Zobrazeno 1 - 10
of 22
pro vyhledávání: '"Steven B. Torrisi"'
Autor:
Cameron J. Owen, Steven B. Torrisi, Yu Xie, Simon Batzner, Kyle Bystrom, Jennifer Coulter, Albert Musaelian, Lixin Sun, Boris Kozinsky
Publikováno v:
npj Computational Materials, Vol 10, Iss 1, Pp 1-16 (2024)
Abstract This work examines challenges associated with the accuracy of machine-learned force fields (MLFFs) for bulk solid and liquid phases of d-block elements. In exhaustive detail, we contrast the performance of force, energy, and stress predictio
Externí odkaz:
https://doaj.org/article/a68504f7e9f24d84b4a565a76a1b91db
Autor:
Eli Gerber, Steven B. Torrisi, Sara Shabani, Eric Seewald, Jordan Pack, Jennifer E. Hoffman, Cory R. Dean, Abhay N. Pasupathy, Eun-Ah Kim
Publikováno v:
Nature Communications, Vol 14, Iss 1, Pp 1-7 (2023)
Abstract Forming a hetero-interface is a materials-design strategy that can access an astronomically large phase space. However, the immense phase space necessitates a high-throughput approach for an optimal interface design. Here we introduce a high
Externí odkaz:
https://doaj.org/article/60b5b7080304416f976fcacbbeb86aaa
Autor:
Steven B. Torrisi, Martin Z. Bazant, Alexander E. Cohen, Min Gee Cho, Jens S. Hummelshøj, Linda Hung, Gaurav Kamat, Arash Khajeh, Adeesh Kolluru, Xiangyun Lei, Handong Ling, Joseph H. Montoya, Tim Mueller, Aini Palizhati, Benjamin A. Paren, Brandon Phan, Jacob Pietryga, Elodie Sandraz, Daniel Schweigert, Yang Shao-Horn, Amalie Trewartha, Ruijie Zhu, Debbie Zhuang, Shijing Sun
Publikováno v:
APL Machine Learning, Vol 1, Iss 2, Pp 020901-020901-11 (2023)
Machine learning (ML) is gaining popularity as a tool for materials scientists to accelerate computation, automate data analysis, and predict materials properties. The representation of input material features is critical to the accuracy, interpretab
Externí odkaz:
https://doaj.org/article/365358503b2c467dbae43bc88f59b30b
Autor:
Aini Palizhati, Steven B. Torrisi, Muratahan Aykol, Santosh K. Suram, Jens S. Hummelshøj, Joseph H. Montoya
Publikováno v:
Scientific Reports, Vol 12, Iss 1, Pp 1-13 (2022)
Abstract Sequential learning for materials discovery is a paradigm where a computational agent solicits new data to simultaneously update a model in service of exploration (finding the largest number of materials that meet some criteria) or exploitat
Externí odkaz:
https://doaj.org/article/44fdc6a6728f43ec91758e5db3bb9303
Autor:
Nicholas Marcella, Jin Soo Lim, Anna M. Płonka, George Yan, Cameron J. Owen, Jessi E. S. van der Hoeven, Alexandre C. Foucher, Hio Tong Ngan, Steven B. Torrisi, Nebojsa S. Marinkovic, Eric A. Stach, Jason F. Weaver, Joanna Aizenberg, Philippe Sautet, Boris Kozinsky, Anatoly I. Frenkel
Publikováno v:
Nature Communications, Vol 13, Iss 1, Pp 1-9 (2022)
Rational catalyst design is crucial toward achieving more energy-efficient and sustainable catalytic processes. Here the authors report a data-driven approach for understanding catalytic reactions mechanisms in dilute bimetallic catalysts by combinin
Externí odkaz:
https://doaj.org/article/59d8a90a6e7347d28f6c4ff406713a0d
Publikováno v:
eLife, Vol 10 (2021)
Hundreds of genes interact with the yeast nuclear pore complex (NPC), localizing at the nuclear periphery and clustering with co-regulated genes. Dynamic tracking of peripheral genes shows that they cycle on and off the NPC and that interaction with
Externí odkaz:
https://doaj.org/article/02174105337c4df7b618885be3ffdd7f
Lithium-ion batteries (LIBs) have attracted widespread attention as an efficient energy storage device on electric vehicles (EV) to achieve emission-free mobility. However, the performance of LIBs deteriorates with time and usage, and the state of he
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::bc7ff69c41f71cd45c1ab3e7ca9b38e5
https://doi.org/10.26434/chemrxiv-2023-sm0lj
https://doi.org/10.26434/chemrxiv-2023-sm0lj
Autor:
Michaela Burke Stevens, Megha Anand, Melissa E. Kreider, Eliza K. Price, José Zamara Zeledón, Liang Wang, Jiayu Peng, Hao Li, John M. Gregoire, Jens Hummelshøj, Thomas F. Jaramillo, Hongfei Jia, Jens K. Nørskov, Yuriy Roman-Leshkov, Yang Shao-Horn, Brian D. Storey, Santosh K. Suram, Steven B. Torrisi, Joseph H. Montoya
Publikováno v:
Energy & Environmental Science. 15:3775-3794
In this perspective, we highlight results of a research consortium devoted to advancing understanding of oxygen reduction reaction (ORR) catalysis as a means to inform fuel cell science.
Autor:
Steven B. Torrisi, John M. Gregoire, Junko Yano, Matthew R. Carbone, Carla P. Gomes, Linda Hung, Santosh K. Suram
Publikováno v:
Accelerated Materials Discovery ISBN: 9783110738087
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::cd2ab590bc2499294b0c6c265df43a29
https://doi.org/10.1515/9783110738087-003
https://doi.org/10.1515/9783110738087-003
Autor:
Steven B. Torrisi, Efthimios Kaxiras, Wei J. Chen, Amir Yacoby, Daniel T. Larson, Trevor David Rhone, Shaan Desai
Publikováno v:
Scientific Reports, Vol 10, Iss 1, Pp 1-11 (2020)
Scientific Reports
Scientific Reports
We use a data-driven approach to study the magnetic and thermodynamic properties of van der Waals (vdW) layered materials. We investigate monolayers of the form $$\hbox {A}_2\hbox {B}_2\hbox {X}_6$$ A 2 B 2 X 6 , based on the known material $$\hbox {