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A flexible and scalable scheme for mixing computed formation energies from different levels of theory

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npj Computational Materials 2022年第1期8卷 1857-1867页

作者： Ryan S.Kingsbury Andrew S.Rosen Ayush S.Gupta Jason M.Munro Shyue Ping Ong Anubhav Jain Shyam Dwaraknath Matthew K.Horton Kristin A.Persson department of Materials Science and engineering University of California BerkeleyBerkeleyCA94720USA Energy Technologies Area Lawrence Berkeley National LaboratoryBerkeleyCA94720USA Materials Science Division Lawrence Berkeley National LaboratoryBerkeleyCA94720USA Miller Institute for Basic Research in Science University of CaliforniaBerkeleyCA94720USA University of California San Diego La JollaCA92093USA Molecular Foundry Lawrence Berkeley National LaboratoryBerkeleyCA94720USA

Computational materials discovery efforts are enabled by large databases of properties derived from high-throughput density functional theory(DFT),which now contain millions of calculations at the generalized gradient approximation(GGA)level of *** is now feasible to carry out high-throughput calculations using more accurate methods,such as meta-GGA DFT;however recomputing an entire database with a higher-fidelity method would not effectively leverage the enormous investment of computational resources embodied in existing(GGA)***,we propose here a general procedure by which higher-fidelity,low-coverage calculations(e.g.,meta-GGA calculations for selected chemical systems)can be combined with lower-fidelity,high-coverage calculations(e.g.,an existing database of GGA calculations)in a robust and scalable *** then use legacy PBE(+U)GGA calculations and new r2SCAN meta-GGA calculations from the Materials Project database to demonstrate that our scheme improves solid and aqueous phase stability predictions,and discuss practical considerations for its implementation.

关键词： theory scheme theory

Disentangling multiple scattering with deep learning:application to strain mapping from electron diffraction patterns

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npj Computational Materials 2022年第1期8卷 2419-2433页

作者： Joydeep Munshi Alexander Rakowski Benjamin H.Savitzky Steven E.Zeltmann Jim Ciston Matthew Henderson Shreyas Cholia Andrew M.Minor Maria K.Y.Chan Colin Ophus Center for Nanoscale Materials Argonne National LaboratoryArgonne DrWoodridge60517ILUSA national Center for Electron Microscopy Molecular FoundryLawrence Berkeley National Laboratory1 Cyclotron RoadBerkeley94720CAUSA department of Materials Science and engineering University of California BerkeleyBerkeley94720CAUSA Scientific Data Division Lawrence Berkeley National Laboratory1 Cyclotron RoadBerkeley94720CAUSA

A fast,robust pipeline for strain mapping of crystalline materials is important for many technological *** electron nanodiffraction allows us to calculate strain maps with high accuracy and spatial resolutions,but this technique is limited when the electron beam undergoes multiple ***-learning methods have the potential to invert these complex signals,but require a large number of training *** implement a Fourier space,complex-valued deep-neural network,FCU-Net,to invert highly nonlinear electron diffraction patterns into the corresponding quantitative structure factor ***-Net was trained using over 200,000 unique simulated dynamical diffraction patterns from different combinations of crystal structures,orientations,thicknesses,and microscope parameters,which are augmented with experimental *** evaluated FCU-Net against simulated and experimental datasets,where it substantially outperforms conventional analysis *** code,models,and training library are open-source and may be adapted to different diffraction measurement problems.

关键词： valued adapted crystalline

Study on operation strategy and load forecasting for distributed energy system based on Chinese supply-side power grid reform

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能源与人工环境(英文) 2022年第1期3卷 113-127页

作者： Canjun Li Zhengrong Li Han Zhu Zhe Tian Wei Feng School of mechanical engineering Tongji UniversityShanghaiChina School of Environmental Science and engineering Tianjin UniversityTianjinChina lawrence berkeley national laboratory BerkeleyCAUSA

This study focuses on the development and analysis of a real-time updated operations strategy of a distributed energy system(DES).Owing to the relevant Chinese policy of electrical transmission and distribution,combined cooling,heating,and power system(CCHP)and photovoltaic(PV)systems are not currently ***,with the Chinese supply-side power grid reform,the permissions for connections between DESs and utilities are gradually *** performing building simulation and using mixed integer linear programming(MILP),a real-time updated operation strategy of a DES is ***,considering the DES from Tianjin Eco-city as a case study,a comparative analysis between this updated strategy and the current operation strategy is performed by evaluating three factors:economic efficiency,energy consumption,and CO2 *** results show that the updated strategy can reduce 29.12%of electricity time-of-use cost,10.11%of total fuel consumption,and 18.40%of CO2 emission during the cooling ***,a method of“rolling load forecasting”for DES by using Support vector regression machine(SVR)is proposed and *** testing shows that the Mean Absolute Percentage Error(MAPE)is below 7.5%.And when the training sample is large,the particle swarm optimization algorithm can be used to shorten the modeling time of the air conditioning load forecasting model.

关键词： Distributed Energy System(DES) Mixed integer linear programming Energy efficiency,Operation strategy establishment

High-throughput predictions of metal-organic framework electronic properties:theoretical challenges,graph neural networks,and data exploration

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npj Computational Materials 2022年第1期8卷 1053-1062页

作者： Andrew S.Rosen Victor Fung Patrick Huck Cody T.O’Donnell Matthew K.Horton Donald G.Truhlar Kristin A.Persson Justin M.Notestein Randall Q.Snurr department of Materials Science and engineering University of CaliforniaBerkeleyCA94720USA Miller Institute for Basic Research in Science University of CaliforniaBerkeleyBerkeleyCA94720USA Materials Science Division Lawrence Berkeley National LaboratoryBerkeleyCA94720USA department of Chemical and Biological engineering Northwestern UniversityEvanstonIL60208USA Center for Nanophase Materials Sciences Oak Ridge National LaboratoryOak RidgeTN37831USA Energy Technologies Area Lawrence Berkeley National LaboratoryBerkeleyCA94720USA department of Chemistry Chemical Theory Centerand Minnesota Supercomputing InstituteUniversity of MinnesotaMinneapolisMN55455USA Molecular Foundry Lawrence Berkeley National LaboratoryBerkeleyCA94720USA

With the goal of accelerating the design and discovery of metal–organic frameworks(MOFs)for electronic,optoelectronic,and energy storage applications,we present a dataset of predicted electronic structure properties for thousands of MOFs carried out using multiple density functional *** to more accurate hybrid functionals,we find that the widely used PBE generalized gradient approximation(GGA)functional severely underpredicts MOF band gaps in a largely systematic manner for semi-conductors and insulators without magnetic ***,an even larger and less predictable disparity in the band gap prediction is present for MOFs with open-shell 3d transition metal *** regards to partial atomic charges,we find that different density functional approximations predict similar charges overall,although hybrid functionals tend to shift electron density away from the metal centers and onto the ligand environments compared to the GGA point of *** more significant differences in partial atomic charges are observed when comparing different charge partitioning *** conclude by using the dataset of computed MOF properties to train machine-learning models that can rapidly predict MOF band gaps for all four density functional approximations considered in this work,paving the way for future high-throughput screening *** encourage exploration and reuse of the theoretical calculations presented in this work,the curated data is made publicly available via an interactive and user-friendly web application on the Materials Project.

关键词： charges electronic centers

Modeling antiphase boundary energies of Ni_(3)Al-based alloys using automated density functional theory and machine learning

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npj Computational Materials 2022年第1期8卷 757-766页

作者： Enze Chen Artur Tamm Tao Wang Mario E.Epler Mark Asta Timofey Frolov department of Materials Science and engineering University of CaliforniaBerkeleyCA94720USA Materials Sciences Division Lawrence Berkeley National LaboratoryBerkeleyCA94720USA Materials Science Division Lawrence Livermore National LaboratoryLivermoreCA94550USA Carpenter Technology Corporation PhiladelphiaPA19103USA

Antiphase boundaries(APBs)are planar defects that play a critical role in strengthening Ni-based superalloys,and their sensitivity to alloy composition offers a flexible tuning parameter for alloy ***,we report a computational workflow to enable the development of sufficient data to train machine-learning(ML)models to automate the study of the effect of composition on the(111)APB energy in Ni_(3)Al-based *** employ ML to leverage this wealth of data and identify several physical properties that are used to build predictive models for the APB energy that achieve a cross-validation error of 0.033 J m^(−2).We demonstrate the transferability of these models by predicting APB energies in commercial ***,our use of physically motivated features such as the ordering energy and stoichiometry-based features opens the way to using existing materials properties databases to guide superalloy design strategies to maximize the APB energy.

关键词： Ni_(3)Al alloy superalloy

Quantifying spatiotemporal variability in occupant exposure to an indoor airborne contaminant with an uncertain source location

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Building Simulation 2023年第6期16卷 889-913页

作者： John E.Castellini Jr Cary A.Faulkner Wangda Zuo Michael D.Sohn department of mechanical engineering University of Colorado BoulderUCB 427BoulderCO80309USA department Architectural engineering The Pennsylvania State UniversityUniversity ParkPA16802USA national Renewable Energy national laboratory GoldenCO80401USA Energy Analysis and Environmental Impacts Division Lawrence Berkeley National Laboratory1 Cyclotron RoadBerkeleyCA94720USA

Well-mixed zone models are often employed to compute indoor air quality and occupant *** effective,a potential downside to assuming instantaneous,perfect mixing is underpredicting exposures to high intermittent concentrations within a *** such cases are of concern,more spatially resolved models,like computational-fluid dynamics methods,are used for some or all of the ***,these models have higher computational costs and require more input information.A preferred compromise would be to continue with a multi-zone modeling approach for all rooms,but with a better assessment of the spatial variability within a *** do so,we present a quantitative method for estimating a room’s spatiotemporal variability,based on influential room *** proposed method disaggregates variability into the variability in a room’s average concentration,and the spatial variability within the room relative to that *** enables a detailed assessment of how variability in particular room parameters impacts the uncertain occupant *** demonstrate the utility of this method,we simulate contaminant dispersion for a variety of possible source *** compute breathing-zone exposure during the releasing(source is active)and decaying(source is removed)*** CFD methods,we found after a 30 minutes release the average standard deviation in the spatial distribution of exposure was approximately 28%of the source average exposure,whereas variability in the different average exposures was lower,only 10%of the total *** also find that although uncertainty in the source location leads to variability in the average magnitude of transient exposure,it does not have a particularly large influence on the spatial distribution during the decaying period,or on the average contaminant removal *** systematically characterizing a room’s average concentration,its variability,and the spatial variability within the room important insights can be gain

关键词： uncertainty variability airborne contaminant spatiotemporal variability well-mixed heterogeneity

Validating neural networks for spectroscopic classification on a universal synthetic dataset

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npj Computational Materials 2023年第1期9卷 1325-1336页

作者： Jan Schuetzke Nathan J.Szymanski Markus Reischl Institute for Automation and Applied Informatics Karlsruhe Institute of TechnologyKarlsruhe 76131Germany department of Materials Science&engineering Lawrence Berkeley National LaboratoryBerkeleyCA 94720USA department of Materials Science&engineering UC BerkeleyBerkeleyCA 94720USA

To aid the development of machine learning models for automated spectroscopic data classification,we created a universal synthetic dataset for the validation of their *** dataset mimics the characteristic appearance of experimental measurements from techniques such as X-ray diffraction,nuclear magnetic resonance,and Raman spectroscopy among *** applied eight neural network architectures to classify artificial spectra,evaluating their ability to handle common experimental *** all models achieved over 98%accuracy on the synthetic dataset,misclassifications occurred when spectra had overlapping peaks or *** found that non-linear activation functions,specifically ReLU in the fully-connected layers,were crucial for distinguishing between these classes,while adding more sophisticated components,such as residual blocks or normalization layers,provided no performance *** on these findings,we summarize key design principles for neural networks in spectroscopic data classification and publicly share all scripts used in this study.

关键词： spectroscopic adding classify

Detecting lithium plating dynamics in a solid-state battery with operando X-ray computed tomography using machine learning

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npj Computational Materials 2023年第1期9卷 1402-1410页

作者： Ying Huang David Perlmutter Andrea Fei-Huei Su Jerome Quenum Pavel Shevchenko Dilworth Y.Parkinson Iryna V.Zenyuk Daniela Ushizima department of Materials Science&engineering University of California IrvineIrvineCaliforniaUSA national Fuel Cell Research Center University of California IrvineIrvineCaliforniaUSA Applied Math and Computational Research Division Lawrence Berkeley National LaboratoryBerkeleyCaliforniaUSA department of Chemical&Biomolecular engineering University of California IrvineIrvineCaliforniaUSA department of Electrical engineering and Computer Sciences College of EngineeringUniversity of California BerkeleyBerkeleyCaliforniaUSA Advanced Photon Source Argonne National Laboratory9700 South Cass AvenueLemontIllinoisUSA Advanced Light Source Lawrence Berkeley National LaboratoryBerkeleyCaliforniaUSA berkeley Institute for Data Science University of California BerkeleyBerkeleyCaliforniaUSA

Operando X-ray micro-computed tomography(µCT)provides an opportunity to observe the evolution of Li structures inside pouch *** is an essential step to quantitatively analyzingµCT datasets but is challenging to achieve on operando Li-metal battery datasets due to the low X-ray attenuation of the Li metal and the sheer size of the ***,we report a computational approach,batteryNET,to train an Iterative Residual U-Net-based network to detect Li *** resulting semantic segmentation shows singular Li-related component changes,addressing diverse morphologies in the *** addition,visualizations of the dead Li are provided,including calculations about the volume and effective thickness of electrodes,deposited Li,and redeposited *** also report discoveries about the spatial relationships between these *** approach focuses on a method for analyzing battery performance,which brings insight that significantly benefits future Li-metal battery design and a semantic segmentation transferrable to other datasets.

关键词： battery lithium state

Fast prediction of indoor airflow distribution inspired by synthetic image generation artificial intelligence

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Building Simulation 2023年第7期16卷 1219-1238页

作者： Cary A.Faulkner Dominik S.Jankowski John E.Castellini Jr Wangda Zuo Philipp Epple Michael D.Sohn Ali Taleb Zadeh Kasgari Walid Saad department of mechanical engineering University of Colorado BoulderBoulderCOUSA HySON Institut SonnebergGermany department of Architectural engineering Pennsylvania State UniversityUniversity ParkPAUSA department of mechanical engineering Coburg University of Applied SciencesCoburgGermany Energy Analysis and Environmental Impacts Division Lawrence Berkeley National LaboratoryBerkeleyCAUSA department of Electrical and Computer engineering Virginia TechArlingtonVAUSA

Prediction of indoor airflow distribution often relies on high-fidelity,computationally intensive computational fluid dynamics(CFD)*** intelligence(Al)models trained by CFD data can be used for fast and accurate prediction of indoor airflow,but current methods have limitations,such as only predicting limited outputs rather than the entire flow ***,conventional Al models are not always designed to predict different outputs based on a continuous input range,and instead make predictions for one or a few discrete *** work addresses these gaps using a conditional generative adversarial network(CGAN)model approach,which is inspired by current state-of-the-art Al for synthetic image *** create a new Boundary Condition CGAN(BC-CGAN)model by extending the original CGAN model to generate 2D airflow distribution images based on a continuous input parameter,such as a boundary ***,we design a novel feature-driven algorithm to strategically generate training data,with the goal of minimizing the amount of computationally expensive data while ensuring training quality of the Al *** BC-CGAN model is evaluated for two benchmark airflow cases:an isothermal lid-driven cavity flow and a non-isothermal mixed convection flow with a heated *** also investigate the performance of the BC-CGAN models when training is stopped based on different levels of validation error *** results show that the trained BC-CGAN model can predict the 2D distribution of velocity and temperature with less than 5%relative error and up to about 75,ooo times faster when compared to reference CFD *** proposed feature-driven algorithm shows potential for reducing the amount of data and epochs required to train the Al models while maintaining prediction accuracy,particularly when the flow changes non-linearlywith respectto an input.

关键词： artificial intelligence indoor airflow conditional generative adversarial network computational fluid dynamics