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Active learning for accelerated design of layered materials

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npj Computational Materials 2018年第1期4卷 54-62页

作者： Lindsay Bassman Pankaj Rajak Rajiv k.kalia Aiichiro Nakano Fei Sha Jifeng Sun David J.Singh Muratahan Aykol patrick.huck.k.istin persson Priya Vashishta Collaboratory for Advanced Computing and Simulations University of Southern CaliforniaLos AngelesCA 90089USA Department of Physics and Astronomy University of Southern CaliforniaLos AngelesCA 90089USA Department of Chemical Engineering and Material Science University of Southern CaliforniaLos AngelesCA 90089USA Department of Computer Science University of Southern CaliforniaLos AngelesCA 90089USA Department of Biological Sciences University of Southern CaliforniaLos AngelesCA 90089USA Department of Physics and Astronomy University of MissouriColumbiaMO 65211-7010USA Lawrence Berkeley National Laboratory 1 Cyclotron RoadBerkeleyCA 94720USA

Hetero-structures made from vertically stacked monolayers of transition metal dichalcogenides hold great potential for optoelectronic and thermoelectric *** of the optimal layered material for specific applications necessitates the estimation of key material properties,such as electronic band structure and thermal transport ***,screening of material properties via brute force ab initio calculations of the entire material structure space exceeds the limits of current computing ***,the functional dependence of material properties on the structures is often complicated,making simplistic statistical procedures for prediction difficult to employ without large amounts of data ***,we present a Gaussian process regression model,which predicts material properties of an input hetero-structure,as well as an active learning model based on Bayesian optimization,which can efficiently discover the optimal hetero-structure using a minimal number of ab initio *** electronic band gap,conduction/valence band dispersions,and thermoelectric performance are used as representative material properties for prediction and *** Materials Project platform is used for electronic structure computation,while the BoltzTraP code is used to compute thermoelectric *** optimization is shown to significantly reduce the computational cost of discovering the optimal structure when compared with finding an optimal structure by building a regression model to predict material *** models can be used for predictions with respect to any material property and our software,including data preparation code based on the Python Materials Genomics(PyMatGen)library as well as python-based machine learning code,is available open source.

关键词： Active Python python

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

Differential Regulation of Carbon Partitioning by the Central Growth Regulator Target of Rapamycin （TOR）

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Molecular Plant 2013年第6期6卷 1731-1733页

作者： Yi Zhang, Staffan persson patrick Giavalisco Max-Planck-Institute of Molecular Plant Physiology Am Muehlenberg 1 14476 Potsdam Germany

Target of rapamycin （TOR） is an evolutionary conserved ser- ine/threonine protein kinase found in yeast, plants, and ani- mals. TOR plays a central role in sensing nutrients and energy status, growth factors, and other environmental signals, and integrates these cues to synchronize cell growth-related pro- cesses （Laplante and Sabatini, 2012）. In yeast and animal cells, TOR acts as the catalytic component in two structurally and functionally distinct protein complexes termed TORC （TOR complex） 1 and TORC2. TOR seems to be localized specifi- cally to the endomembrane system and the nucleus with a certain mobility, depending on the environmental conditions （Laplante and Sabatini, 2012）. The three major components of TORCl, namely TOR, RAPTOR （Regulatory associated protein of mTOR）, and LST8 （Lethal with Sec13 protein 8）, are present in the genome of Arabidopsis, and interactions between TOR and RAPTOR, as well as TOR and LST8, occur also in plants, indicating the existence of a plant TORC1 （Moreau et al., 2012）. Here, we highlight the possible role of TOR in carbon utilization and growth regulation in Arabidopsis.

关键词： Target of Rapamycin (TOR) Arabidopsis carbon

WARA-PS:a research arena for public safety demonstrations and autonomous collaborative rescue robotics experimentation

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Autonomous Intelligent Systems 2021年第1期1卷 104-134页

作者： Olov Andersson patrick Doherty Mårten Lager Jens-Olof Lindh Linnea persson Elin A.Topp Jesper Tordenlid Bo Wahlberg Department of Computer and Information Science Linköping UniversityLinköpingSweden Saab kockums MalmöSweden Division of Decision and Control Systems School of Electrical Engineering and Computer ScienceKTH Royal Institute of TechnologyStockholmSweden Department of Computer Science Lund UniversityLundSweden Saab Combitech AB LinköpingSweden Division of Automatic Control Department of Electrical EngineeringLinköping UniversityLinköpingSweden

A research arena(WARA-PS)for sensing,data fusion,user interaction,planning and control of collaborative autonomous aerial and surface vehicles in public safety applications is *** objective is to demonstrate scientific discoveries and to generate new directions for future research on autonomous systems for societal *** enabler is a computational infrastructure with a core system architecture for industrial and academic *** includes a control and command system together with a framework for planning and executing tasks for unmanned surface vehicles and aerial *** motivating application for the demonstration is marine search and rescue operations.A state-of-art delegation framework for the mission planning together with three specific applications is also *** first one concerns model predictive control for cooperative rendezvous of autonomous unmanned aerial and surface *** second project is about learning to make safe real-time decisions under uncertainty for autonomous vehicles,and the third one is on robust terrain-aided navigation through sensor fusion and virtual reality tele-operation to support a GPS-free positioning system in marine *** research results have been experimentally evaluated and demonstrated to industry and public sector audiences at a marine test *** would be most difficult to do experiments on this large scale without the WARA-PS research ***,these demonstrator activities have resulted in effective research dissemination with high public visibility,business impact and new research collaborations between academia and industry.

关键词： Autonomous systems Intelligent system architectures Research demonstration arena Autonomous drones Autonomous marine vessels Public safety and security Collaborative robotics

Author Correction:Active learning for accelerated design of layered materials

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npj Computational Materials 2022年第1期8卷 2075-2075页

作者： Lindsay Bassman Oftelie Pankaj Rajak Rajiv kkalia Aiichiro Nakano Fei Sha Jifeng Sun David JSingh Muratahan Aykol patrick.huck.k.istin persson Priya Vashishta Collaboratory for Advanced Computing and Simulations University of Southern California Los Angeles CA 90089 USA Department of Physics and Astronomy University of Southern California Los Angeles CA 90089 USA Department of Chemical Engineering and Material Science University of Southern California Los Angeles CA 90089 USA Department of Computer Science University of Southern California Los Angeles CA 90089 USA Department of Biological Sciences University of Southern California Los Angeles CA 90089 USA Department of Physics and Astronomy University of Missouri Columbia MO 65211-7010 USA Lawrence Berkeley National Laboratory 1 Cyclotron Road Berkeley CA 94720 USA

Since the publication of this work,Lindsay Bassman Oftelie has changed their name from Lindsay *** has now been amended.

关键词： Active learning layered