Zobrazeno 1 - 10
of 563
pro vyhledávání: '"Chris, Wolverton"'
Autor:
Richard Barker, Colin P. S. Kruse, Christina Johnson, Amanda Saravia-Butler, Homer Fogle, Hyun-Seok Chang, Ralph Møller Trane, Noah Kinscherf, Alicia Villacampa, Aránzazu Manzano, Raúl Herranz, Laurence B. Davin, Norman G. Lewis, Imara Perera, Chris Wolverton, Parul Gupta, Pankaj Jaiswal, Sigrid S. Reinsch, Sarah Wyatt, Simon Gilroy
Publikováno v:
npj Microgravity, Vol 9, Iss 1, Pp 1-15 (2023)
Abstract Spaceflight presents a multifaceted environment for plants, combining the effects on growth of many stressors and factors including altered gravity, the influence of experiment hardware, and increased radiation exposure. To help understand t
Externí odkaz:
https://doaj.org/article/7be59dfdfe9b474f860220e2ba16e858
Publikováno v:
npj Computational Materials, Vol 8, Iss 1, Pp 1-10 (2022)
Abstract The discovery of high-dielectric materials is crucial to increasing the efficiency of electronic devices and batteries. Here, we report three previously unexplored materials with very high dielectric constants (69
Externí odkaz:
https://doaj.org/article/f5794614e00d4c2a96d8e52e66b9ba4c
Autor:
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
Publikováno v:
npj Computational Materials, Vol 8, Iss 1, Pp 1-26 (2022)
Abstract 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 automat
Externí odkaz:
https://doaj.org/article/959cff4db83443668adcec7142ba32a4
Publikováno v:
npj Computational Materials, Vol 8, Iss 1, Pp 1-12 (2022)
Abstract We design an advanced machine-learning (ML) model based on crystal graph convolutional neural network that is insensitive to volumes (i.e., scale) of the input crystal structures to discover novel quaternary chalcogenides, AMM′Q3 (A/M/M' =
Externí odkaz:
https://doaj.org/article/6ca50203a6c349a7a18b24b5453b58f3
Autor:
Casper W. Andersen, Rickard Armiento, Evgeny Blokhin, Gareth J. Conduit, Shyam Dwaraknath, Matthew L. Evans, Ádám Fekete, Abhijith Gopakumar, Saulius Gražulis, Andrius Merkys, Fawzi Mohamed, Corey Oses, Giovanni Pizzi, Gian-Marco Rignanese, Markus Scheidgen, Leopold Talirz, Cormac Toher, Donald Winston, Rossella Aversa, Kamal Choudhary, Pauline Colinet, Stefano Curtarolo, Davide Di Stefano, Claudia Draxl, Suleyman Er, Marco Esters, Marco Fornari, Matteo Giantomassi, Marco Govoni, Geoffroy Hautier, Vinay Hegde, Matthew K. Horton, Patrick Huck, Georg Huhs, Jens Hummelshøj, Ankit Kariryaa, Boris Kozinsky, Snehal Kumbhar, Mohan Liu, Nicola Marzari, Andrew J. Morris, Arash A. Mostofi, Kristin A. Persson, Guido Petretto, Thomas Purcell, Francesco Ricci, Frisco Rose, Matthias Scheffler, Daniel Speckhard, Martin Uhrin, Antanas Vaitkus, Pierre Villars, David Waroquiers, Chris Wolverton, Michael Wu, Xiaoyu Yang
Publikováno v:
Scientific Data, Vol 8, Iss 1, Pp 1-10 (2021)
Abstract The Open Databases Integration for Materials Design (OPTIMADE) consortium has designed a universal application programming interface (API) to make materials databases accessible and interoperable. We outline the first stable release of the s
Externí odkaz:
https://doaj.org/article/195f6a32cdad4e099318119238e9d978
Autor:
Koushik Pal, Yi Xia, Jiahong Shen, Jiangang He, Yubo Luo, Mercouri G. Kanatzidis, Chris Wolverton
Publikováno v:
npj Computational Materials, Vol 7, Iss 1, Pp 1-13 (2021)
Abstract The development of efficient thermal energy management devices such as thermoelectrics and barrier coatings often relies on compounds having low lattice thermal conductivity (κ l ). Here, we present the computational discovery of a large fa
Externí odkaz:
https://doaj.org/article/708c97e2328b4f82b5bd38047d3eb4b9
Autor:
Cheol Woo Park, Mordechai Kornbluth, Jonathan Vandermause, Chris Wolverton, Boris Kozinsky, Jonathan P. Mailoa
Publikováno v:
npj Computational Materials, Vol 7, Iss 1, Pp 1-9 (2021)
Abstract 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 autom
Externí odkaz:
https://doaj.org/article/69d11dd21aef4871997919972e929e7b
Publikováno v:
npj Computational Materials, Vol 7, Iss 1, Pp 1-8 (2021)
Abstract We investigate the microscopic mechanism of ultralow lattice thermal conductivity (κ l) of TlInTe2 and its weak temperature dependence using a unified theory of lattice heat transport, that considers contributions arising from the particle-
Externí odkaz:
https://doaj.org/article/8de841e634764945a66e36a640b1a4fe
Autor:
Muratahan Aykol, Vinay I. Hegde, Linda Hung, Santosh Suram, Patrick Herring, Chris Wolverton, Jens S. Hummelshøj
Publikováno v:
Nature Communications, Vol 10, Iss 1, Pp 1-7 (2019)
Predicting the synthesizability of inorganic materials is challenging due to the many variables and complex phenomena involved in synthesis. Here, the authors combine material stabilities with a historical analysis of experimental discovery timelines
Externí odkaz:
https://doaj.org/article/bc3f71be28634060b681de832add6283
Autor:
Qianqian Li, Zhenpeng Yao, Eungje Lee, Yaobin Xu, Michael M. Thackeray, Chris Wolverton, Vinayak P. Dravid, Jinsong Wu
Publikováno v:
Nature Communications, Vol 10, Iss 1, Pp 1-7 (2019)
Aided by advanced electron microscopy, the authors imaged dissociated dislocations in Li2MnO3 during an initial charge to 5 V. Such defects possess high gliding and transverse mobility and prompt O2 release. This work provides fresh insights into the
Externí odkaz:
https://doaj.org/article/dd2a97e0984840618306e75e43f3fc43