An efficient and reliable automated model that can map physical Soil and Water Conservation（SWC） structures on cultivated land was developed using very high spatial resolution imagery obtained from Google Earth and ...An efficient and reliable automated model that can map physical Soil and Water Conservation（SWC） structures on cultivated land was developed using very high spatial resolution imagery obtained from Google Earth and Arc GIS？ERDAS IMAGINE？and SDC Morphology Toolbox for MATLAB and statistical techniques. The model was developed using the following procedures：（1） a high-pass spatial filter algorithm was applied to detect linear features,（2） morphological processing was used to remove unwanted linear features,（3） the raster format was vectorized,（4） the vectorized linear features were split per hectare（ha） and each line was then classified according to its compass directionand（5） the sum of all vector lengths per class of direction per ha was calculated. Finallythe direction class with the greatest length was selected from each ha to predict the physical SWC structures. The model was calibrated and validated on the Ethiopian Highlands. The model correctly mapped 80% of the existing structures. The developed model was then tested at different sites with different topography. The results show that the developed model is feasible for automated mapping of physical SWC structures. Thereforethe model is useful for predicting and mapping physical SWC structures areas across diverse areas.展开更多
We propose a strategy for generating unbiased and systematically extendable training data for machine learning interatomic potentials(MLIP)for multicomponent alloys,called Automated Small SYmmetric Structure Training ...We propose a strategy for generating unbiased and systematically extendable training data for machine learning interatomic potentials(MLIP)for multicomponent alloys,called Automated Small SYmmetric Structure Training or ASSYST.Based on exploring the full space of random crystal structures with space groups,it facilitates the construction of training sets for MLIPs in an automatic way without prior knowledge of the material in question.The advantages of this approach are that only cells consisting of few atoms(≈10)are needed for the DFT training set,and the size and completeness of the data set can be systematically controlled with very few parameters.We validate that potentials fitted this way can accurately describe a wide range of binary and ternary phases,random alloys,as well as point and extended defects,that have not been part of the training set.Finally,we estimate the binary phase diagrams with good experimental agreement.We demonstrate that the overall excellent performance is not a coincidence,but a consequence of the extensive sampling in phase space of ASSYST.Overall,this means that ASSYST will enable the largely autonomous generation of highquality DFT reference data and MLIPs.展开更多
基金covered by the Swiss National Centre of Competence in Research North-South (NCCR North-South) program
文摘An efficient and reliable automated model that can map physical Soil and Water Conservation（SWC） structures on cultivated land was developed using very high spatial resolution imagery obtained from Google Earth and Arc GIS？ERDAS IMAGINE？and SDC Morphology Toolbox for MATLAB and statistical techniques. The model was developed using the following procedures：（1） a high-pass spatial filter algorithm was applied to detect linear features,（2） morphological processing was used to remove unwanted linear features,（3） the raster format was vectorized,（4） the vectorized linear features were split per hectare（ha） and each line was then classified according to its compass directionand（5） the sum of all vector lengths per class of direction per ha was calculated. Finallythe direction class with the greatest length was selected from each ha to predict the physical SWC structures. The model was calibrated and validated on the Ethiopian Highlands. The model correctly mapped 80% of the existing structures. The developed model was then tested at different sites with different topography. The results show that the developed model is feasible for automated mapping of physical SWC structures. Thereforethe model is useful for predicting and mapping physical SWC structures areas across diverse areas.
基金funding from the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) through the Collaborative Research Center 1394 (SFB 1394, No. 409476157) and Project No. 405621160MP would like to thank Prince Matthews for setting up the hcp grain boundaries, Sarath Menon for providing support for CALPHY34Bengt Hallstedt for providing plots of the Mg/Ca and Al/Ca phase diagrams from his assessments, Chad Sinclair together with SFB 1394 for funding a research stay at UBC Vancouver where part of this work was conducted, as well Mira Todorova and Ali Tehranchi for fruitful discussions and Ralf Drautz for critical reading of the manuscript.
文摘We propose a strategy for generating unbiased and systematically extendable training data for machine learning interatomic potentials(MLIP)for multicomponent alloys,called Automated Small SYmmetric Structure Training or ASSYST.Based on exploring the full space of random crystal structures with space groups,it facilitates the construction of training sets for MLIPs in an automatic way without prior knowledge of the material in question.The advantages of this approach are that only cells consisting of few atoms(≈10)are needed for the DFT training set,and the size and completeness of the data set can be systematically controlled with very few parameters.We validate that potentials fitted this way can accurately describe a wide range of binary and ternary phases,random alloys,as well as point and extended defects,that have not been part of the training set.Finally,we estimate the binary phase diagrams with good experimental agreement.We demonstrate that the overall excellent performance is not a coincidence,but a consequence of the extensive sampling in phase space of ASSYST.Overall,this means that ASSYST will enable the largely autonomous generation of highquality DFT reference data and MLIPs.
