Developing materials with ultrahigh thermal conductivity is crucial for thermal management and energy conversion.The recent development of generative models and machine learning(ML)holds great promise for predicting n...Developing materials with ultrahigh thermal conductivity is crucial for thermal management and energy conversion.The recent development of generative models and machine learning(ML)holds great promise for predicting new functional materials.However,these data-driven methods are not tailored to identifying energetically stable structures and accurately predicting their thermal properties,as they lack physical constraints and information about the complexity of atomic many-body interactions.Here,we show how combining deep generative models of crystal structures with quantum-accurate,fast ML interatomic potentials can accelerate the prediction of materials with ultrahigh lattice thermal conductivity while ensuring energy optimality.We exploit structural symmetry and similarity metrics derived from atomic coordination environments to enable fast exploration of the structural space produced by the generative model.Additionally,we propose an active-learning-based protocol for the on-the-fly training of ML potentials to achieve high-fidelity predictions of stability and lattice thermal conductivity in prospective materials.Applying this method to carbon materials,we screen 100,000 candidates and identify 34 carbon polymorphs,approximately a quarter of which had not been previously predicted,to have lattice thermal conductivity above 800 W m^(−1)K^(−1),reaching up to 2,400 W m^(−1)K^(−1)aside from diamond.These findings provide a viable pathway toward the ML-assisted prediction of periodic materials with exceptional thermal properties.展开更多
With aperture synthesis(AS)technique,a number of small antennas can be assembled to form a large telescope whose spatial resolution is determined by the distance of two farthest antennas instead of the diameter of a s...With aperture synthesis(AS)technique,a number of small antennas can be assembled to form a large telescope whose spatial resolution is determined by the distance of two farthest antennas instead of the diameter of a single-dish antenna.In contrast from a direct imaging system,an AS telescope captures the Fourier coefficients of a spatial object,and then implement inverse Fourier transform to reconstruct the spatial image.Due to the limited number of antennas,the Fourier coefficients are extremely sparse in practice,resulting in a very blurry image.To remove/reduce blur,“CLEAN”deconvolution has been widely used in the literature.However,it was initially designed for a point source.For an extended source,like the Sun,its efficiency is unsatisfactory.In this study,a deep neural network,referring to Generative Adversarial Network(GAN),is proposed for solar image deconvolution.The experimental results demonstrate that the proposed model is markedly better than traditional CLEAN on solar images.The main purpose of this work is visual inspection instead of quantitative scientific computation.We believe that this will also help scientists to better understand solar phenomena with high quality images.展开更多
基金supported by the National Natural Science Foundation of China(Nos.52425601,52250273,52327809,U20A20301,and 82361138571)National Key Research and Development Program of China(No.2023YFB4404104)Beijing Natural Science Foundation(No.L233022).
文摘Developing materials with ultrahigh thermal conductivity is crucial for thermal management and energy conversion.The recent development of generative models and machine learning(ML)holds great promise for predicting new functional materials.However,these data-driven methods are not tailored to identifying energetically stable structures and accurately predicting their thermal properties,as they lack physical constraints and information about the complexity of atomic many-body interactions.Here,we show how combining deep generative models of crystal structures with quantum-accurate,fast ML interatomic potentials can accelerate the prediction of materials with ultrahigh lattice thermal conductivity while ensuring energy optimality.We exploit structural symmetry and similarity metrics derived from atomic coordination environments to enable fast exploration of the structural space produced by the generative model.Additionally,we propose an active-learning-based protocol for the on-the-fly training of ML potentials to achieve high-fidelity predictions of stability and lattice thermal conductivity in prospective materials.Applying this method to carbon materials,we screen 100,000 candidates and identify 34 carbon polymorphs,approximately a quarter of which had not been previously predicted,to have lattice thermal conductivity above 800 W m^(−1)K^(−1),reaching up to 2,400 W m^(−1)K^(−1)aside from diamond.These findings provide a viable pathway toward the ML-assisted prediction of periodic materials with exceptional thermal properties.
基金the National Natural Science Foundation of China(NSFC)(Grant Nos.61572461,61811530282,61872429,11790301 and 11790305).
文摘With aperture synthesis(AS)technique,a number of small antennas can be assembled to form a large telescope whose spatial resolution is determined by the distance of two farthest antennas instead of the diameter of a single-dish antenna.In contrast from a direct imaging system,an AS telescope captures the Fourier coefficients of a spatial object,and then implement inverse Fourier transform to reconstruct the spatial image.Due to the limited number of antennas,the Fourier coefficients are extremely sparse in practice,resulting in a very blurry image.To remove/reduce blur,“CLEAN”deconvolution has been widely used in the literature.However,it was initially designed for a point source.For an extended source,like the Sun,its efficiency is unsatisfactory.In this study,a deep neural network,referring to Generative Adversarial Network(GAN),is proposed for solar image deconvolution.The experimental results demonstrate that the proposed model is markedly better than traditional CLEAN on solar images.The main purpose of this work is visual inspection instead of quantitative scientific computation.We believe that this will also help scientists to better understand solar phenomena with high quality images.
