Papers citing Anthropogenic biases in chemical reaction data hinder exploratory inorganic synthesis
Automation and computer-assisted planning for chemical synthesis
Nature Reviews Methods Primers · 2021
Yuning Shen, Julia E. Borowski, Melissa A. Hardy, Richmond Sarpong, Abigail G. Doyle, Tim Cernak
Read original paper ↗ ·Predicting Reaction Yields via Supervised Learning
Accounts of Chemical Research · 2021
Andrzej M. Żurański, Jesus I. Martinez Alvarado, Benjamin J. Shields, Abigail G. Doyle
Read original paper ↗ ·Improving Data and Prediction Quality of High-Throughput Perovskite Synthesis with Model Fusion
Journal of Chemical Information and Modeling · 2021
Yuanqing Tang, Zhi Li, Mansoor Ani Najeeb, Hamed Eramian, Emory M. Chan, Alexander J. Norquist, D. Frank Hsu, Joshua Schrier
Read original paper ↗ ·What's Left for a Computational Chemist To Do in the Age of Machine Learning?
Israel Journal of Chemistry · 2021
Heather J. Kulik
Read original paper ↗ ·Toward Better and Smarter Batteries by Combining AI with Multisensory and Self‐Healing Approaches
Advanced Energy Materials · 2021
Tejs Vegge, Jean‐Marie Tarascon, Kristina Edström
Read original paper ↗ ·Atomic understanding of structural deformations upon ablation of graphene
Nano Select · 2021
Mohammad Alaghemandi, Leili Salehi, Panagis D. Samolis, Benyamin T. Trachtenberg, Ahmet Turnalı, Michelle Y. Sander, Sahar Sharifzadeh
Read original paper ↗ ·Observing and Modeling the Sequential Pairwise Reactions that Drive Solid‐State Ceramic Synthesis
Advanced Materials · 2021
AKIRA B. MIURA, Christopher J. Bartel, Yosuke Goto, Yoshikazu Mizuguchi, Chikako Moriyoshi, Yoshihiro Kuroiwa, Yongming Wang, Toshie Yaguchi, Manabu Shirai, Masanori Nagao, Nataly Carolina Rosero‐Navarro, Kiyoharu Tadanaga, Gerbrand Ceder, Wenhao Q. Sun
Read original paper ↗ ·Putting Density Functional Theory to the Test in Machine-Learning-Accelerated Materials Discovery
The Journal of Physical Chemistry Letters · 2021
Chenru Duan, Fang Liu, Aditya Nandy, Heather J. Kulik
Read original paper ↗ ·Predicting inorganic dimensionality in templated metal oxides
The Journal of Chemical Physics · 2021
Qianxiang Ai, Davion Marquise Williams, Matthew Danielson, Liam G. Spooner, Joshua A. Engler, Zihui Ding, Matthias Zeller, Alexander J. Norquist, Joshua Schrier
Read original paper ↗ ·Extraction of Catalyst Design Heuristics from Random Catalyst Dataset and their Utilization in Catalyst Development for Oxidative Coupling of Methane
ChemCatChem · 2021
Sunao Nakanowatari, Thanh Nhat Nguyen, Hiroki Chikuma, Aya Fujiwara, Kalaivani Seenivasan, Ashutosh Thakur, Lauren Takahashi, KEISUKE G. TAKAHASHI, Toshiaki Taniike
Read original paper ↗ ·Analysis of Updated Literature Data up to 2019 on the Oxidative Coupling of Methane Using an Extrapolative Machine‐Learning Method to Identify Novel Catalysts
ChemCatChem · 2021
Shinya Mine, Motoshi Takao, Taichi Yamaguchi, Takashi Toyao, Zen Maeno, S. M. A. Hakim Siddiki, Satoru Takakusagi, Ken‐ichi Shimizu, Ichigaku Takigawa
Read original paper ↗ ·Best practices in machine learning for chemistry
Nature Chemistry · 2021
Nongnuch Artrith, Keith T. Butler, François‐Xavier Coudert, Seungwu Han, Olexandr Isayev, Anubhav Jain, Aron Walsh
Read original paper ↗ ·Evaluating the Performance of a Transformer-based Organic Reaction Prediction Model
ChemRxiv · 2021
William Borrelli, Joshua Schrier
Read original paper ↗ ·Scalable HPC & AI infrastructure for COVID-19 therapeutics
Platform for Advanced Scientific Computing Conference (PASC) · 2021
Hyungro Lee, André Merzky, Li Tan, Mikhail Titov, Matteo Turilli, Dario Alfè, Agastya P. Bhati, Alex Brace, Austin Clyde, Peter V. Coveney, Heng Ma, Arvind Ramanathan, Rick L. Stevens, Anda Trifan, Hubertus J. J. van Dam, Shunzhou Wan, Sean R. Wilkinson, Shantenu Jha
Read original paper ↗ ·Computational Discovery of Transition-metal Complexes: From High-throughput Screening to Machine Learning
Chemical Reviews · 2021
Aditya Nandy, Chenru Duan, Michael G. Taylor, Fang Liu, Adam H. Steeves, Heather J. Kulik
Read original paper ↗ ·Using automated serendipity to discover how trace water promotes and inhibits lead halide perovskite crystal formation
Applied Physics Letters · 2021
Philip W. Nega, Zhi Li, Victor Ghosh, Janak Thapa, Shijing Sun, Noor Titan Putri Hartono, Mansoor Ani Najeeb, Alexander J. Norquist, Tonio Buonassisi, Emory M. Chan, Joshua Schrier
Read original paper ↗ ·Design and Application of a Screening Set for Monophosphine Lig-ands in Metal Catalysis
Venue not supplied by source · 2021
Tobias Gensch, Sleight R. Smith, Thomas J. Colacot, Yam N. Timsina, Guolin Xu, Ben W. Glasspoole, Matthew S. Sigman
Read original paper ↗ ·Highly accurate prediction of reaction performance and enantioselectivity with a uniform machine learning protocol
Research Square · 2021
Xiaofei Sun, Jingyuan Zhu, Hengzhi You, Bin Chen, Fen‐Er Chen
Read original paper ↗ ·Scaffold-Induced Molecular Graph (SIMG): Effective Graph Sampling Methods for High-Throughput Computational Drug Discovery
arXiv (Cornell University) · 2021
Austin Clyde, Ashka Shah, Max Zvyagin, Arvind Ramanathan, Rick L. Stevens
Read original paper ↗ ·Panoramic (in beam) studies of materials synthesis
Elsevier eBooks · 2021
Mercouri G. Kanatzidis, Rebecca McClain
Read original paper ↗ ·Using Machine Learning and Data Mining to Leverage Community Knowledge for the Engineering of Stable Metal–Organic Frameworks
Journal of the American Chemical Society · 2021
Aditya Nandy, Chenru Duan, Heather J. Kulik
Read original paper ↗ ·Approaches using AI in medicinal chemistry
Elsevier eBooks · 2021
Christian Tyrchan, Eva Nittinger, Dea Gogishvili, Atanas Patronov, Thierry Kogej
Read original paper ↗ ·Mimicking nature to control bio-material surface wetting and adhesion
International Materials Reviews · 2021
Ming Li, Chang Li, Bamber R.K. Blackman, Eduardo Saiz
Read original paper ↗ ·Ultrahigh Throughput Protein–Ligand Docking with Deep Learning
Methods in molecular biology · 2021
Austin Clyde
Read original paper ↗ ·Dataset of Solution-based Inorganic Materials Synthesis Recipes Extracted from the Scientific Literature
arXiv (Cornell University) · 2021
Zheren Wang, Kononova, Olga, Kevin Cruse, Tanjin He, Haoyan Huo, Yuxing Fei, Yan Zeng, Yingzhi Sun, Zijian Cai, Wenhao Q. Sun, Gerbrand Ceder
Read original paper ↗ ·Implications of the BATTERY 2030+ AI‐Assisted Toolkit on Future Low‐TRL Battery Discoveries and Chemistries
Advanced Energy Materials · 2021
Arghya Bhowmik, Maitane Berecibar, Montse Casas‐Cabanas, Gábor Cśanyi, Robert Dominko, Kersti Hermansson, M. Rosa Palacín, Helge Sören Stein, Tejs Vegge
Read original paper ↗ ·Audacity of huge: overcoming challenges of data scarcity and data quality for machine learning in computational materials discovery
Current Opinion in Chemical Engineering · 2021
Aditya Nandy, Chenru Duan, Heather J. Kulik
Read original paper ↗ ·Shapley Additive Explanations of Multigeometrical Variable Coupling Effect in Transonic Compressor
Journal of Engineering for Gas Turbines and Power · 2021
Junying Wang, Xiao Gang He, Baotong Wang, Xinqian Zheng
Read original paper ↗ ·Towards Predictive Synthesis of Inorganic Materials Using Network Science
Frontiers in Chemistry · 2021
Alex Aziz, Javier Carrasco
Read original paper ↗ ·Knowledge extraction in catalysis utilizing design of experiments and machine learning
Current Opinion in Chemical Engineering · 2021
Blake MacQueen, Rasika Jayarathna, Jochen A. Lauterbach
Read original paper ↗ ·Artificial Intelligence based Approach for Rapid Material Discovery: From Chemical Synthesis to Quantum Materials
Venue not supplied by source · 2022
Robert S. Tempke
Read original paper ↗ ·Predicting compositional changes of organic–inorganic hybrid materials with Augmented CycleGAN
Digital Discovery · 2022
Qianxiang Ai, Alexander J. Norquist, Joshua Schrier
Read original paper ↗ ·Active meta-learning for predicting and selecting perovskite crystallization experiments
The Journal of Chemical Physics · 2022
Venkateswaran Shekar, Gareth Nicholas, Mansoor Ani Najeeb, Margaret Zeile, Vincent Yu, Xiaorong Wang, Dylan Slack, Zhi Li, Philip W. Nega, Emory M. Chan, Alexander J. Norquist, Joshua Schrier, Sorelle A. Friedler
Read original paper ↗ ·Innovative Separations Research and Development Needs for Advanced Fuel Cycles
Venue not supplied by source · 2022
Bruce A. Moyer, Gregg J. Lumetta, Stephanie H. Bruffey, Sarah C. Finkeldei, Ken C. Marsden, Michael Forrest Simpson, Mark Jensen, Peter R. Zalupski, Aurora E. Clark, Ping Xiong Yang, Gregory P. Horne
Read original paper ↗ ·A review of the recent progress in battery informatics
npj Computational Materials · 2022
Chen Ling
Read original paper ↗ ·Autonomous design of new chemical reactions using a variational autoencoder
Communications Chemistry · 2022
Robert S. Tempke, Terence Musho
Read original paper ↗ ·Machine Learning for the Discovery, Design, and Engineering of Materials
Annual Review of Chemical and Biomolecular Engineering · 2022
Chenru Duan, Aditya Nandy, Heather J. Kulik
Read original paper ↗ ·The case for data science in experimental chemistry: examples and recommendations
Nature Reviews Chemistry · 2022
Junko Yano, Kelly J. Gaffney, John M. Gregoire, Linda Hung, A. Ourmazd, Joshua Schrier, James A. Sethian, Francesca Maria Toma
Read original paper ↗ ·Modeling antiphase boundary energies of Ni3Al-based alloys using automated density functional theory and machine learning
npj Computational Materials · 2022
Enze Chen, Artur Tamm, Tao Wang, Mario E. Epler, Mark Asta, Timofey Frolov
Read original paper ↗ ·Machine Learning for Chemical Reactivity: The Importance of Failed Experiments
Angewandte Chemie International Edition · 2022
Felix Strieth‐Kalthoff, Frederik Sandfort, Marius Kühnemund, Felix R. Schäfer, Herbert Kuchen, Frank Glorius
Read original paper ↗ ·Maschinelles Lernen zur Vorhersage chemischer Reaktivität: Die Bedeutung “gescheiterter” Experimente
Angewandte Chemie · 2022
Felix Strieth‐Kalthoff, Frederik Sandfort, Marius Kühnemund, Felix R. Schäfer, Herbert Kuchen, Frank Glorius
Read original paper ↗ ·Chemical property prediction under experimental biases
Scientific Reports · 2022
Yang Liu, Hisashi Kashima
Read original paper ↗ ·Theory‐Guided Machine Learning to Predict the Performance of Noble Metal Catalysts in the Water‐Gas Shift Reaction
ChemCatChem · 2022
Joyjit Chattoraj, Brahim Hamadicharef, Jian Feng Kong, Mohan Kashyap Pargi, Yingzhi Zeng, Chee Kok Poh, Luwei Chen, Fei Gao, Teck Leong Tan
Read original paper ↗ ·Dataset of solution-based inorganic materials synthesis procedures extracted from the scientific literature
Scientific Data · 2022
Zheren Wang, Olga Kononova, Kevin Cruse, Tanjin He, Haoyan Huo, Yuxing Fei, Yan Zeng, Yingzhi Sun, Zijian Cai, Wenhao Q. Sun, Gerbrand Ceder
Read original paper ↗ ·Advancing Base Metal Catalysis through Data Science: Insight and Predictive Models for Ni-Catalyzed Borylation through Supervised Machine Learning
Organometallics · 2022
Jason M. Stevens, Jun Li, Eric M. Simmons, Steven R. Wisniewski, Stacey DiSomma, Kenneth J. Fraunhoffer, Peng Geng, Bo Nan Hao, Erika W. Jackson
Read original paper ↗ ·Classifying the toxicity of pesticides to honey bees via support vector machines with random walk graph kernels
The Journal of Chemical Physics · 2022
Ping Yang, E. Adrian Henle, Xiaoli Z. Fern, Cory M. Simon
Read original paper ↗ ·Design and Application of a Screening Set for Monophosphine Ligands in Cross-Coupling
ACS Catalysis · 2022
Tobias Gensch, Sleight R. Smith, Thomas J. Colacot, Yam N. Timsina, Guolin Xu, Ben W. Glasspoole, Matthew S. Sigman
Read original paper ↗ ·Mechanistic Inference from Statistical Models at Different Data-Size Regimes
ACS Catalysis · 2022
Danilo Machado Lustosa, Anat Milo
Read original paper ↗ ·Reaction impurity prediction using a data mining approach
ChemRxiv · 2022
Adarsh Arun, Zhen Guo, Simon Sung, Alexei A. Lapkin
Read original paper ↗ ·Machine Learning Modeling of Environmentally Relevant Chemical Reactions for Organic Compounds
ACS ES&T Water · 2022
Kai Zhang, Huichun Judy Zhang
Read original paper ↗ ·