Výsledky vyhledávání - "Zachary, Ulissi"

AdsorbML: A Leap in Efficiency for Adsorption Energy Calculations using Generalizable Machine Learning Potentials

Autor: Janice Lan, Aini Palizhati, Muhammed Shuaibi, Brandon Wood, Brook Wander, Abhishek Das, Matt Uyttendaele, C. Zitnick, Zachary Ulissi

Computational catalysis is playing an increasingly significant role in the design of catalysts across a wide range of applications. A common task for many computational methods is the need to accurately compute the minimum binding energy — the adso

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::2ee6822669792e8b6ff064c30909eaa0
https://doi.org/10.21203/rs.3.rs-2592476/v1

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Multi-Descriptor Design of Ruthenium Catalysts for Durable Acidic Water Oxidation

Autor: Jehad Abed, Javier Heras-Domingo, Mingchuan Luo, Rohan Sanspeur, Wajdi Alnoush, Debora Meira, Hsiao-Tsu Wang, Jian Wang, Jigang Zhou, Daojin Zhou, Khalid Fatih, Drew Higgins, Zachary Ulissi, Edward Sargent

Further improvements in the performance and cost-effectiveness of water electrolyzers are urgently needed to accelerate decarbonization of hydrogen production. Iridium-free oxygen evolution reaction (OER) electrocatalysts are needed that are active a

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::a1413f85a952ae6bdd5e23c741f6b2a7
https://doi.org/10.21203/rs.3.rs-2410178/v1

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Hydrogen Adsorption Energy Necessary but Not Sufficient for HER Catalysis: Connecting Machine-Learned Descriptors with High-Throughput Experimental Catalysis over Bimetallic Nanoparticles

Autor: Kirby Broderick, Eric Lopato, Brook Wander, Stefan Bernhard, John Kitchin, Zachary Ulissi

The Sabatier principle is of fundamental importance to computational catalyst discovery, saving researchers time and expense by predicting catalytic activity \emph{in silico} at scale. However, as polycrystalline and nanoscale catalysts increasingly

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::f3bd25a773637c9c92bf60aae7fd02b8
https://doi.org/10.26434/chemrxiv-2022-fkj67

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FINETUNA: Fine-tuning Accelerated Molecular Simulations

Autor: Joseph Musielewicz, Xiaoxiao Wang, Tian Tian, Zachary Ulissi

Machine learning approaches have the potential to approximate Density Functional Theory (DFT) for atomistic simulations in a computationally efficient manner, which could dramatically increase the impact of computational simulations on real-world pro

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::49d2bbfdd151aca2ea141d180e8dea85

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The Open Catalyst 2022 (OC22) Dataset and Challenges for Oxide Electrocatalysts

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

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Identifying limitations in screening high-throughput photocatalytic bimetallic nanoparticles with machine-learned hydrogen adsorptions

Autor: Kirby Broderick, Eric Lopato, Brook Wander, Stefan Bernhard, John Kitchin, Zachary Ulissi

Publikováno v: Applied Catalysis B: Environmental. 320:121959

The Sabatier principle is of fundamental importance to computational catalyst discovery, saving researchers time and expense by predicting catalytic activity in silico at scale. However, as polycrystalline and nanoscale catalysts increasingly dominat

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::368d4dec9f08127b59f768d7fa688ef6
https://doi.org/10.1016/j.apcatb.2022.121959

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