Crossref Citations
This article has been cited by the following publications. This list is generated based on data provided by
Crossref.
Tanaka, Isao
Rajan, Krishna
and
Wolverton, Christopher
2018.
Data-centric science for materials innovation.
MRS Bulletin,
Vol. 43,
Issue. 9,
p.
659.
Kautz, Elizabeth J.
Hagen, Alexander R.
Johns, Jesse M.
and
Burkes, Douglas E.
2019.
A machine learning approach to thermal conductivity modeling: A case study on irradiated uranium-molybdenum nuclear fuels.
Computational Materials Science,
Vol. 161,
Issue. ,
p.
107.
Jensen, Zach
Kim, Edward
Kwon, Soonhyoung
Gani, Terry Z. H.
Román-Leshkov, Yuriy
Moliner, Manuel
Corma, Avelino
and
Olivetti, Elsa
2019.
A Machine Learning Approach to Zeolite Synthesis Enabled by Automatic Literature Data Extraction.
ACS Central Science,
Vol. 5,
Issue. 5,
p.
892.
Venkatasubramanian, Venkat
2019.
The promise of artificial intelligence in chemical engineering: Is it here, finally?.
AIChE Journal,
Vol. 65,
Issue. 2,
p.
466.
Schleder, Gabriel R
Padilha, Antonio C M
Acosta, Carlos Mera
Costa, Marcio
and
Fazzio, Adalberto
2019.
From DFT to machine learning: recent approaches to materials science–a review.
Journal of Physics: Materials,
Vol. 2,
Issue. 3,
p.
032001.
Schmidt, Jonathan
Marques, Mário R. G.
Botti, Silvana
and
Marques, Miguel A. L.
2019.
Recent advances and applications of machine learning in solid-state materials science.
npj Computational Materials,
Vol. 5,
Issue. 1,
Saal, James E.
Oliynyk, Anton O.
and
Meredig, Bryce
2020.
Machine Learning in Materials Discovery: Confirmed Predictions and Their Underlying Approaches.
Annual Review of Materials Research,
Vol. 50,
Issue. 1,
p.
49.
Crabtree, George
2020.
Self-Driving Laboratories Coming of Age.
Joule,
Vol. 4,
Issue. 12,
p.
2538.
Elnabawy, Ahmed O.
Schumann, Julia
Bothra, Pallavi
Cao, Ang
and
Nørskov, Jens K.
2020.
The Challenge of CO Hydrogenation to Methanol: Fundamental Limitations Imposed by Linear Scaling Relations.
Topics in Catalysis,
Vol. 63,
Issue. 7-8,
p.
635.
Herring, Patrick
Balaji Gopal, Chirranjeevi
Aykol, Muratahan
Montoya, Joseph H.
Anapolsky, Abraham
Attia, Peter M.
Gent, William
Hummelshøj, Jens S.
Hung, Linda
Kwon, Ha-Kyung
Moore, Patrick
Schweigert, Daniel
Severson, Kristen A.
Suram, Santosh
Yang, Zi
Braatz, Richard D.
and
Storey, Brian D.
2020.
BEEP: A Python library for Battery Evaluation and Early Prediction.
SoftwareX,
Vol. 11,
Issue. ,
p.
100506.
Montoya, Joseph H.
Winther, Kirsten T.
Flores, Raul A.
Bligaard, Thomas
Hummelshøj, Jens S.
and
Aykol, Muratahan
2020.
Autonomous intelligent agents for accelerated materials discovery.
Chemical Science,
Vol. 11,
Issue. 32,
p.
8517.
Aykol, Muratahan
Herring, Patrick
and
Anapolsky, Abraham
2020.
Machine learning for continuous innovation in battery technologies.
Nature Reviews Materials,
Vol. 5,
Issue. 10,
p.
725.
Kautz, Elizabeth
Ma, Wufei
Jana, Saumyadeep
Devaraj, Arun
Joshi, Vineet
Yener, Bülent
and
Lewis, Daniel
2020.
An image-driven machine learning approach to kinetic modeling of a discontinuous precipitation reaction.
Materials Characterization,
Vol. 166,
Issue. ,
p.
110379.
Hong, Zhi
Ward, Logan
Chard, Kyle
Blaiszik, Ben
and
Foster, Ian
2021.
Challenges and Advances in Information Extraction from Scientific Literature: a Review.
JOM,
Vol. 73,
Issue. 11,
p.
3383.
Melia, Hannah R.
Muckley, Eric S.
and
Saal, James E.
2021.
Materials informatics and sustainability—The case for urgency.
Data-Centric Engineering,
Vol. 2,
Issue. ,
Stoll, Anke
and
Benner, Peter
2021.
Machine learning for material characterization with an application for predicting mechanical properties.
GAMM-Mitteilungen,
Vol. 44,
Issue. 1,
Xu, Pengcheng
Lu, Tian
Ju, Lifei
Tian, Lumin
Li, Minjie
and
Lu, Wencong
2021.
Machine Learning Aided Design of Polymer with Targeted Band Gap Based on DFT Computation.
The Journal of Physical Chemistry B,
Vol. 125,
Issue. 2,
p.
601.
Jiao, Pengcheng
and
Alavi, Amir H.
2021.
Artificial intelligence-enabled smart mechanical metamaterials: advent and future trends.
International Materials Reviews,
Vol. 66,
Issue. 6,
p.
365.
Mann, Vipul
and
Venkatasubramanian, Venkat
2021.
Predicting chemical reaction outcomes: A grammar ontology‐based transformer framework.
AIChE Journal,
Vol. 67,
Issue. 3,
Roy, Ankit
Roy, Indranil
Santodonato, Louis J.
and
Balasubramanian, Ganesh
2022.
Data-Guided Feature Identification for Predicting Specific Heat of Multicomponent Alloys.
JOM,
Vol. 74,
Issue. 4,
p.
1406.