Papers citing Inverse molecular design using machine learning: Generative models for matter engineering
Autonomous Discovery in the Chemical Sciences Part I: Progress
Angewandte Chemie International Edition · 2019
Connor W. Coley, Natalie S. Eyke, Klavs F. Jensen
Read original paper ↗ ·Autonomous Discovery in the Chemical Sciences Part II: Outlook
Angewandte Chemie International Edition · 2019
Connor W. Coley, Natalie S. Eyke, Klavs F. Jensen
Read original paper ↗ ·LIA: Latently Invertible Autoencoder with Adversarial Learning
arXiv (Cornell University) · 2019
Jiapeng Zhu, Deli Zhao, Bolei Zhou, Bo Zhang
Read original paper ↗ ·Machine Learning Applied to Zeolite Synthesis: The Missing Link for Realizing High-Throughput Discovery
Accounts of Chemical Research · 2019
Manuel Moliner, Yuriy Román‐Leshkov, Avelino Corma
Read original paper ↗ ·Adversarial-residual-coarse-graining: Applying machine learning theory to systematic molecular coarse-graining
The Journal of Chemical Physics · 2019
Aleksander E. P. Durumeric, Gregory A. Voth
Read original paper ↗ ·Efficiently measuring a quantum device using machine learning
npj Quantum Information · 2019
D. T. Lennon, H. Moon, L. C. Camenzind, Liuqi Yu, Dominik M. Zumbühl, G. A .D. Briggs, M. A. Osborne, Edward A. Laird, N. Ares
Read original paper ↗ ·Efficiently measuring a quantum device using machine learning
Lancaster EPrints (Lancaster University) · 2019
Lennon, D. T., Moon, H., Camenzind, L. C., Yu, Liuqi, Dominik M. Zumbühl, Briggs, G. A. D., Osborne, M. A., Edward A. Laird, Ares, N.
Read original paper ↗ ·Quantum Materials with Atomic Precision: Artificial Atoms in Solids: Ab Initio Design, Control, and Integration of Single Photon Emitters in Artificial Quantum Materials
Advanced Functional Materials · 2019
Prineha Narang, Christopher J. Ciccarino, Johannes Flick, Dirk Englund
Read original paper ↗ ·Interpretable deep learning for guided microstructure-property explorations in photovoltaics
npj Computational Materials · 2019
Balaji Sesha Sarath Pokuri, Sambuddha Ghosal, Apurva Kokate, Soumik Sarkar, Baskar Ganapathysubramanian
Read original paper ↗ ·Inverse Design of Solid-State Materials via a Continuous Representation
Matter · 2019
Juhwan Noh, Jaehoon Kim, Helge Sören Stein, Benjamín Sánchez-Lengeling, John M. Gregoire, Alán Aspuru‐Guzik, Yousung Jung
Read original paper ↗ ·Blind motion deblurring with cycle generative adversarial networks
The Visual Computer · 2019
Quan Yuan, Junxia Li, Lingwei Zhang, Zhefu Wu, Guangyu Liu
Read original paper ↗ ·Virtual Materials Intelligence for Design and Discovery of Advanced Electrocatalysts
ChemPhysChem · 2019
Ali Malek, Mohammad J. Eslamibidgoli, Mehrdad Mokhtari, Qianpu Wang, Michael Hermann Eikerling, Kourosh Malek
Read original paper ↗ ·Robust data-driven approach for predicting the configurational energy of high entropy alloys
Materials & Design · 2019
Jiaxin Zhang, Xianglin Liu, Sirui Bi, Junqi Yin, Guannan Zhang, Markus Eisenbach
Read original paper ↗ ·Aluminum alloy microstructural segmentation in micrograph with hierarchical parameter transfer learning method
Journal of Electronic Imaging · 2019
Dali Chen, Pengyuan Zhang, Shixin Liu, YangQuan Chen, Wei Zhao
Read original paper ↗ ·A high throughput molecular screening for organic electronics via machine learning: present status and perspective
Japanese Journal of Applied Physics · 2019
Akinori Saeki, Kakaraparthi Kranthiraja
Read original paper ↗ ·Regression Clustering for Improved Accuracy and Training Costs with Molecular-Orbital-Based Machine Learning
Journal of Chemical Theory and Computation · 2019
Lixue Cheng, Nikola Kovachki, Matthew Welborn, Thomas F. Miller
Read original paper ↗ ·Study of Deep Generative Models for Inorganic Chemical Compositions
arXiv (Cornell University) · 2019
Yoshihide Sawada, Morikawa, Koji, Mikiya Fujii
Read original paper ↗ ·Deep Learning to Generate in Silico Chemical Property Libraries and Candidate Molecules for Small Molecule Identification in Complex Samples
Analytical Chemistry · 2019
Sean Colby, Jamie Nuñez, Nathan O. Hodas, Courtney D. Corley, Ryan Scott Renslow
Read original paper ↗ ·Thinking Globally, Acting Locally: On the Issue of Training Set Imbalance and the Case for Local Machine Learning Models in Chemistry
ChemRxiv · 2019
Mojtaba Haghighatlari, Ching-Yen Shih, Johannes Hachmann
Read original paper ↗ ·iQSPR in XenonPy: A Bayesian Molecular Design Algorithm
Molecular Informatics · 2019
Stephen Wu, G. Lambard, Chang Liu, Hironao Yamada, Ryo Yoshida
Read original paper ↗ ·Designing Catalytic Sites on Surfaces with Optimal H-Atom Binding via Atom Doping Using the Inverse Molecular Design Approach
The Journal of Physical Chemistry B · 2019
Trevor Callahan, Daniel Masi, Dequan Xiao
Read original paper ↗ ·Unraveling Correlations between Molecular Properties and Device Parameters of Organic Solar Cells Using Machine Learning
The Journal of Physical Chemistry Letters · 2019
Harikrishna Sahu, Haibo Ma
Read original paper ↗ ·Convolutional Neural Networks for the Design and Analysis of Non-Fullerene Acceptors
Journal of Chemical Information and Modeling · 2019
Shi-Ping Peng, Yi Zhao
Read original paper ↗ ·Constrained Bayesian optimization for automatic chemical design using variational autoencoders
Chemical Science · 2019
Ryan‐Rhys Griffiths, José Miguel Hernández-Lobato
Read original paper ↗ ·Machine Learning Coupled Multi‐Scale Modeling for Redox Flow Batteries
Advanced Theory and Simulations · 2019
Jie Bao, Vijayakumar Murugesan, Carl Justin Kamp, Yuyan Shao, Litao Yan, Wei Wang
Read original paper ↗ ·Designing phononic crystal with anticipated band gap through a deep learning based data-driven method
Computer Methods in Applied Mechanics and Engineering · 2019
Xiang Li, Shaowu Ning, Zhanli Liu, Ziming Yan, Chengcheng Luo, Zhuo Zhuang
Read original paper ↗ ·Efficient learning of non-autoregressive graph variational autoencoders for molecular graph generation
Journal of Cheminformatics · 2019
Youngchun Kwon, Jiho Yoo, Youn-Suk Choi, Won‐Joon Son, Dongseon Lee, Seokho Kang
Read original paper ↗ ·Integrating machine learning and multiscale modeling—perspectives, challenges, and opportunities in the biological, biomedical, and behavioral sciences
npj Digital Medicine · 2019
Mark Alber, Adrián Buganza Tepole, William R. Cannon, Suvranu De, Salvador Durá-Bernal, Krishna C. Garikipati, George Em Karniadakis, William W. Lytton, Paris G. Perdikaris, Linda Ruth Petzold, Ellen Kuhl
Read original paper ↗ ·Deep Learning for Deep Chemistry: Optimizing the Prediction of Chemical Patterns
Frontiers in Chemistry · 2019
Tânia F. G. G. Cova, Alberto A. C. C. Pais
Read original paper ↗ ·On failure modes in molecule generation and optimization
Drug Discovery Today Technologies · 2019
Philipp Renz, Dries Van Rompaey, Jörg K. Wegner, Sepp Hochreiter, Günter Klambauer
Read original paper ↗ ·Scaffold-based molecular design with a graph generative model
Chemical Science · 2019
Jaechang Lim, Sang-Yeon Hwang, Seokhyun Moon, Seungsu Kim, Woo Youn Kim
Read original paper ↗ ·Exploration of flow reaction conditions using machine-learning for enantioselective organocatalyzed Rauhut–Currier and [3+2] annulation sequence
Chemical Communications · 2019
Masaru Kondo, H. D. P. Wathsala, Makoto Sako, Yutaro Hanatani, Kazunori Ishikawa, Satoshi Hara, Takayuki Takaai, Takashi Washio, Shinobu Takizawa, Hiroaki Sasai
Read original paper ↗ ·Machine Learning Enabled Tailor-Made Design of Application-Specific Metal–Organic Frameworks
ACS Applied Materials & Interfaces · 2019
Xiangyu Zhang, Kexin Zhang, Yongjin Lee
Read original paper ↗ ·Molecular Generative Model Based on an Adversarially Regularized Autoencoder
Journal of Chemical Information and Modeling · 2019
Seung Hwan Hong, Seongok Ryu, Jaechang Lim, Woo Youn Kim
Read original paper ↗ ·Machine Learning for Catalysis Informatics: Recent Applications and Prospects
ACS Catalysis · 2019
Takashi Toyao, Zen Maeno, Satoru Takakusagi, Takashi Kamachi, Ichigaku Takigawa, Ken‐ichi Shimizu
Read original paper ↗ ·De novo Molecular Design with Generative Long Short-term Memory
CHIMIA International Journal for Chemistry · 2019
Francesca Grisoni, Gisbert Schneider
Read original paper ↗ ·Compounding Meta‐Atoms into Metamolecules with Hybrid Artificial Intelligence Techniques
Advanced Materials · 2019
Zhaocheng Liu, Dayu Zhu, Kyu‐Tae Lee, Andrew S. Kim, Lakshmi Raju, Wenshan Cai
Read original paper ↗ ·Molecular Generation Targeting Desired Electronic Properties via Deep Generative Models
ChemRxiv · 2019
Yuan Qi, Alejandro Santana‐Bonilla, Martijn A. Zwijnenburg, Kim E. Jelfs
Read original paper ↗ ·A machine-learning-assisted study of the permeability of small drug-like molecules across lipid membranes
Physical Chemistry Chemical Physics · 2020
Guang Chen, Zhiqiang Shen, Ying Li
Read original paper ↗ ·cheML.io: an online database of ML-generated molecules
RSC Advances · 2020
Rustam Zhumagambetov, Daniyar Kazbek, Mansur Shakipov, Daulet Maksut, Vsevolod A. Peshkov, Siamac Fazli
Read original paper ↗ ·Correction: Ensemble-machine-learning-based correlation analysis of internal and band characteristics of thermoelectric materials
Journal of Materials Chemistry C · 2020
Lihao Chen, Ben Xu, Jia Chen, Ke Bi, Changjiao Li, Shengyu Lu, Guo-Sheng Hu, Yuanhua Lin
Read original paper ↗ ·Correction: Exploration of flow reaction conditions using machine-learning for enantioselective organocatalyzed Rauhut–Currier and [3+2] annulation sequence
Chemical Communications · 2020
Masaru Kondo, H. D. P. Wathsala, Makoto Sako, Yutaro Hanatani, Kazunori Ishikawa, Satoshi Hara, Takayuki Takaai, Takashi Washio, Shinobu Takizawa, Hiroaki Sasai
Read original paper ↗ ·Deep learning enabled inorganic material generator
Physical Chemistry Chemical Physics · 2020
Yashaswi Pathak, Karandeep Singh Juneja, Girish Varma, Masahiro Ehara, U. Deva Priyakumar
Read original paper ↗ ·Deep Neural Networks for Designing Integrated Photonics
Optical Fiber Communication Conference · 2020
Keisuke Kojima, Mohammad Hossein Tahersima, Toshiaki Koike–Akino, Devesh K. Jha, Yingheng Tang, Ye Wang, Kieran Parsons, Fengqiao Sang, Jonathan Klamkin
Read original paper ↗ ·Dramatically Enhanced Combination of Ultimate Tensile Strength and Electric Conductivity of Alloys via Machine Learning Screening
Acta Materialia · 2020
Hongtao Zhang, Huadong Fu, Xingqun He, Changsheng Wang, Lei Jiang, Long‐Qing Chen, Jianxin Xie
Read original paper ↗ ·Ensemble-machine-learning-based correlation analysis of internal and band characteristics of thermoelectric materials
Journal of Materials Chemistry C · 2020
Lihao Chen, Ben Xu, Jia Chen, Ke Bi, Changjiao Li, Shengyu Lu, Guo-Sheng Hu, Yuanhua Lin
Read original paper ↗ ·Generative Models for Automatic Chemical Design
Lecture notes in physics · 2020
Daniel Schwalbe‐Koda, Rafael Gómez‐Bombarelli
Read original paper ↗ ·Inverse Design of Nanophotonic Devices using Deep Neural Networks
Venue not supplied by source · 2020
Keisuke Kojima, Yingheng Tang, Toshiaki Koike–Akino, Ye Wang, Devesh K. Jha, Kieran Parsons, Mohammad Hossein Tahersima, Fengqiao Sang, Jonathan Klamkin, Minghao Qi
Read original paper ↗ ·Machine learned prediction of reaction template applicability for data-driven retrosynthetic predictions of energetic materials
AIP conference proceedings · 2020
Michael William-Patrick Fortunato, Connor W. Coley, Brian C. Barnes, Klavs F. Jensen
Read original paper ↗ ·Machine learning dihydrogen activation in the chemical space surrounding Vaska's complex
Chemical Science · 2020
Pascal Friederich, Gabe Gomes, Riccardo De Bin, Alán Aspuru‐Guzik, David Balcells
Read original paper ↗ ·