Papers citing Optimizing Chemical Reactions with Deep Reinforcement Learning
Chimera: enabling hierarchy based multi-objective optimization for self-driving laboratories
Chemical Science · 2018
Florian Häse, Loı̈c M. Roch, Alán Aspuru‐Guzik
Read original paper ↗ ·Machine learning for predicting product distributions in catalytic regioselective reactions
Physical Chemistry Chemical Physics · 2018
Sayan Banerjee, A. Sreenithya, Raghavan B. Sunoj
Read original paper ↗ ·Towards a continuous formic acid synthesis: a two-step carbon dioxide hydrogenation in flow
Reaction Chemistry & Engineering · 2018
Helena Reymond, Juan José Corral‐Pérez, Atsushi Urakawa, Philipp Rudolf von Rohr
Read original paper ↗ ·Towards operando computational modeling in heterogeneous catalysis
Chemical Society Reviews · 2018
Lukáš Grajciar, Christopher James Heard, A. A. Bondarenko, Mikhail V. Polynski, Jittima Meeprasert, Evgeny A. Pidko, Petr Nachtigall
Read original paper ↗ ·Tuning the molecular weight distribution from atom transfer radical polymerization using deep reinforcement learning
Molecular Systems Design & Engineering · 2018
Haichen Li, Christopher R. Collins, Thomas G. Ribelli, Krzysztof Aleksander Matyjaszewski, Geoffrey J. Gordon, Tomasz Kowalewski, David Yaron
Read original paper ↗ ·Accelerating the discovery of materials for clean energy in the era of smart automation
Nature Reviews Materials · 2018
Daniel P. Tabor, Loı̈c M. Roch, Semion K. Saikin, Christoph Kreisbeck, Dennis Sheberla, Joseph H. Montoya, Shyam Dwaraknath, Muratahan Aykol, C. Ortiz, Hermann Tribukait, Carlos Amador‐Bedolla, Christoph Josef Brabec, Benji Maruyama, Kristin Aslaug Persson, Alán Aspuru‐Guzik
Read original paper ↗ ·Machine Learning for Organic Synthesis: Are Robots Replacing Chemists?
Angewandte Chemie International Edition · 2018
Boris Maryasin, Philipp Marquetand, Nuno Maulide
Read original paper ↗ ·Maschinelles Lernen für die organische Synthese: Ersetzen Roboter Chemiker?
Angewandte Chemie · 2018
Boris Maryasin, Philipp Marquetand, Nuno Maulide
Read original paper ↗ ·Combining theory and experiment in lithium–sulfur batteries: Current progress and future perspectives
Materials Today · 2018
Xiang Chen, Tingzheng Hou, Kristin Aslaug Persson, Qiang Zhang
Read original paper ↗ ·Deeply learning molecular structure-property relationships using attention- and gate-augmented graph convolutional network
arXiv (Cornell University) · 2018
Seongok Ryu, Jaechang Lim, Seung Hwan Hong, Woo Youn Kim
Read original paper ↗ ·Deeply learning molecular structure-property relationships using graph attention neural network.
arXiv (Cornell University) · 2018
Seongok Ryu, Jaechang Lim, Woo Youn Kim
Read original paper ↗ ·Globally Optimal Catalytic Fields – Inverse Design of Abstract Embeddings for Maximum Reaction Rate Acceleration
Journal of Chemical Theory and Computation · 2018
Mark Dittner, Bernd Hartke
Read original paper ↗ ·ChemOS: Orchestrating autonomous experimentation
Science Robotics · 2018
Loı̈c M. Roch, Florian Häse, Christoph Kreisbeck, Teresa Tamayo-Mendoza, Lars P. E. Yunker, Jason E. Hein, Alán Aspuru‐Guzik
Read original paper ↗ ·Inverse molecular design using machine learning: Generative models for matter engineering
Science · 2018
Benjamín Sánchez-Lengeling, Alán Aspuru‐Guzik
Read original paper ↗ ·ACS Central Science Virtual Issue on Machine Learning
ACS Central Science · 2018
Andrew L. Ferguson
Read original paper ↗ ·Sparsity Facilitates Chemical-Reaction Selection for Engine Simulations
The Journal of Physical Chemistry A · 2018
Gina M. Magnotti, Zihan Wang, Wei Liu, Raghu Sivaramakrishnan, Sibendu Som, Michael J. Davis
Read original paper ↗ ·Phoenics: A Bayesian Optimizer for Chemistry
ACS Central Science · 2018
Florian Häse, Loı̈c M. Roch, Christoph Kreisbeck, Alán Aspuru‐Guzik
Read original paper ↗ ·Error-Controlled Exploration of Chemical Reaction Networks with Gaussian Processes
Journal of Chemical Theory and Computation · 2018
Gregor N. C. Simm, Markus Reiher
Read original paper ↗ ·Deep reinforcement learning for the control of microbial co-cultures in bioreactors
bioRxiv (Cold Spring Harbor Laboratory) · 2018
Neythen J. Treloar, Alex J. H. Fedorec, Brian P. Ingalls, Chris P. Barnes
Read original paper ↗ ·AIR-Chem: Authentic Intelligent Robotics for Chemistry
The Journal of Physical Chemistry A · 2018
Jiagen Li, Yihua Lu, Yanheng Xu, Chongfeng Liu, Yuxiao Tu, Shuqian Ye, Haochen Liu, Yi Xie, Huihuan Qian, Xi Zhu
Read original paper ↗ ·Prediction of Major Regio‐, Site‐, and Diastereoisomers in Diels–Alder Reactions by Using Machine‐Learning: The Importance of Physically Meaningful Descriptors
Angewandte Chemie International Edition · 2018
Wiktor Beker, Ewa P. Gajewska, Tomasz Badowski, Bartosz A. Grzybowski
Read original paper ↗ ·Prediction of Major Regio‐, Site‐, and Diastereoisomers in Diels–Alder Reactions by Using Machine‐Learning: The Importance of Physically Meaningful Descriptors
Angewandte Chemie · 2018
Wiktor Beker, Ewa P. Gajewska, Tomasz Badowski, Bartosz A. Grzybowski
Read original paper ↗ ·Uncertainty quantification of molecular property prediction using Bayesian neural network models
arXiv (Cornell University) · 2018
Seongok Ryu, Yongchan Kwon, Woo Youn Kim
Read original paper ↗ ·High Throughput Strategies for the Discovery and Optimization of Catalytic Reactions
Angewandte Chemie International Edition · 2018
Eric S. Isbrandt, Ryan J. Sullivan, Stephen G. Newman
Read original paper ↗ ·Hochdurchsatzstrategien zur Entdeckung und Optimierung katalytischer Reaktionen
Angewandte Chemie · 2018
Eric S. Isbrandt, Ryan J. Sullivan, Stephen G. Newman
Read original paper ↗ ·Using Reinforcement Learning to find Efficient Qubit Routing Policies for Deployment in Near-term Quantum Computers
arXiv (Cornell University) · 2018
Steven Herbert, Akash Sengupta
Read original paper ↗ ·A Bayesian graph convolutional network for reliable prediction of molecular properties with uncertainty quantification
Chemical Science · 2019
Seongok Ryu, Yongchan Kwon, Woo Youn Kim
Read original paper ↗ ·A microdroplet-accelerated Biginelli reaction: mechanisms and separation of isomers using IMS-MS
Chemical Science · 2019
Navneet Sahota, Deyaa I. AbuSalim, Melinda L. Wang, Christopher J. Brown, Zhicaho Zhang, Tarick J. El‐Baba, Silas P. Cook, David E. Clemmer
Read original paper ↗ ·Automated platforms for reaction self-optimization in flow
Reaction Chemistry & Engineering · 2019
Carlos Mateos, María José Nieves‐Remacha, Juan A. Rincón
Read original paper ↗ ·Decentralized manufacturing for biomimetics through cooperation of digitization and nanomaterial design
Nanoscale · 2019
Xiao Meng, Hui Yang, Quanlan Xiao, Han Zhang, Xiaodong Chen
Read original paper ↗ ·Machine Learning for Understanding Compatibility of Organic–Inorganic Hybrid Perovskites with Post-Treatment Amines
ACS Energy Letters · 2019
Yongze Yu, Xuanheng Tan, Shougui Ning, Yiying Wu
Read original paper ↗ ·Enhancing Retrosynthetic Reaction Prediction with Deep Learning Using Multiscale Reaction Classification
Journal of Chemical Information and Modeling · 2019
Javier L. Baylon, Nicholas A. Cilfone, Jeffrey R. Gulcher, Thomas W. Chittenden
Read original paper ↗ ·How to explore chemical space using algorithms and automation
Nature Reviews Chemistry · 2019
Piotr S. Gromski, Alon Henson, Jarosław M. Granda, Leroy Cronin
Read original paper ↗ ·Prediction of higher-selectivity catalysts by computer-driven workflow and machine learning
Science · 2019
Andrew F. Zahrt, Jeremy J. Henle, Brennan T. Rose, Yang Wang, William T. Darrow, Scott E. Denmark
Read original paper ↗ ·Capturing chemical intuition in synthesis of metal-organic frameworks
Nature Communications · 2019
Seyed Mohamad Moosavi, Arunraj Chidambaram, Leopold Talirz, Maciej Harańczyk, Kyriakos C. Stylianou, Berend Smit
Read original paper ↗ ·Nanomaterials Discovery and Design through Machine Learning
Small Methods · 2019
Ming Wang, Ting Wang, Pingqiang Cai, Xiaodong Chen
Read original paper ↗ ·Next-Generation Experimentation with Self-Driving Laboratories
Trends in Chemistry · 2019
Florian Häse, Loı̈c M. Roch, Alán Aspuru‐Guzik
Read original paper ↗ ·From Complexity to Simplicity: Adaptive ES-Active Subspaces for Blackbox Optimization
arXiv (Cornell University) · 2019
Krzysztof Choromański, Aldo Pacchiano, Jack Parker-Holder, Yunhao Tang
Read original paper ↗ ·Miniaturized and Automated Synthesis of Biomolecules—Overview and Perspectives
Advanced Materials · 2019
Daniela S. Mattes, Nicole Jung, Laura K. Weber, Stefan Bräse, Frank Breitling
Read original paper ↗ ·A reinforcement learning unit matching recurrent neural network for the state trend prediction of rolling bearings
Measurement · 2019
Li Feng, Yong Chen, Jiaxu Wang, Xueming Zhou, Baoping Tang
Read original paper ↗ ·Virtual Reaction Condition Optimization based on Machine Learning for a Small Number of Experiments in High-dimensional Continuous and Discrete Variables
Chemistry Letters · 2019
Mikito Fujinami, Junji Seino, Takumi Nukazawa, Shintaro Ishida, Takeaki Iwamoto, Hiromi Nakai
Read original paper ↗ ·Experiment Specification, Capture and Laboratory Automation Technology (ESCALATE): a software pipeline for automated chemical experimentation and data management
MRS Communications · 2019
Ian M. Pendleton, Gary Cattabriga, Zhi Li, Mansoor Ani Najeeb, Sorelle A. Friedler, Alexander J. Norquist, Emory M. Chan, Joshua Schrier
Read original paper ↗ ·Deep Learning in Chemistry
Journal of Chemical Information and Modeling · 2019
Adam C. Mater, Michelle L. Coote
Read original paper ↗ ·Efficient Navigation of Active Particles in an Unseen Environment via Deep Reinforcement Learning.
arXiv (Cornell University) · 2019
Yuguang Yang, Michael A. Bevan, Bo Li
Read original paper ↗ ·Statistical Analysis and Discovery of Heterogeneous Catalysts Based on Machine Learning from Diverse Published Data
ChemCatChem · 2019
Keisuke Suzuki, Takashi Toyao, Zen Maeno, Satoru Takakusagi, Ken‐ichi Shimizu, Ichigaku Takigawa
Read original paper ↗ ·Advancing Drug Discovery via Artificial Intelligence
Trends in Pharmacological Sciences · 2019
H. C. Stephen Chan, Hanbin Shan, Thamani Dahoun, Horst Vogel, Shuguang Yuan
Read original paper ↗ ·Deep learning in drug discovery: opportunities, challenges and future prospects
Drug Discovery Today · 2019
Antonio Lavecchia
Read original paper ↗ ·Synthetic organic chemistry driven by artificial intelligence
Nature Reviews Chemistry · 2019
A. Filipa Almeida, Rui Moreira, Tiago Rodrigues
Read original paper ↗ ·Machine learning in materials science
InfoMat · 2019
Jing Wei, Xuan Chu, Xiangyu Sun, Kun Xu, Hui‐Xiong Deng, Ji-Gen Chen, Zhongming Wei, Ming Lei
Read original paper ↗ ·A deep neural network combined with molecular fingerprints (DNN-MF) to develop predictive models for hydroxyl radical rate constants of water contaminants
Journal of Hazardous Materials · 2019
Shifa Zhong, Jiajie Hu, Xudong Fan, Xiong Bill Yu, Huichun Judy Zhang
Read original paper ↗ ·