Search Results - "npj computational materials"

Recent advances and applications of machine learning in solid-state materials science by Schmidt, Jonathan, Marques, Mário R. G., Botti, Silvana, Marques, Miguel A. L.

“…One of the most exciting tools that have entered the material science toolbox in recent years is machine learning. This collection of statistical methods has…”
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Recent advances and applications of deep learning methods in materials science by Choudhary, Kamal, DeCost, Brian, Chen, Chi, Jain, Anubhav, Tavazza, Francesca, Cohn, Ryan, Park, Cheol Woo, Choudhary, Alok, Agrawal, Ankit, Billinge, Simon J. L., Holm, Elizabeth, Ong, Shyue Ping, Wolverton, Chris

“…Deep learning (DL) is one of the fastest-growing topics in materials data science, with rapidly emerging applications spanning atomistic, image-based,…”
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Atomistic Line Graph Neural Network for improved materials property predictions by Choudhary, Kamal, DeCost, Brian

“…Graph neural networks (GNN) have been shown to provide substantial performance improvements for atomistic material representation and modeling compared with…”
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A review of oxygen reduction mechanisms for metal-free carbon-based electrocatalysts by Ma, Ruguang, Lin, Gaoxin, Zhou, Yao, Liu, Qian, Zhang, Tao, Shan, Guangcun, Yang, Minghui, Wang, Jiacheng

“…The sluggish kinetics of Oxygen Reduction Reaction (ORR) at the cathode in proton exchange membrane fuel cells or metal-air batteries requires highly effective…”
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Review on modeling of the anode solid electrolyte interphase (SEI) for lithium-ion batteries by Wang, Aiping, Kadam, Sanket, Li, Hong, Shi, Siqi, Qi, Yue

“…A passivation layer called the solid electrolyte interphase (SEI) is formed on electrode surfaces from decomposition products of electrolytes. The SEI allows…”
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Machine learning in materials informatics: recent applications and prospects by Ramprasad, Rampi, Batra, Rohit, Pilania, Ghanshyam, Mannodi-Kanakkithodi, Arun, Kim, Chiho

“…Propelled partly by the Materials Genome Initiative, and partly by the algorithmic developments and the resounding successes of data-driven efforts in other…”
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The joint automated repository for various integrated simulations (JARVIS) for data-driven materials design by Choudhary, Kamal, Garrity, Kevin F., Reid, Andrew C. E., DeCost, Brian, Biacchi, Adam J., Hight Walker, Angela R., Trautt, Zachary, Hattrick-Simpers, Jason, Kusne, A. Gilad, Centrone, Andrea, Davydov, Albert, Jiang, Jie, Pachter, Ruth, Cheon, Gowoon, Reed, Evan, Agrawal, Ankit, Qian, Xiaofeng, Sharma, Vinit, Zhuang, Houlong, Kalinin, Sergei V., Sumpter, Bobby G., Pilania, Ghanshyam, Acar, Pinar, Mandal, Subhasish, Haule, Kristjan, Vanderbilt, David, Rabe, Karin, Tavazza, Francesca

“…The Joint Automated Repository for Various Integrated Simulations (JARVIS) is an integrated infrastructure to accelerate materials discovery and design using…”
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Precision and efficiency in solid-state pseudopotential calculations by Prandini, Gianluca, Marrazzo, Antimo, Castelli, Ivano E., Mounet, Nicolas, Marzari, Nicola

“…Despite the enormous success and popularity of density-functional theory, systematic verification and validation studies are still limited in number and scope…”
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Plasmon-enhanced light–matter interactions and applications by Yu, Huakang, Peng, Yusi, Yang, Yong, Li, Zhi-Yuan

“…Surface plasmons are coherent and collective electron oscillations confined at the dielectric–metal interface. Benefitting from the inherent subwavelength…”
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Machine learning for perovskite materials design and discovery by Tao, Qiuling, Xu, Pengcheng, Li, Minjie, Lu, Wencong

“…The development of materials is one of the driving forces to accelerate modern scientific progress and technological innovation. Machine learning (ML)…”
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A general-purpose machine learning framework for predicting properties of inorganic materials by Ward, Logan, Agrawal, Ankit, Choudhary, Alok, Wolverton, Christopher

“…A very active area of materials research is to devise methods that use machine learning to automatically extract predictive models from existing materials…”
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New frontiers for the materials genome initiative by de Pablo, Juan J., Jackson, Nicholas E., Webb, Michael A., Chen, Long-Qing, Moore, Joel E., Morgan, Dane, Jacobs, Ryan, Pollock, Tresa, Schlom, Darrell G., Toberer, Eric S., Analytis, James, Dabo, Ismaila, DeLongchamp, Dean M., Fiete, Gregory A., Grason, Gregory M., Hautier, Geoffroy, Mo, Yifei, Rajan, Krishna, Reed, Evan J., Rodriguez, Efrain, Stevanovic, Vladan, Suntivich, Jin, Thornton, Katsuyo, Zhao, Ji-Cheng

“…The Materials Genome Initiative (MGI) advanced a new paradigm for materials discovery and design, namely that the pace of new materials deployment could be…”
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The ReaxFF reactive force-field: development, applications and future directions by Senftle, Thomas P, Hong, Sungwook, Islam, Md Mahbubul, Kylasa, Sudhir B, Zheng, Yuanxia, Shin, Yun Kyung, Junkermeier, Chad, Engel-Herbert, Roman, Janik, Michael J, Aktulga, Hasan Metin, Verstraelen, Toon, Grama, Ananth, van Duin, Adri C T

“…The reactive force-field (ReaxFF) interatomic potential is a powerful computational tool for exploring, developing and optimizing material properties. Methods…”
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Machine-learning-assisted discovery of polymers with high thermal conductivity using a molecular design algorithm by Wu, Stephen, Kondo, Yukiko, Kakimoto, Masa-aki, Yang, Bin, Yamada, Hironao, Kuwajima, Isao, Lambard, Guillaume, Hongo, Kenta, Xu, Yibin, Shiomi, Junichiro, Schick, Christoph, Morikawa, Junko, Yoshida, Ryo

“…The use of machine learning in computational molecular design has great potential to accelerate the discovery of innovative materials. However, its practical…”
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On-the-fly active learning of interpretable Bayesian force fields for atomistic rare events by Vandermause, Jonathan, Torrisi, Steven B., Batzner, Simon, Xie, Yu, Sun, Lixin, Kolpak, Alexie M., Kozinsky, Boris

“…Machine learned force fields typically require manual construction of training sets consisting of thousands of first principles calculations, which can result…”
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Inverse-designed spinodoid metamaterials by Kumar, Siddhant, Tan, Stephanie, Zheng, Li, Kochmann, Dennis M.

“…After a decade of periodic truss-, plate-, and shell-based architectures having dominated the design of metamaterials, we introduce the non-periodic class of…”
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Small data machine learning in materials science by Xu, Pengcheng, Ji, Xiaobo, Li, Minjie, Lu, Wencong

“…This review discussed the dilemma of small data faced by materials machine learning. First, we analyzed the limitations brought by small data. Then, the…”
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A strategy to apply machine learning to small datasets in materials science by Zhang, Ying, Ling, Chen

“…There is growing interest in applying machine learning techniques in the research of materials science. However, although it is recognized that materials…”
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Active learning in materials science with emphasis on adaptive sampling using uncertainties for targeted design by Lookman, Turab, Balachandran, Prasanna V., Xue, Dezhen, Yuan, Ruihao

“…One of the main challenges in materials discovery is efficiently exploring the vast search space for targeted properties as approaches that rely on…”
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Interplay between Kitaev interaction and single ion anisotropy in ferromagnetic CrI3 and CrGeTe3 monolayers by Xu, Changsong, Feng, Junsheng, Xiang, Hongjun, Bellaiche, Laurent

“…Magnetic anisotropy is crucially important for the stabilization of two-dimensional (2D) magnetism, which is rare in nature but highly desirable in spintronics…”
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