Zobrazeno 1 - 4
of 4
pro vyhledávání: '"Adeesh Kolluru"'
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:
Adeesh Kolluru, Muhammed Shuaibi, Aini Palizhati, Nima Shoghi, Abhishek Das, Brandon Wood, C. Lawrence Zitnick, John R. Kitchin, Zachary W. Ulissi
Publikováno v:
ACS Catalysis. 12:8572-8581
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e3c7385ede0aa728601e5bd6f46498af
https://doi.org/10.1021/acscatal.2c02291
https://doi.org/10.1021/acscatal.2c02291
Autor:
Richard Tran, Janice Lan, Muhammed Shuaibi, Brandon M. Wood, Siddharth Goyal, Abhishek Das, Javier Heras-Domingo, Adeesh Kolluru, Ammar Rizvi, Nima Shoghi, Anuroop Sriram, Félix Therrien, Jehad Abed, Oleksandr Voznyy, Edward H. Sargent, Zachary Ulissi, C. Lawrence Zitnick
The development of machine learning models for electrocatalysts requires a broad set of training data to enable their use across a wide variety of materials. One class of materials that currently lacks sufficient training data is oxides, which are cr
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6d071f29a5d51a9516c9a6213f3abc17
Autor:
Adeesh Kolluru, Nima Shoghi, Muhammed Shuaibi, Siddharth Goyal, Abhishek Das, C. Lawrence Zitnick, Zachary Ulissi
Publikováno v:
The Journal of Chemical Physics. 156:184702
Recent advances in Graph Neural Networks (GNNs) have transformed the space of molecular and catalyst discovery. Despite the fact that the underlying physics across these domains remain the same, most prior work has focused on building domain-specific
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::acc574d18cf6a84e96d33337a9c84a5d
https://doi.org/10.1063/5.0088019
https://doi.org/10.1063/5.0088019