We present a differentiation framework for plane-wave density-functional theory(DFT)that combines the strengths of forward-mode algorithmic differentiation(AD)and density-functional perturbation theory(DFPT).In the re...We present a differentiation framework for plane-wave density-functional theory(DFT)that combines the strengths of forward-mode algorithmic differentiation(AD)and density-functional perturbation theory(DFPT).In the resulting AD-DFPT framework derivatives of any DFT output quantity with respect to any input parameter(e.g.,geometry,density functional or pseudopotential)can be computed accurately without deriving gradient expressions by hand.We implement AD-DFPT into the Density-Functional ToolKit(DFTK)and show its broad applicability.Amongst others we consider the inverse design of a semiconductor band gap,the learning of exchange-correlation functional parameters,or the propagation of DFT parameter uncertainties to relaxed structures.These examples demonstrate a number of promising research avenues opened by gradient-driven workflows in first-principles materials modeling.展开更多
Traditional hyperspectral imaging(HI)systems are constrained by a limited depth of field(DoF),necessitating refocusing for any out-of-focus objects.This requirement not only slows down the imaging speed but also compl...Traditional hyperspectral imaging(HI)systems are constrained by a limited depth of field(DoF),necessitating refocusing for any out-of-focus objects.This requirement not only slows down the imaging speed but also complicates the system architecture.It is challenging to trade off among speed,resolution,and DoF within an ultrasimple system.While some studies have reported advancements in extending DoF,the improvements remain insufficient.To address this challenge,we propose a novel,to our knowledge,differentiable framework that integrates an extended DoF(E-DoF)wave propagation model and an achromatic hyperspectral reconstructor powered by deep learning.Through rigorous experimental validation,we have demonstrated that the compact HI system is capable of snapshot capturing of high-fidelity images with an exceptional DoF reaching approximately 5 m,marking a significant improvement of over three orders of magnitude.Additionally,the system achieves over 90%spectral accuracy without aberration,nearly doubling the accuracy performance of existing methods.An asymmetric freeform surface design is introduced for diffractive optical elements,enabling dual functionality with design freedom and E-DoF.The sparse prior conditions for spatial texture and spectral features of hyperspectral cubic data are integrated into the reconstruction network,effectively mitigating texture blurring and chromatic aberration.It foresees that the optimal strategy for achromatic E-DoF can be adopted into other optical systems such as polarization imaging and depth measurement.展开更多
Machine learning(ML)surrogate models offer a promising route to accelerate material property prediction,bypassing costly atomistic simulations.Here,we introduce IsothermODE,a neural ordinary differential equation(NODE...Machine learning(ML)surrogate models offer a promising route to accelerate material property prediction,bypassing costly atomistic simulations.Here,we introduce IsothermODE,a neural ordinary differential equation(NODE)framework for reconstructing full uptake and heat of adsorption(ΔH_(ads))isotherms for CO_(2)adsorption in metal-organic frameworks(MOFs)using only sparse pressure data.Unlike traditional ML models,IsothermODE leverages the intrinsic structure of differential equations to produce smooth,physically-consistent predictions that generalize across wide pressure ranges.We demonstrate high-fidelity interpolation and extrapolation,even with only five pressure points.To address the stochasticity inherent in Grand Canonical Monte Carlo(GCMC)simulations,we integrate uncertainty quantification,yielding tight bounds on predicted enthalpy curves.We further interpret the learned latent dynamics in terms of adsorption thermodynamics and textural properties,offering insight into structure-property relationships.Finally,we demonstrate IsothermODE’s long-range interpolation and extrapolation capabilities with sparse isotherm data(5 pressure points)and large incomplete intervals featuring missing data between 4–40(case 1)and 25–50(case 2)bars.IsothermODE provides a fast,robust alternative to simulation-heavy workflows,enabling scalable screening and design of next-generation carbon capture materials.展开更多
基金supported by the Swiss National Science Foundation (SNSF, Grant No. 221186)the NCCR MARVEL, a National Centre of Competence in Research, funded by the SNSF (Grant No. 205602). The funder played no role in study design, data collection, analysis and interpretation of data, or the writing of this manuscript. Fruitful discussions with Andrea Azzali, Gaspard Kemlin, Antoine Levitt, Uwe Naumann, Étienne Polack, and Markus Towara on the technical aspects of our AD-DFPT implementation and with Giovanni Pizzi regarding the pseudopotential training example are gratefully acknowledged.
文摘We present a differentiation framework for plane-wave density-functional theory(DFT)that combines the strengths of forward-mode algorithmic differentiation(AD)and density-functional perturbation theory(DFPT).In the resulting AD-DFPT framework derivatives of any DFT output quantity with respect to any input parameter(e.g.,geometry,density functional or pseudopotential)can be computed accurately without deriving gradient expressions by hand.We implement AD-DFPT into the Density-Functional ToolKit(DFTK)and show its broad applicability.Amongst others we consider the inverse design of a semiconductor band gap,the learning of exchange-correlation functional parameters,or the propagation of DFT parameter uncertainties to relaxed structures.These examples demonstrate a number of promising research avenues opened by gradient-driven workflows in first-principles materials modeling.
基金Youth Innovation Promotion Association of the Chinese Academy of Sciences(2022246)Shanghai Sailing Program(22YF1454800,20YF145480)。
文摘Traditional hyperspectral imaging(HI)systems are constrained by a limited depth of field(DoF),necessitating refocusing for any out-of-focus objects.This requirement not only slows down the imaging speed but also complicates the system architecture.It is challenging to trade off among speed,resolution,and DoF within an ultrasimple system.While some studies have reported advancements in extending DoF,the improvements remain insufficient.To address this challenge,we propose a novel,to our knowledge,differentiable framework that integrates an extended DoF(E-DoF)wave propagation model and an achromatic hyperspectral reconstructor powered by deep learning.Through rigorous experimental validation,we have demonstrated that the compact HI system is capable of snapshot capturing of high-fidelity images with an exceptional DoF reaching approximately 5 m,marking a significant improvement of over three orders of magnitude.Additionally,the system achieves over 90%spectral accuracy without aberration,nearly doubling the accuracy performance of existing methods.An asymmetric freeform surface design is introduced for diffractive optical elements,enabling dual functionality with design freedom and E-DoF.The sparse prior conditions for spatial texture and spectral features of hyperspectral cubic data are integrated into the reconstruction network,effectively mitigating texture blurring and chromatic aberration.It foresees that the optimal strategy for achromatic E-DoF can be adopted into other optical systems such as polarization imaging and depth measurement.
基金the Engaging OnDemand clusters at MIT Office of Research Computing and Data (ORCD) and MIT SuperCloud. This work additionally used Bridges-2 at Pittsburgh Supercomputing Center (PSC) through allocation MCH230021 from the Advanced Cyberinfrastructure Coordination Ecosystem: Services & Support (ACCESS) program, which is supported by National Science Foundation Grants No. 2138259, 2138286, 2138307, 2137603, and 2138296E.L. is supported by the National Science Foundation Graduate Research Fellowship under Grant No. 2141064.
文摘Machine learning(ML)surrogate models offer a promising route to accelerate material property prediction,bypassing costly atomistic simulations.Here,we introduce IsothermODE,a neural ordinary differential equation(NODE)framework for reconstructing full uptake and heat of adsorption(ΔH_(ads))isotherms for CO_(2)adsorption in metal-organic frameworks(MOFs)using only sparse pressure data.Unlike traditional ML models,IsothermODE leverages the intrinsic structure of differential equations to produce smooth,physically-consistent predictions that generalize across wide pressure ranges.We demonstrate high-fidelity interpolation and extrapolation,even with only five pressure points.To address the stochasticity inherent in Grand Canonical Monte Carlo(GCMC)simulations,we integrate uncertainty quantification,yielding tight bounds on predicted enthalpy curves.We further interpret the learned latent dynamics in terms of adsorption thermodynamics and textural properties,offering insight into structure-property relationships.Finally,we demonstrate IsothermODE’s long-range interpolation and extrapolation capabilities with sparse isotherm data(5 pressure points)and large incomplete intervals featuring missing data between 4–40(case 1)and 25–50(case 2)bars.IsothermODE provides a fast,robust alternative to simulation-heavy workflows,enabling scalable screening and design of next-generation carbon capture materials.
