Papers citing Anthropogenic biases in chemical reaction data hinder exploratory inorganic synthesis
Serendipity based recommender system for perovskites material discovery: balancing exploration and exploitation across multiple models
ChemRxiv · 2022
Venkateswaran Shekar, Vincent Yu, Benjamin J. Garcia, David Benjamin Gordon, Gemma E. Moran, David M. Blei, Loı̈c M. Roch, Alberto García-Durán, Mansoor Ani Najeeb, Margaret Zeile, Philip W. Nega, Zhi Li, Mina A. Kim, Emory M. Chan, Alexander J. Norquist, Sorelle A. Friedler, Joshua Schrier
Read original paper ↗ ·Serendipity based recommender system for perovskites material discovery: balancing exploration and exploitation across multiple models
ChemRxiv · 2022
Venkateswaran Shekar, Vincent Yu, Benjamin J. Garcia, David Benjamin Gordon, Gemma E. Moran, David M. Blei, Loı̈c M. Roch, Alberto García-Durán, Mansoor Ani Najeeb, Margaret Zeile, Philip W. Nega, Zhi Li, Mina A. Kim, Emory M. Chan, Alexander J. Norquist, Sorelle A. Friedler, Joshua Schrier
Read original paper ↗ ·Machine Learning for Electrocatalyst and Photocatalyst Design and Discovery
Chemical Reviews · 2022
Haoxin Mai, Tu C. Le, Dehong Chen, David Alan Winkler, Rachel A. Caruso
Read original paper ↗ ·Principled Exploration of Bipyridine and Terpyridine Additives to Promote Methylammonium Lead Iodide Perovskite Crystallization
Crystal Growth & Design · 2022
Noor Titan Putri Hartono, Mansoor Ani Najeeb, Zhi Li, Philip W. Nega, Clare A. Fleming, Xiaohe Sun, Emory M. Chan, Antonio Abate, Alexander J. Norquist, Joshua Schrier, Tonio Buonassisi
Read original paper ↗ ·Human- and machine-centred designs of molecules and materials for sustainability and decarbonization
Nature Reviews Materials · 2022
Jiayu Peng, Daniel Schwalbe‐Koda, Karthik Akkiraju, Tian Xie, Livia Giordano, Yang Yu, C. John Eom, Jaclyn R. Lunger, Daniel J. Zheng, Reshma R. Rao, Sokseiha Muy, Jeffrey C. Grossman, Karsten Reuter, Rafael Gómez‐Bombarelli, Yang Shao‐Horn
Read original paper ↗ ·Why big data and compute are not necessarily the path to big materials science
Communications Materials · 2022
Naohiro Fujinuma, Brian DeCost, Jason R. Hattrick-Simpers, S. E. Lofland
Read original paper ↗ ·Artificial intelligence in computational materials science
MRS Bulletin · 2022
Heather J. Kulik, Pratyush Tiwary
Read original paper ↗ ·Training data selection for accuracy and transferability of interatomic potentials
npj Computational Materials · 2022
David Montes de Oca Zapiain, Mitchell A. Wood, Nicholas Lubbers, Carlos Z. Pereyra, Aidan P. Thompson, Danny Pérez
Read original paper ↗ ·Machine learning accelerates the materials discovery
Materials Today Communications · 2022
Jiheng Fang, Ming Xie, Xingqun He, Jiming Zhang, Jieqiong Hu, Yongtai Chen, Youcai Yang, Qinglin Jin
Read original paper ↗ ·Toward autonomous laboratories: Convergence of artificial intelligence and experimental automation
Progress in Materials Science · 2022
Yunchao Xie, Kianoosh Sattari, Chi Zhang, Jian Lin
Read original paper ↗ ·High-Throughput Experimentation for Resource-Efficient Discovery of Methane Functionalization Catalysts
ACS symposium series · 2022
Orion Staples, Jacob S. Mohar, Daniel J. Mindiola
Read original paper ↗ ·Predictive stochastic analysis of massive filter-based electrochemical reaction networks
Digital Discovery · 2022
Daniel Barter, Evan Walter Clark Spotte‐Smith, Nikita S. Redkar, Aniruddh Khanwale, Shyam Dwaraknath, Kristin Aslaug Persson, Samuel M. Blau
Read original paper ↗ ·A catalyst acceleration platform toward realizing the energy transition
Matter · 2022
Marcus Tze‐Kiat Ng, Ahmed S.M. Ismail, Alexander J. S. Hammer
Read original paper ↗ ·Improving molecular machine learning through adaptive subsampling with active learning
Digital Discovery · 2023
Yujing Wen, Zhixiong Li, Yan Xiang, Daniel Reker
Read original paper ↗ ·Leveraging machine learning engineering to uncover insights into heterogeneous catalyst design for oxidative coupling of methane
Catalysis Science & Technology · 2023
Shun Nishimura, Xinyue Li, Junya Ohyama, KEISUKE G. TAKAHASHI
Read original paper ↗ ·Pharmacophore-based virtual screening of large compound databases can aid “big data” problems in drug discovery
Elsevier eBooks · 2023
Apurba K. Bhattacharjee
Read original paper ↗ ·Retrosynthesis from transforms to predictive sustainable chemistry and nanotechnology: a brief tutorial review
Green Chemistry · 2023
Alicja Mikołajczyk, Uladzislau Zhdan, Sylvain Antoniotti, Adam Smoliński, Karolina Jagiełło, Piotr Skurski, Moussab Harb, Tomasz Puzyn, Jarosław Polański
Read original paper ↗ ·Synthesize in a Smart Way: A Brief Introduction to Intelligence and Automation in Organic Synthesis
Challenges and advances in computational chemistry and physics · 2023
Dian‐Zhao Lin, Guichun Fang, Kuangbiao Liao
Read original paper ↗ ·The rise of automated curiosity-driven discoveries in chemistry
Chemical Science · 2023
Latimah Bustillo, Teodoro Laino, Tiago Rodrigues
Read original paper ↗ ·Knowledge-integrated machine learning for materials: lessons from gameplaying and robotics
Nature Reviews Materials · 2023
Kedar Hippalgaonkar, Qianxiao Li, Xiaonan Wang, John W. Fisher, James Kirkpatrick, Tonio Buonassisi
Read original paper ↗ ·Porous Molecular Materials
Venue not supplied by source · 2023
Steven Bennett, Kim E. Jelfs
Read original paper ↗ ·Interpretable Machine Learning Enabled Inorganic Reaction Classification and Synthesis Condition Prediction
Chemistry of Materials · 2023
Christopher J. Karpovich, Elton Pan, Zach Jensen, Elsa Olivetti
Read original paper ↗ ·Machine Learning Scoring Functions for Drug Discovery from Experimental and Computer-Generated Protein–Ligand Structures: Towards Per-Target Scoring Functions
Molecules · 2023
Francesco Pellicani, Diego Dal Ben, Andrea Perali, Sebastiano Pilati
Read original paper ↗ ·Closed-loop optimization of nanoparticle synthesis enabled by robotics and machine learning
Matter · 2023
Jungwon Park, Young‐Min Kim, Seonghun Hong, Byungchan Han, Ki Tae Nam, Yousung Jung
Read original paper ↗ ·The value of negative results in data-driven catalysis research
Nature Catalysis · 2023
Toshiaki Taniike, KEISUKE G. TAKAHASHI
Read original paper ↗ ·Reaction Impurity Prediction using a Data Mining Approach**
Chemistry - Methods · 2023
Adarsh Arun, Zhen Guo, Simon Sung, Alexei A. Lapkin
Read original paper ↗ ·A critical examination of robustness and generalizability of machine learning prediction of materials properties
npj Computational Materials · 2023
Kangming Li, Brian DeCost, Kamal Kumar Choudhary, Michael Greenwood, Jason R. Hattrick-Simpers
Read original paper ↗ ·ET-AL: Entropy-targeted active learning for bias mitigation in materials data
Applied Physics Reviews · 2023
James M. Rondinelli, Wei Chen
Read original paper ↗ ·Introduction to artificial intelligence and deep learning using interactive electronic programming notebooks
Archiv der Pharmazie · 2023
Janosch Menke, Samuel K. R. Homberg, Oliver Koch
Read original paper ↗ ·Combatting over-specialization bias in growing chemical databases
Journal of Cheminformatics · 2023
Katharina Dost, Zac Pullar-Strecker, Liam Brydon, Kunyang Zhang, Jasmin Hafner, Patricia Riddle, Jörg Simon Wicker
Read original paper ↗ ·Auto-MatRegressor: liberating machine learning alchemists
中国科学通报：英文版 · 2023
Yue Liu, Shuangyan Wang, Zhengwei Yang, Maxim Avdeev, Siqi Shi
Read original paper ↗ ·ChemoGraph: Interactive Visual Exploration of the Chemical Space
Computer Graphics Forum · 2023
Bharat Kale, Austin Clyde, Maoyuan Sun, Arvind Ramanathan, Rick L. Stevens, Michael E. Papka
Read original paper ↗ ·Precursor recommendation for inorganic synthesis by machine learning materials similarity from scientific literature
Science Advances · 2023
Tanjin He, Haoyan Huo, Christopher J. Bartel, Zheren Wang, Kevin Cruse, Gerbrand Ceder
Read original paper ↗ ·Machine Learning Strategies for Reaction Development: Toward the Low-Data Limit
Journal of Chemical Information and Modeling · 2023
Eunjae Shim, Ambuj Tewari, Tim Cernak, Paul M. Zimmerman
Read original paper ↗ ·Artificial Intelligence in Evaluation of Permanent Impairment: New Operational Frontiers
Healthcare · 2023
Roberto Scendoni, Luca Tomassini, Mariano Cingolani, Andrea Perali, Sebastiano Pilati, Piergiorgio Fedeli
Read original paper ↗ ·Unified roadmap for ZIF-8 Nucleation and Growth: Machine Learning Analysis of Synthetic Variables and their Impact on Particle Size and Morphology
ChemRxiv · 2023
Juan A. Allegretto, Diego Onna, Laura Ación, Sara A. Bilmes, Omar Azzaroni, Matías Rafti
Read original paper ↗ ·Machine Learning for the Optimization of Chemical Reaction Conditions
Venue not supplied by source · 2023
A. Filipa Almeida, Tiago Rodrigues
Read original paper ↗ ·Data-Driven Imputation of Miscibility of Aqueous Solutions via Graph-Regularized Logistic Matrix Factorization
The Journal of Physical Chemistry B · 2023
Diba Behnoudfar, Cory M. Simon, Joshua Schrier
Read original paper ↗ ·In Pursuit of the Exceptional: Research Directions for Machine Learning in Chemical and Materials Science
Journal of the American Chemical Society · 2023
Joshua Schrier, Alexander J. Norquist, Tonio Buonassisi, Jakoah Brgoch
Read original paper ↗ ·Assessing Thermodynamic Selectivity of Solid-State Reactions for the Predictive Synthesis of Inorganic Materials
ACS Central Science · 2023
Matthew J. McDermott, Brennan C. McBride, Corlyn E. Regier, Gia Thinh Tran, Yu Chen, Adam A. Corrao, Max C. Gallant, Gabrielle E. Kamm, Christopher J. Bartel, Karena W. Chapman, Peter G. Khalifah, Gerbrand Ceder, James Richmond Neilson, Kristin Aslaug Persson
Read original paper ↗ ·VOLTA: a tool for battery screening bridging the gap between virtual electrode materials and practical applications
ChemRxiv · 2023
Antonio Carnevali, M. Rosa Palacín, Clare P. Grey, Alejandro A. Franco
Read original paper ↗ ·Advanced Design of a III‐Nitride Light‐Emitting Diode via Machine Learning
Laser & Photonics Review · 2023
Zhuoying Jiang, Ying Jiang, Mengyu Chen, Jinchai Li, Penggang Li, Binghuan Chen, Shanshan Zhao, Jie Wang, Sijie Jiang, Miaomin Cai, Lin Li, Cheng Li, Kai Huang, Weifang Lu, Junyong Kang, Rong Zhang
Read original paper ↗ ·Exploiting redundancy in large materials datasets for efficient machine learning with less data
Nature Communications · 2023
Kangming Li, Daniel Persaud, Kamal Kumar Choudhary, Brian DeCost, Michael Greenwood, Jason R. Hattrick-Simpers
Read original paper ↗ ·Accelerated chemical science with AI
Digital Discovery · 2023
Seoin Back, Alán Aspuru‐Guzik, Michele Ceriotti, Ganna Gryn’ova, Bartosz A. Grzybowski, Geun Ho Gu, Jason E. Hein, Kedar Hippalgaonkar, Rodrigo Hormázabal, Yousung Jung, Seonah Kim, Woo Youn Kim, Seyed Mohamad Moosavi, Juhwan Noh, Chang-Young Park, Joshua Schrier, Philippe Schwaller, Koji Tsuda, Tejs Vegge, O. Anatole von Lilienfeld, Aron Walsh
Read original paper ↗ ·Artificial intelligence-powered electronic skin
Nature Machine Intelligence · 2023
Changhao Xu, Samuel A. Solomon, Wei Feng Gao
Read original paper ↗ ·Accelerating materials research with a comprehensive data management tool: a case study on an electrochemical laboratory
Journal of Materials Chemistry A · 2024
Nico C. Röttcher, Gun Deniz Akkoc, S.M. Finger, Birk Fritsch, Jonas Möller, Karl J. J. Mayrhofer, Dominik Dworschak
Read original paper ↗ ·Computational chemistry for water-splitting electrocatalysis
Chemical Society Reviews · 2024
Licheng Miao, Wenqi Jia, Xuejie Cao, Lifang Jiao
Read original paper ↗ ·Learning the Next Experiment: A Decision Architecture for Closed-Loop Drug Discovery under Cost, Uncertainty, and Scientific Value
Pharmacophore · 2024
Daniel Osei, Koenraad Janssens, Akua Asare, Mohammed El-Sayed
Read original paper ↗ ·Probing machine learning models based on high throughput experimentation data for the discovery of asymmetric hydrogenation catalysts
Chemical Science · 2024
Adarsh V. Kalikadien, Cecile Valsecchi, Robbert van Putten, Tor Maes, Mikko Muuronen, Natalia Dyubankova, Laurent Lefort, Evgeny A. Pidko
Read original paper ↗ ·Paving the road towards automated homogeneous catalyst design
ChemPlusChem · 2024
Adarsh V. Kalikadien, Adrian Mirza, Aydin Najl Hossaini, A. Sreenithya, Evgeny A. Pidko
Read original paper ↗ ·