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Reference EntriesFull Text
Ulissi, Zachary W
Journal title:American Men & Women of Science : A Biographical Directory of Today’s Leaders in Physical, Biological and Related Sciences: TRA-WID, 2022, p.11637-11637
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Ulissi, Zachary W
Journal title:American Men & Women of Science : A Biographical Directory of Today’s Leaders in Physical, Biological and Related Sciences: TRO-WIK, 2021, p.11607-11608
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ArticlesPeer ReviewedFull Text
To address surface reaction network complexity using scaling relations machine learning and DFT calculations
Author / Creator:Ulissi, Zachary W. ; Medford, Andrew J. ; Bligaard, Thomas ; Nørskov, Jens K.
Publisher:London: Nature Publishing Group UK
Journal title:Nature communications, 2017-03, Vol.8 (1), p.14621-14621, Article 14621
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ArticlesPeer ReviewedFull Text
Methods for comparing uncertainty quantifications for material property predictions
Author / Creator:Tran, Kevin ; Neiswanger, Willie ; Yoon, Junwoong ; Zhang, Qingyang ; Xing, Eric ; Ulissi, Zachary W
Publisher:Bristol: IOP Publishing
Journal title:Machine learning: science and technology, 2020-06, Vol.1 (2), p.25006
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ArticlesPeer ReviewedFull Text
Diameter-dependent ion transport through the interior of isolated single-walled carbon nanotubes
Author / Creator:Choi, Wonjoon ; Ulissi, Zachary W. ; Shimizu, Steven F.E. ; Bellisario, Darin O. ; Ellison, Mark D. ; Strano, Michael S.
Publisher:London: Nature Publishing Group UK
Journal title:Nature communications, 2013-09, Vol.4 (1), p.2397-2397, Article 2397
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ArticlesPeer ReviewedFull Text
Molecular recognition using corona phase complexes made of synthetic polymers adsorbed on carbon nanotubes
Author / Creator:Zhang, Jingqing ; Landry, Markita P. ; Barone, Paul W. ; Kim, Jong-Ho ; Lin, Shangchao ; Ulissi, Zachary W. ; Lin, Dahua ; Mu, Bin ; Boghossian, Ardemis A. ; Hilmer, Andrew J. ; Rwei, Alina ; Hinckley, Allison C. ; Kruss, Sebastian ; Shandell, Mia A. ; Nair, Nitish ; Blake, Steven ; Şen, Fatih ; Şen, Selda ; Croy, Robert G. ; Li, Deyu ; Yum, Kyungsuk ; Ahn, Jin-Ho ; Jin, Hong ; Heller, Daniel A. ; Essigmann, John M. ; Blankschtein, Daniel ; Strano, Michael S.
Publisher:London: Nature Publishing Group UK
Journal title:Nature nanotechnology, 2013-12, Vol.8 (12), p.959-968
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AdsorbML: a leap in efficiency for adsorption energy calculations using generalizable machine learning potentials
Author / Creator:Lan, Janice ; Palizhati, Aini ; Shuaibi, Muhammed ; Wood, Brandon M. ; Wander, Brook ; Das, Abhishek ; Uyttendaele, Matt ; Zitnick, C. Lawrence ; Ulissi, Zachary W.
Publisher:London: Nature Publishing Group UK
Journal title:npj computational materials, 2023-09, Vol.9 (1), p.172-9, Article 172
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ArticlesPeer ReviewedFull Text
Generalization of graph-based active learning relaxation strategies across materials
Author / Creator:Wang, Xiaoxiao ; Musielewicz, Joseph ; Tran, Richard ; Kumar Ethirajan, Sudheesh ; Fu, Xiaoyan ; Mera, Hilda ; Kitchin, John R ; Kurchin, Rachel C ; Ulissi, Zachary W
Publisher:Bristol: IOP Publishing
Journal title:Machine learning: science and technology, 2024-06, Vol.5 (2), p.25018
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Robust and scalable uncertainty estimation with conformal prediction for machine-learned interatomic potentials
Author / Creator:Hu, Yuge ; Musielewicz, Joseph ; Ulissi, Zachary W ; Medford, Andrew J
Publisher:Bristol: IOP Publishing
Journal title:Machine learning: science and technology, 2022-12, Vol.3 (4), p.45028
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Enabling robust offline active learning for machine learning potentials using simple physics-based priors
Author / Creator:Shuaibi, Muhammed ; Sivakumar, Saurabh ; Chen, Rui Qi ; Ulissi, Zachary W
Publisher:Bristol: IOP Publishing
Journal title:Machine learning: science and technology, 2021-06, Vol.2 (2), p.25007
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Deep reinforcement learning for predicting kinetic pathways to surface reconstruction in a ternary alloy
Author / Creator:Yoon, Junwoong ; Cao, Zhonglin ; Raju, Rajesh K ; Wang, Yuyang ; Burnley, Robert ; Gellman, Andrew J ; Barati Farimani, Amir ; Ulissi, Zachary W
Publisher:Bristol: IOP Publishing
Journal title:Machine learning: science and technology, 2021-12, Vol.2 (4), p.45018
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ArticlesPeer ReviewedFull Text
FINETUNA: fine-tuning accelerated molecular simulations
Author / Creator:Musielewicz, Joseph ; Wang, Xiaoxiao ; Tian, Tian ; Ulissi, Zachary
Publisher:Bristol: IOP Publishing
Journal title:Machine learning: science and technology, 2022-09, Vol.3 (3), p.3
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ArticlesPeer ReviewedFull Text
Robust and scalable uncertainty estimation with conformal prediction for machine-learned interatomic potentials
Author / Creator:Hu, Yuge ; Musielewicz, Joseph ; Ulissi, Zachary W. ; Medford, Andrew J.
Publisher:United States: IOP Publishing
Journal title:Machine learning: science and technology, 2022-12, Vol.3 (4)
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ArticlesPeer ReviewedFull Text
Deep reinforcement learning for predicting kinetic pathways to surface reconstruction in a ternary alloy
Author / Creator:Yoon, Junwoong ; Cao, Zhonglin ; Raju, Rajesh K. ; Wang, Yuyang ; Burnley, Robert ; Gellman, Andrew J. ; Barati Farimani, Amir ; Ulissi, Zachary W.
Publisher:United States: IOP Publishing
Journal title:Machine learning: science and technology, 2021-08, Vol.2 (4)
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ArticlesPeer ReviewedFull Text
Molecular recognition using nanotube-adsorbed polymer phases: nanotube antibodies
Author / Creator:Zhang, Jingqing ; Landry, Markita P. ; Barone, Paul W. ; Kim, Jong-Ho ; Lin, Shangchao ; Ulissi, Zachary W. ; Lin, Dahua ; Mu, Bin ; Heller, Daniel A. ; Boghossian, Ardemis A. ; Hilmer, Andrew J. ; Rwei, Alina ; Hinckley, Allison C. ; Kruss, Sebastian ; Shandell, Mia A. ; Nair, Nitish ; Blake, Steven ; Sen, Fatih ; Sen, Selda ; Croy, Robert G. ; Li, Deyu ; Yum, Kyungsuk ; Ahn, Jin-Ho ; Jin, Hong ; Essigmann, John M. ; Blankschtein, Daniel ; Strano, Michael S.
Journal title:Nature nanotechnology, 2013-11, Vol.8 (12), p.959-968
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ArticlesFull Text
Catlas: an automated framework for catalyst discovery demonstrated for direct syngas conversion
Author / Creator:Wander, Brook ; Broderick, Kirby ; Ulissi, Zachary W
Publisher:Ithaca: Cornell University Library, arXiv.org
Journal title:arXiv.org, 2022-08
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AdsorbML: A Leap in Efficiency for Adsorption Energy Calculations using Generalizable Machine Learning Potentials
Author / Creator:Lan, Janice ; Palizhati, Aini ; Shuaibi, Muhammed ; Wood, Brandon M ; Wander, Brook ; Das, Abhishek ; Uyttendaele, Matt ; Zitnick, C Lawrence ; Ulissi, Zachary W
Publisher:Ithaca: Cornell University Library, arXiv.org
Journal title:arXiv.org, 2023-09
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Adapting OC20-trained EquiformerV2 Models for High-Entropy Materials
Author / Creator:Clausen, Christian M ; Rossmeisl, Jan ; Ulissi, Zachary W
Publisher:Ithaca: Cornell University Library, arXiv.org
Journal title:arXiv.org, 2024-03
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Computational catalyst discovery: Active classification through myopic multiscale sampling
Author / Creator:Tran, Kevin ; Neiswanger, Willie ; Broderick, Kirby ; Xing, Erix ; Schneider, Jeff ; Ulissi, Zachary W
Publisher:Ithaca: Cornell University Library, arXiv.org
Journal title:arXiv.org, 2021-02
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Enabling robust offline active learning for machine learning potentials using simple physics-based priors
Author / Creator:Shuaibi, Muhammed ; Sivakumar, Saurabh ; Rui Qi Chen ; Ulissi, Zachary W
Publisher:Ithaca: Cornell University Library, arXiv.org
Journal title:arXiv.org, 2020-08
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Ulissi, Zachary W

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Ulissi, Zachary W

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To address surface reaction network complexity using scaling relations machine learning and DFT calculations

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Methods for comparing uncertainty quantifications for material property predictions

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Diameter-dependent ion transport through the interior of isolated single-walled carbon nanotubes

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Molecular recognition using corona phase complexes made of synthetic polymers adsorbed on carbon nanotubes

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AdsorbML: a leap in efficiency for adsorption energy calculations using generalizable machine learning potentials

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Generalization of graph-based active learning relaxation strategies across materials

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Robust and scalable uncertainty estimation with conformal prediction for machine-learned interatomic potentials

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Enabling robust offline active learning for machine learning potentials using simple physics-based priors

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Deep reinforcement learning for predicting kinetic pathways to surface reconstruction in a ternary alloy

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FINETUNA: fine-tuning accelerated molecular simulations

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Robust and scalable uncertainty estimation with conformal prediction for machine-learned interatomic potentials

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Deep reinforcement learning for predicting kinetic pathways to surface reconstruction in a ternary alloy

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Molecular recognition using nanotube-adsorbed polymer phases: nanotube antibodies

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Catlas: an automated framework for catalyst discovery demonstrated for direct syngas conversion

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

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Adapting OC20-trained EquiformerV2 Models for High-Entropy Materials

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Computational catalyst discovery: Active classification through myopic multiscale sampling

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Enabling robust offline active learning for machine learning potentials using simple physics-based priors

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