Contract No.DE-AC02-06CH11357+2 种基金This research also used resources from the Argonne Leadership Computing Facility at Argonne National Laboratory,which is supported by the Office of Science of the U.S.Department of Energy under contract DE-AC02-06CH11357This research used resources of the National Energy Research Scientific Computing Centera DOE Office of Science User Facility supported by the Office of Science of the U.S.Department of Energy under Contract No.DE-AC02-05CH11231.We gratefully acknowledge the computing resources provided via high-performance computing clusters operated by the Laboratory Computing Resource Center(LCRC)at Argonne National Laboratory.
文摘We introduce Crystal Edge Graph Attention Neural Network (CEGANN) workflow that uses graph attention-based architecture tolearn unique feature representations and perform classification of materials across multiple scales (from atomic to mesoscale) anddiverse classes ranging from metals, oxides, non-metals to hierarchical materials such as zeolites and semi-ordered mesophases.CEGANN can classify based on a global, structure-level representation such as space group and dimensionality (e.g., bulk, 2D,clusters, etc.). Using representative materials such as polycrystals and zeolites, we demonstrate its transferability in performing localatom-level classification tasks, such as grain boundary identification and other heterointerfaces. CEGANN classifies in (thermal)noisy dynamical environments as demonstrated for representative zeolite nucleation and growth from an amorphous mixture.Finally, we use CEGANN to classify multicomponent systems with thermal noise and compositional diversity. Overall, our approachis material agnostic and allows for multiscale feature classification ranging from atomic-scale crystals to heterointerfaces tomicroscale grain boundaries.
基金supported by the U.S.DOE,Office of Basic Energy Sciences,under Contract No.DE-AC02-06CH11357supported by the U.S.DOE Office of Science-Advanced Scientific Computing Research Program,under Contract No.DE-AC02-06CH11357.
文摘Upgrades to advanced scientific user facilities such as next-generation x-ray light sources,nanoscience centers,and neutron facilities are revolutionizing our understanding of materials across the spectrum of the physical sciences,from life sciences to microelectronics.However,these facility and instrument upgrades come with a significant increase in complexity.Driven by more exacting scientific needs,instruments and experiments become more intricate each year.This increased operational complexity makes it ever more challenging for domain scientists to design experiments that effectively leverage the capabilities of and operate on these advanced instruments.Large language models(LLMs)can perform complex information retrieval,assist in knowledge-intensive tasks across applications,and provide guidance on tool usage.Using x-ray light sources,leadership computing,and nanoscience centers as representative examples,we describe preliminary experiments with a Context-Aware Language Model for Science(CALMS)to assist scientists with instrument operations and complex experimentation.With the ability to retrieve relevant information from facility documentation,CALMS can answer simple questions on scientific capabilities and other operational procedures.With the ability to interface with software tools and experimental hardware,CALMS can conversationally operate scientific instruments.By making information more accessible and acting on user needs,LLMs could expand and diversify scientific facilities’users and accelerate scientific output.
基金This work performed at the Center for Nanoscale Materials,a U.S.Department of Energy Office of Science User Facility,was supported by the U.S.DOE,Office of Basic Energy Sciences,under Contract No.DE-AC02-06CH11357This material is based on work supported by the DOE,Office of Science,BES Data,Artificial Intelligence,and Machine Learning at DOE Scientific User Facilities program(ML-Exchange).S.K.R.S.would also like to acknowledge the support from the UIC faculty start-up fund.This research used resources of the National Energy Research Scientific Computing Center(NERSC),a US Department of Energy Office of Science User Facility located at Lawrence Berkeley National Laboratory,operated under Contract No.DE-AC02-05CH11231The authors(SKRS and TDL)would like to acknowledge the Air Force Office of Scientific Research(AFOSR)for funding this research under Award#FA9550-20-1-0332,with Dr.Chipping Li as the program manager.
文摘Material properties share an intrinsic relationship with their structural attributes,making inverse design approaches crucial for discovering new materials with desired functionalities.Reinforcement Learning(RL)approaches are emerging as powerful inverse design tools,often functioning in discrete action spaces.This constrains their application in materials design problems,which involve continuous search spaces.Here,we introduce an RL-based framework CASTING(Continuous Action Space Tree Search for inverse design),that employs a decision tree-based Monte Carlo Tree Search(MCTS)algorithm with continuous space adaptation through modified policies and sampling.Using representative examples like Silver(Ag)for metals,Carbon(C)for covalent systems,and multicomponent systems such as graphane,boron nitride,and complex correlated oxides,we showcase its accuracy,convergence speed,and scalability in materials discovery and design.Furthermore,with the inverse design of super-hard Carbon phases,we demonstrate CASTING’s utility in discovering metastable phases tailored to user-defined target properties and preferences.
基金This work was supported by the Department of Energy Basic Energy Sciences through CPIMS awards DE-SC0020201“Elucidating the formation mechanisms of zeolites using data-driven modeling and in-situ characterization”and DE-SC0023213“Elucidating the Mechanisms of Formation of Zeolites Using Data-Driven Modeling and Experimental Characterization”Work performed at the Center for Nanoscale Materials,a U.S.Department of Energy Office of Science User Facility,was supported by the U.S.DOE,Office of Basic Energy Sciences,under Contract No.DE-AC02-06CH11357This research used resources of the National Energy Research Scientific Computing Center(NERSC),a U.S.Department of Energy Office of Science User Facility located at Lawrence Berkeley National Laboratory,operated under Contract No.DE-AC02-05CH11231.
文摘Silica is an abundant and technologically attractive material.Due to the structural complexities of silica polymorphs coupled with subtle differences in Si–O bonding characteristics,the development of accurate models to predict the structure,energetics and properties of silica polymorphs remain challenging.Current models for silica range from computationally efficient Buckingham formalisms(BKS,CHIK,Soules)to reactive(ReaxFF)and more recent machine-learned potentials that are flexible but computationally costly.Here,we introduce an improved formalism and parameterization of BKS model via a multireward reinforcement learning(RL)using an experimental training dataset.Our model concurrently captures the structure,energetics,density,equation of state,and elastic constants of quartz(equilibrium)as well as 20 other metastable silica polymorphs.We also assess its ability in capturing amorphous properties and highlight the limitations of the BKS-type functional forms in simultaneously capturing crystal and amorphous properties.We demonstrate ways to improve model flexibility and introduce a flexible formalism,machine-learned ML-BKS,that outperforms existing empirical models and is on-par with the recently developed 50 to 100 times more expensive Gaussian approximation potential(GAP)in capturing the experimental structure and properties of silica polymorphs and amorphous silica.
