The diagnostic methods for the profile of the radiation source were estab-lished at first based on the pinhole imaging principle. In this paper, the relationships among various parameters of the gamma-rays crammer suc...The diagnostic methods for the profile of the radiation source were estab-lished at first based on the pinhole imaging principle. In this paper, the relationships among various parameters of the gamma-rays crammer such as the modulation transfer function (MTF), the noise power spectrum (NPS), the signal-noise ratio (SNR) and the detective quantum efficiency (DQE) are developed and studied experimentally on the cobalt radiation source. The image diagnostic system is consisting with rays-fluorescence convertor (YAG crystal), optical imaging system, MCP image intensifier, CCD camera and other devices. The spatial resolution of the modulation transfer function (MTF) at 10% intensity was measured as 1 lp/mm by knife-edge method. The quantum of the measurement system is about 150 under weak radiation condition due to the single particle detection efficiency of the system. The dynamic range was inferred preliminarily as about 437. The required radiation intensity was calculated using the experiment result for the (SNR) = 1, 5, 10, respectively. The theoretical investigation results show that the radiation image with (SNR) = 1 can be only obtained when the pinhole diameter is 0.7 mm, object distance and image distance are both 200 cm, and the radiation intensity is about 1.0 × 1012 Sr-1·cm-2.展开更多
CVD growth of uniform conformal polycrystalline diamond (PCD) coatings over complex three dimensional structures is very important material processing technique. It has been found that the nucleation and subsequent gr...CVD growth of uniform conformal polycrystalline diamond (PCD) coatings over complex three dimensional structures is very important material processing technique. It has been found that the nucleation and subsequent growth period is very critical for successful development of CVD diamond based technologies. There are many methods of enhancing diamond nucleation on foreign substrates-ultrasonic treatment with diamond seed suspension being the best among them. A combination of ultrasonic seeding (US) technique with prior treatment (PT) of the substrate under CVD diamond growth conditions for brief period of time, has found to be very effective in enhancing the diamond nucleation during CVD growth—together they are known as NNP. But successive usage of the same seeding suspension up to ten cycles deteriorates the seeding efficiency. 6th seeding cycle onwards the silicon substrates are barely get covered by diamond crystallites. Five different diamond micron grits were used for seeding the silicon substrates and it is observed that US with the sub-micron particles (0.25 μm) is very effective in efficient nucleation of PCD on Si substrates. PT of the substrate somewhat negates the effect of successive use of the same seeding slurry but it is best to avoid recycling of the same seeding suspension using micron size diamond grits.展开更多
For neural network potentials(NNPs)to gain widespread use,researchers must be able to trust model outputs.However,the blackbox nature of neural networks and their inherent stochasticity are often deterrents,especially...For neural network potentials(NNPs)to gain widespread use,researchers must be able to trust model outputs.However,the blackbox nature of neural networks and their inherent stochasticity are often deterrents,especially for foundationmodels trained over broad swaths of chemical space.Uncertainty information provided at the time of prediction can help reduce aversion to NNPs.In this work,we detail two uncertainty quantification(UQ)methods.Readout ensembling,by finetuning the readout layers of an ensemble of foundation models,provides information about model uncertainty,while quantile regression,by replacing point predictions with distributional predictions,provides information about uncertainty within the underlying training data.We demonstrate our approach with the MACE-MP-0 model,applying UQ to the foundation model and a series of finetuned models.The uncertainties produced by the readout ensemble and quantile methods are demonstrated to be distinct measures by which the quality of the NNP output can be judged.展开更多
The discovery and optimization of high-energy materials(HEMs)face challenges due to the computational expense and slow iteration of traditional methods.Neural network potentials(NNPs)have emerged as an efficient alter...The discovery and optimization of high-energy materials(HEMs)face challenges due to the computational expense and slow iteration of traditional methods.Neural network potentials(NNPs)have emerged as an efficient alternative to first-principles simulations.This study presents EMFF-2025,a general NNP model for C,H,N,and O-based HEMs,leveraging transfer learning with minimal data from DFT calculations.The model achieves DFT-level accuracy,predicting the structure,mechanical properties,and decomposition characteristics of 20 HEMs.Integrating EMFF-2025 with PCA and correlation heatmaps,we map the chemical space and structural evolution of these HEMs across temperatures.Surprisingly,EMFF-2025 uncovers that most HEMs follow similar hightemperature decomposition mechanisms,challenging the conventional view of material-specific behavior.EMFF-2025 offers a versatile computational framework for accelerating HEM design and optimization.展开更多
We present large-scale molecular dynamics(MD)simulations based on a neural network potential(NNP)to investigate alkaline wet etching of GaN,a process critical to nitride-based semiconductor fabrication.A Behler–Parri...We present large-scale molecular dynamics(MD)simulations based on a neural network potential(NNP)to investigate alkaline wet etching of GaN,a process critical to nitride-based semiconductor fabrication.A Behler–Parrinello-type NNP is trained on extensive DFT datasets to capture chemical reactions between GaN and KOH.Using temperature-accelerated dynamics,our NNP-MD simulations accurately reproduce experimentally observed structural modifications of GaN nanorods during etching.The etching simulations reveal surface-specific morphological evolution:pyramidal pits on the−c plane,truncated pyramids on the+c plane,and planar morphologies on non-polar m and a surfaces.We also identify key chemical reactions governing the etching mechanisms.Enhanced-sampling simulations provide free-energy profiles for Ga dissolution,which critically influences the etching rate.The−c,a,and m planes exhibit moderate activation barriers,confirming their etchability,while the+c surface shows a significantly higher barrier,indicating strong resistance.Wealso observe the formation of Ga-O-Ga bridges on etched surfaces,which may act as carrier traps.This work provides atomistic insights into the mechanisms and kinetics of GaN wet etching,offering guidance for the fabrication of nanostructures in advanced GaN-based electronic and display applications.展开更多
文摘The diagnostic methods for the profile of the radiation source were estab-lished at first based on the pinhole imaging principle. In this paper, the relationships among various parameters of the gamma-rays crammer such as the modulation transfer function (MTF), the noise power spectrum (NPS), the signal-noise ratio (SNR) and the detective quantum efficiency (DQE) are developed and studied experimentally on the cobalt radiation source. The image diagnostic system is consisting with rays-fluorescence convertor (YAG crystal), optical imaging system, MCP image intensifier, CCD camera and other devices. The spatial resolution of the modulation transfer function (MTF) at 10% intensity was measured as 1 lp/mm by knife-edge method. The quantum of the measurement system is about 150 under weak radiation condition due to the single particle detection efficiency of the system. The dynamic range was inferred preliminarily as about 437. The required radiation intensity was calculated using the experiment result for the (SNR) = 1, 5, 10, respectively. The theoretical investigation results show that the radiation image with (SNR) = 1 can be only obtained when the pinhole diameter is 0.7 mm, object distance and image distance are both 200 cm, and the radiation intensity is about 1.0 × 1012 Sr-1·cm-2.
文摘CVD growth of uniform conformal polycrystalline diamond (PCD) coatings over complex three dimensional structures is very important material processing technique. It has been found that the nucleation and subsequent growth period is very critical for successful development of CVD diamond based technologies. There are many methods of enhancing diamond nucleation on foreign substrates-ultrasonic treatment with diamond seed suspension being the best among them. A combination of ultrasonic seeding (US) technique with prior treatment (PT) of the substrate under CVD diamond growth conditions for brief period of time, has found to be very effective in enhancing the diamond nucleation during CVD growth—together they are known as NNP. But successive usage of the same seeding suspension up to ten cycles deteriorates the seeding efficiency. 6th seeding cycle onwards the silicon substrates are barely get covered by diamond crystallites. Five different diamond micron grits were used for seeding the silicon substrates and it is observed that US with the sub-micron particles (0.25 μm) is very effective in efficient nucleation of PCD on Si substrates. PT of the substrate somewhat negates the effect of successive use of the same seeding slurry but it is best to avoid recycling of the same seeding suspension using micron size diamond grits.
基金supported by the"Transferring exascale computational chemistry to cloud computing environment and emerging hardware technologies(TEC4)"project,which is funded by the U.S.Department of Energy,Office of Science,Office of Basic Energy Sciences,the Division of Chemical Sciences,Geosciences,and Biosciences(under FWP 82037)supported by the U.S.Department of Energy(DOE),Office of Science,Office of Basic Energy Sciences,Division of Chemical Sciences,Geosciences&Biosciences(under FWP 47319)Pacific Northwest National Laboratory(PNNL)is a multiprogram national laboratory operated for the U.S.Department of Energy(DOE)by Battelle Memorial Institute under Contract No.DE-AC05-76RL0-1830.
文摘For neural network potentials(NNPs)to gain widespread use,researchers must be able to trust model outputs.However,the blackbox nature of neural networks and their inherent stochasticity are often deterrents,especially for foundationmodels trained over broad swaths of chemical space.Uncertainty information provided at the time of prediction can help reduce aversion to NNPs.In this work,we detail two uncertainty quantification(UQ)methods.Readout ensembling,by finetuning the readout layers of an ensemble of foundation models,provides information about model uncertainty,while quantile regression,by replacing point predictions with distributional predictions,provides information about uncertainty within the underlying training data.We demonstrate our approach with the MACE-MP-0 model,applying UQ to the foundation model and a series of finetuned models.The uncertainties produced by the readout ensemble and quantile methods are demonstrated to be distinct measures by which the quality of the NNP output can be judged.
基金supported by the National Natural Science Foundation ofChina(Grant 52106130)the State Key Laboratory of Explosion Science and Safety Protection(Grants QNKT23-15).
文摘The discovery and optimization of high-energy materials(HEMs)face challenges due to the computational expense and slow iteration of traditional methods.Neural network potentials(NNPs)have emerged as an efficient alternative to first-principles simulations.This study presents EMFF-2025,a general NNP model for C,H,N,and O-based HEMs,leveraging transfer learning with minimal data from DFT calculations.The model achieves DFT-level accuracy,predicting the structure,mechanical properties,and decomposition characteristics of 20 HEMs.Integrating EMFF-2025 with PCA and correlation heatmaps,we map the chemical space and structural evolution of these HEMs across temperatures.Surprisingly,EMFF-2025 uncovers that most HEMs follow similar hightemperature decomposition mechanisms,challenging the conventional view of material-specific behavior.EMFF-2025 offers a versatile computational framework for accelerating HEM design and optimization.
基金supported by Samsung Electronics Co., Ltd (IO201214-08143-01)the Nano & Material Technology Development Program through the National Research Foundation of Korea (NRF) funded by Ministry of Science and ICT (RS-2024-00407995). We are grateful to Jisu Jung for the technical discussion.
文摘We present large-scale molecular dynamics(MD)simulations based on a neural network potential(NNP)to investigate alkaline wet etching of GaN,a process critical to nitride-based semiconductor fabrication.A Behler–Parrinello-type NNP is trained on extensive DFT datasets to capture chemical reactions between GaN and KOH.Using temperature-accelerated dynamics,our NNP-MD simulations accurately reproduce experimentally observed structural modifications of GaN nanorods during etching.The etching simulations reveal surface-specific morphological evolution:pyramidal pits on the−c plane,truncated pyramids on the+c plane,and planar morphologies on non-polar m and a surfaces.We also identify key chemical reactions governing the etching mechanisms.Enhanced-sampling simulations provide free-energy profiles for Ga dissolution,which critically influences the etching rate.The−c,a,and m planes exhibit moderate activation barriers,confirming their etchability,while the+c surface shows a significantly higher barrier,indicating strong resistance.Wealso observe the formation of Ga-O-Ga bridges on etched surfaces,which may act as carrier traps.This work provides atomistic insights into the mechanisms and kinetics of GaN wet etching,offering guidance for the fabrication of nanostructures in advanced GaN-based electronic and display applications.
