|
|
|
题名
|
作者
|
年代
|
出处
|
被引量
|
| 1 | A general-purpose machine learning framework for predicting properties of inorganic materials显示文摘A very active area of materials research is to devise methods that use machine learning to automatically extract predictive models from existing materials data.While prior examples have demonstrated successful models for some applications,many more applications exist where machine learning can make a strong impact.To enable faster development of machine-learning-based models for such applications,we have created a framework capable of being applied to a broad range of materials data.Our method works by using a chemically diverse list of attributes,which we demonstrate are suitable for describing a wide variety of properties,and a novel method for partitioning the data set into groups of similar materials to boost the predictive accuracy.In this manuscript,we demonstrate how this new method can be used to predict diverse properties of crystalline and amorphous materials,such as band gap energy and glass-forming ability. | Logan Ward Ankit Agrawal Alok Choudhary Christopher Wolverton | 2016 | npj Computational Materials2016，，1： | 83 |
| 2 | The Open Quantum Materials Database (OQMD): assessing the accuracy of DFT formation energies显示文摘The Open Quantum Materials Database(OQMD)is a high-throughput database currently consisting of nearly 300,000 density functional theory(DFT)total energy calculations of compounds from the Inorganic Crystal Structure Database(ICSD)and decorations of commonly occurring crystal structures.To maximise the impact of these data,the entire database is being made available,without restrictions,at www.oqmd.org/download.In this paper,we outline the structure and contents of the database,and then use it to evaluate the accuracy of the calculations therein by comparing DFT predictions with experimental measurements for the stability of all elemental ground-state structures and 1,670 experimental formation energies of compounds.This represents the largest comparison between DFT and experimental formation energies to date.The apparent mean absolute error between experimental measurements and our calculations is 0.096 eV/atom.In order to estimate how much error to attribute to the DFT calculations,we also examine deviation between different experimental measurements themselves where multiple sources are available,and find a surprisingly large mean absolute error of 0.082 eV/atom.Hence,we suggest that a significant fraction of the error between DFT and experimental formation energies may be attributed to experimental uncertainties.Finally,we evaluate the stability of compounds in the OQMD(including compounds obtained from the ICSD as well as hypothetical structures),which allows us to predict the existence of~3,200 new compounds that have not been experimentally characterised and uncover trends in material discovery,based on historical data available within the ICSD. | Scott Kirklin James E Saal Bryce Meredig Alex Thompson Jeff W Doak Muratahan Aykol Stephan Rühl Chris Wolverton | 2015 | npj Computational Materials2015，，1： | 62 |
| 3 | Recent advances and applications of deep learning methods in materials science显示文摘Deep learning(DL)is one of the fastest-growing topics in materials data science,with rapidly emerging applications spanning atomistic,image-based,spectral,and textual data modalities.DL allows analysis of unstructured data and automated identification of features.The recent development of large materials databases has fueled the application of DL methods in atomistic prediction in particular.In contrast,advances in image and spectral data have largely leveraged synthetic data enabled by high-quality forward models as well as by generative unsupervised DL methods.In this article,we present a high-level overview of deep learning methods followed by a detailed discussion of recent developments of deep learning in atomistic simulation,materials imaging,spectral analysis,and natural language processing.For each modality we discuss applications involving both theoretical and experimental data,typical modeling approaches with their strengths and limitations,and relevant publicly available software and datasets.We conclude the review with a discussion of recent cross-cutting work related to uncertainty quantification in this field and a brief perspective on limitations,challenges,and potential growth areas for DL methods in materials science. | Kamal Choudhary Brian DeCost Chi Chen Anubhav Jain Francesca Tavazza Ryan Cohn Cheol Woo Park Alok Choudhary Ankit Agrawal Simon J.L.Billinge Elizabeth Holm Shyue Ping Ong Chris Wolverton | 2022 | npj Computational Materials2022，，1： | 9 |
| 4 | Accurate and scalable graph neural network force field and molecular dynamics with direct force architecture显示文摘Recently,machine learning(ML)has been used to address the computational cost that has been limiting ab initio molecular dynamics(AIMD).Here,we present GNNFF,a graph neural network framework to directly predict atomic forces from automatically extracted features of the local atomic environment that are translationally-invariant,but rotationally-covariant to the coordinate of the atoms.We demonstrate that GNNFF not only achieves high performance in terms of force prediction accuracy and computational speed on various materials systems,but also accurately predicts the forces of a large MD system after being trained on forces obtained from a smaller system.Finally,we use our framework to perform an MD simulation of Li7P3S11,a superionic conductor,and show that resulting Li diffusion coefficient is within 14%of that obtained directly from AIMD.The high performance exhibited by GNNFF can be easily generalized to study atomistic level dynamics of other material systems. | Cheol Woo Park Mordechai Kornbluth Jonathan Vandermause Chris Wolverton Boris Kozinsky Jonathan P.Mailoa | 2021 | npj Computational Materials2021，，1： | 2 |
| 5 | Hydrogen storage in calcium alanate: first-principles thermodynamics and crystal structures 显示文摘 | WOLVERTON C OZOLINS V | 2007 | Phys Rev B2007，75，： | 1 |
| 6 | Water hyacinth (Eichhornia crassipes) productivity and harvesting studies 显示文摘 | Wolverton B C McDonald R C | 1979 | Econ Bot1979，33，： | 1 |
| 7 | Multiscale modeling of θ'precipitation in A1-Cu binary alloys显示文摘 | Vaithyanathan V Wolverton C Chen L Q | 2004 | Acta Materialia2004，10，52： | 1 |
| 8 | Thermodynamic Stability of Mg-based Ternary Long-Period Stacking Ordered Stmctttres 显示文摘 | Saal J E Wolverton C | 2013 | Acta Materialia2013，13，： | 1 |
| 9 | Bioaccumulation and Detection of Trace Levels of Cadium in Aquatic Systems by Eichhornia cras-sipes显示文摘 | WOLVERTON B C MCDONALD R C | 1978 | Eviron Mental Health Perspectives1978，27，： | 1 |
| 10 | First-principles study of solute-vacancy binding in magnesium显示文摘 | SHIN D WOLVERTON C | 2010 | Acta Materialia2010，58，： | 1 |
| 11 | First-principles study of crystalstructure and stability of Al-Mg-Si-(Cu) precipitates显示文摘 | RAVI C WOLVERTON C | 2004 | ActaMaterialia2004，52，： | 1 |
| 12 | Solute-vacancy binding in aluminum显示文摘 | WOLVERTON C | 2007 | Acta Materialia2007，55，： | 1 |
| 13 | First-Principles Determination of Muticomponent Hydride Phase Diagrams : Application to tile Li-Mg-N-H System 显示文摘 | Akbarzadeh A R Ozolins V Wolverton C | 2007 | Adv Mater2007，19，： | 1 |
| 14 | Coordinating planning activ- ity and information flow in a distributed planning system显示文摘 | Marie dcsJardins Michael Wolverton | 1999 | AI Magazinc1999，20，4： | 1 |
| 15 | Foliage plants for removing indoor air pollution from energy-efficient homes显示文摘 | Wolverton B C McDonald R C Watkins E A | 1984 | Economic Botany1984，38，2： | 1 |
| 16 | Foliage plants for removing indoor air pollutants from energy-efficient homes 显示文摘 | Wolverton BC Mcdonald RC Watkins EA | 1984 | Economic Botany1984，38，2： | 1 |
| 17 | Thermodynamic stability of Mg-Y-Z long-period stacking ordered structures 显示文摘 | Saal J E Wolverton C | 2012 | Scripta Materialia2012，67，10： | 1 |
| 18 | Predicting the size-and temperature dependent shapes of precipitates in Al-Zn Alloys 显示文摘 | Muller S Wolverton C Wang L W | 2000 | Acta Mater2000，48，： | 1 |
| 19 | Kinetic improvement in the Mg(NH2)2-Li H storage system by product seeding显示文摘 | Sudik A Yang J Halliday D Wolverton C | 2007 | Journal of Physical Chemistry C2007，111，： | 1 |
| 20 | Electrical energy storage for transportation-approaching the limits of,and going beyond,lithium-ion batteries显示文摘 | Thackeray M M Wolverton C Isaacs E D | 2012 | Energy&Environmental Science2012，5，7： | 1 |
您所在的机构暂未开通此部分的功能，请联系管理员