Papers citing Inverse molecular design using machine learning: Generative models for matter engineering
Machine Learning in Drug Discovery
Journal of Chemical Information and Modeling · 2019-03-25
Günter Klambauer, Sepp Hochreiter, Matthias Rarey
Read original paper ↗ ·Equivalence between dropout and data augmentation: A mathematical check
Neural Networks · 2019-03-27
Dazhi Zhao, Guozhu Yu, Peng Xu, Maokang Luo
Read original paper ↗ ·A Comparative Study of the Efficacy of Local/Global and Parametric/Nonparametric Machine Learning Methods for Establishing Structure–Property Linkages in High-Contrast 3D Elastic Composites
Integrating materials and manufacturing innovation · 2019-03-28
Patxi Fernandez-Zelaia, Yuksel C. Yabansu, Surya R. Kalidindi
Read original paper ↗ ·Optimization under uncertainty in the era of big data and deep learning: When machine learning meets mathematical programming
Computers & Chemical Engineering · 2019-03-28
Chao Ning, Fengqi You
Read original paper ↗ ·A universal density matrix functional from molecular orbital-based machine learning: Transferability across organic molecules
The Journal of Chemical Physics · 2019-04-04
Lixue Cheng, Matthew Welborn, Anders S. Christensen, Thomas F. Miller
Read original paper ↗ ·Deep learning in bioinformatics: Introduction, application, and perspective in the big data era
Methods · 2019-04-22
Yu Li, Chao Huang, Lizhong Ding, Zhongxiao Li, Yijie Pan, Xin Gao
Read original paper ↗ ·Recognition of polymer configurations by unsupervised learning
Physical review. E · 2019-04-22
Xin Xu, Qianshi Wei, Huaping Li, Yuzhang Wang, Yuguo Chen, Ying Lan Jiang
Read original paper ↗ ·Toward Design of Novel Materials for Organic Electronics
Advanced Materials · 2019-04-22
Pascal Friederich, Artem Fediai, Simon Kaiser, Manuel Konrad, Nicole Jung, Wolfgang Wenzel
Read original paper ↗ ·FP2VEC: a new molecular featurizer for learning molecular properties
Bioinformatics · 2019-04-25
Woosung Jeon, Dongsup Kim
Read original paper ↗ ·Improved Chemical Prediction from Scarce Data Sets via Latent Space Enrichment
The Journal of Physical Chemistry A · 2019-04-29
Nicolae C. Iovanac, Brett Matthew Savoie
Read original paper ↗ ·Search for Catalysts by Inverse Design: Artificial Intelligence, Mountain Climbers, and Alchemists
Chemical Reviews · 2019-05-06
Jessica G. Freeze, H. Ray Kelly, Víctor S. Batista
Read original paper ↗ ·Machine learning for the modeling of interfaces in energy storage and conversion materials
Journal of Physics Energy · 2019-05-09
Nongnuch Artrith
Read original paper ↗ ·Style transfer based data augmentation in material microscopic image processing.
arXiv (Cornell University) · 2019-05-12
Boyuan Ma, Xiaoyan Wei, Chuni Liu, Xiaojuan Ban, Haiyou Huang, Hao Henry Wang, Weihua Xue
Read original paper ↗ ·All SMILES Variational Autoencoder
arXiv (Cornell University) · 2019-05-30
Zaccary Alperstein, Artem R Cherkasov, Jason Tyler Rolfe
Read original paper ↗ ·SELFIES: a robust representation of semantically constrained graphs with an example application in chemistry.
arXiv (Cornell University) · 2019-05-31
Mario Krenn, Florian Häse, AkshatKumar Nigam, Pascal Friederich, Alán Aspuru‐Guzik
Read original paper ↗ ·The digitization of organic synthesis
Nature · 2019-06-01
Ian W. Davies
Read original paper ↗ ·Experiment Specification, Capture and Laboratory Automation Technology (ESCALATE): a software pipeline for automated chemical experimentation and data management
MRS Communications · 2019-06-04
Ian M. Pendleton, Gary Cattabriga, Zhi Li, Mansoor Ani Najeeb, Sorelle A. Friedler, Alexander J. Norquist, Emory M. Chan, Joshua Schrier
Read original paper ↗ ·Comparative Exploratory Analysis of Intrinsically Disordered Protein Dynamics Using Machine Learning and Network Analytic Methods
Frontiers in Molecular Biosciences · 2019-06-12
Gianmarc Grazioli, Rachel W. Martin, Carter T. Butts
Read original paper ↗ ·Deep Learning in Chemistry
Journal of Chemical Information and Modeling · 2019-06-13
Adam C. Mater, Michelle L. Coote
Read original paper ↗ ·A perspective on inverse design of battery interphases using multi-scale modelling, experiments and generative deep learning
Energy storage materials · 2019-06-18
Arghya Bhowmik, Ivano E. Castelli, Juan María García-Lastra, Peter Bjørn Jørgensen, Ole Winther, Tejs Vegge
Read original paper ↗ ·Deep Reinforcement Learning for Multiparameter Optimization in de novo Drug Design
Journal of Chemical Information and Modeling · 2019-06-19
Niclas Ståhl, Göran Falkman, Alexander Karlsson, Gunnar Mathiason, Jonas Boström
Read original paper ↗ ·Machine-learning-assisted discovery of polymers with high thermal conductivity using a molecular design algorithm
npj Computational Materials · 2019-06-21
Stephen Wu, Yukiko Kondo, Masa‐aki Kakimoto, Bin Yang, Hironao Yamada, Isao Kuwajima, G. Lambard, Kenta Hongo, Yibin Xu, Junichiro Shiomi, Christoph H. Schick, Junko Morikawa, Ryo Yoshida
Read original paper ↗ ·Alchemy: A Quantum Chemistry Dataset for Benchmarking AI Models
arXiv (Cornell University) · 2019-06-22
Guangyong Chen, Pengfei Chen, Chang‐Yu Hsieh, Chee‐Kong Lee, Ben Liao, Renjie Liao, Weiwen Liu, Jiezhong Qiu, Qiming Sun, Jie Tang, Richard S. Zemel, Shengyu Zhang
Read original paper ↗ ·Artificial intelligence for materials discovery
MRS Bulletin · 2019-07-01
Carla Pedro Gomes, Bart Selman, John M. Gregoire
Read original paper ↗ ·Probabilistic Representation and Inverse Design of Metamaterials Based on a Deep Generative Model with Semi‐Supervised Learning Strategy
Advanced Materials · 2019-07-01
Wei Ma, Feng Cheng, Yihao Xu, Qinlong Wen, Yongmin Liu
Read original paper ↗ ·Machine learning approaches for drug virtual screening
Unknown journal · 2019-07-02
Benoît Playe
Read original paper ↗ ·Enumeration of de novo inorganic complexes for chemical discovery and machine learning
Molecular Systems Design & Engineering · 2019-07-04
Stefan Gugler, Jon Paul Janet, Heather J. Kulik
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 · 2019-07-08
Mojtaba Haghighatlari, Ching-Yen Shih, Johannes Hachmann
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 · 2019-07-08
Mojtaba Haghighatlari, Ching-Yen Shih, Johannes Hachmann
Read original paper ↗ ·Statistical Analysis and Discovery of Heterogeneous Catalysts Based on Machine Learning from Diverse Published Data
ChemCatChem · 2019-07-09
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 · 2019-07-15
H. C. Stephen Chan, Hanbin Shan, Thamani Dahoun, Horst Vogel, Shuguang Yuan
Read original paper ↗ ·Ion Transport in Porous Electrodes Obtained by Impedance Using a Symmetric Cell with Predictable Low-Temperature Battery Performance
The Journal of Physical Chemistry Letters · 2019-07-21
Nobuhiro Ogihara, Yuichi Itou, Shigehiro Kawauchi
Read original paper ↗ ·Embedding Physics Domain Knowledge into a Bayesian Network Enables Layer-by-Layer Process Innovation for Photovoltaics
arXiv (Cornell University) · 2019-07-25
Zekun Ren, Felipe Oviedo, Muang Thway, Siyu Isaac Parker Tian, Yue Wang, Hansong Xue, José Darío Perea, Mariya Layurova, Thomas Heumueller, Erik Birgersson, Armin Gerhard Aberle, Christoph Josef Brabec, Rolf Stangl, Shijing Sun, Qianxiao Li, Fen Lin, Ian Marius Peters, Tonio Buonassisi
Read original paper ↗ ·Structural phase transitions in a MoWSe2 monolayer: Molecular dynamics simulations and variational autoencoder analysis
Physical review. B./Physical review. B · 2019-07-30
Pankaj Rajak, Aravind Krishnamoorthy, Aiichiro Nakano, Priya D. Vashishta, Rajiv K. Kalia
Read original paper ↗ ·A Case Study of Deep Reinforcement Learning for Engineering Design: Application to Microfluidic Devices for Flow Sculpting
Journal of Mechanical Design · 2019-08-01
Xian Yeow Lee, Aditya Balu, Daniel Stoecklein, Baskar Ganapathysubramanian, Soumik Sarkar
Read original paper ↗ ·Fast, efficient fragment-based coordinate generation for Open Babel
Journal of Cheminformatics · 2019-08-01
Naruki Yoshikawa, Geoffrey Hutchison
Read original paper ↗ ·Accurate and transferable multitask prediction of chemical properties with an atoms-in-molecules neural network
Science Advances · 2019-08-02
R.I. Zubatyuk, Justin S. Smith, Jerzy Leszczyński, Olexandr Isayev
Read original paper ↗ ·Energy–Geometry Dependency of Molecular Structures: A Multistep Machine Learning Approach
ACS Combinatorial Science · 2019-08-07
Ehsan Moharreri, Maryam Pardakhti, Ranjan Srivastava, Steven L. Suib
Read original paper ↗ ·Inverse Structural Design of Graphene/Boron Nitride Hybrids by Regressional GAN
arXiv (Cornell University) · 2019-08-21
Yuan Dong, Dawei Li, Chi Zhang, Chuhan Wu, Hong Wang, Xin, Ming, Jianlin Jack Cheng, Jian Lin
Read original paper ↗ ·Report of the Basic Energy Sciences Roundtable on Liquid Solar Fuels
Unknown journal · 2019-08-21
Basic Energy Sciences Roundtable, Bill Tumas, Jillian Lee Dempsey, Tom Mollouk, Shane Ardo, Kara L. Bren, Andrew M. Rappe, Wendy J. Shaw, Héctor D. Abruña, Harry Albert Atwater, Kathy Ayers, Curtis P. Berlinguette, Javier J. Concepcion, Valentino R. Cooper, Daniel V. Esposito, John Gregorie, Leif Hammarström, Sophia Haussener, Frances A. Houle, Sulijo Linic, Hannah S. Shafaat, Yang Shao‐Horn, Wilson A. Smith, Yogi Surendranath, David M. Tiede, Jenny Y. Yang
Read original paper ↗ ·Synthetic organic chemistry driven by artificial intelligence
Nature Reviews Chemistry · 2019-08-21
A. Filipa Almeida, Rui Moreira, Tiago Rodrigues
Read original paper ↗ ·Deep learning enables rapid identification of potent DDR1 kinase inhibitors
Nature Biotechnology · 2019-09-01
Alex A. Zhavoronkov, Yan A. Ivanenkov, Alex Aliper, Mark S. Veselov, Vladimir Aladinskiy, Anastasiya V. Aladinskaya, Victor A. Terentiev, Daniil A. Polykovskiy, Maksim Kuznetsov, Arip Asadulaev, Yury Volkov, Artem Zholus, Rim Shayakhmetov, Alexander Zhebrak, Lidiya I. Minaeva, Bogdan A. Zagribelnyy, Lennart H. Lee, Richard Söll, David J. Madge, Xing Li, Tao Guo, Alán Aspuru‐Guzik
Read original paper ↗ ·Machine learning in materials science
InfoMat · 2019-09-01
Jing Wei, Xuan Chu, Xiangyu Sun, Kun Xu, Hui‐Xiong Deng, Ji-Gen Chen, Zhongming Wei, Ming Lei
Read original paper ↗ ·Controlled exploration of chemical space by machine learning of coarse-grained representations
Physical review. E · 2019-09-03
Christian Hoffmann, Roberto Menichetti, Kiran H. Kanekal, Tristan Bereau
Read original paper ↗ ·DENOPTIM: Software for Computational de Novo Design of Organic and Inorganic Molecules
Journal of Chemical Information and Modeling · 2019-09-03
Marco Foscato, Vishwesh Venkatraman, Vidar Remi Jensen
Read original paper ↗ ·Emerging role of machine learning in light-matter interaction
Light Science & Applications · 2019-09-10
Jiajia Zhou, Bolong Huang, Zheng Yan, Jean‐Claude G. Bünzli
Read original paper ↗ ·Learning Drug Functions from Chemical Structures with Convolutional Neural Networks and Random Forests
Journal of Chemical Information and Modeling · 2019-09-13
Jesse G. Meyer, Shengchao Liu, Ian J. Miller, Joshua J. Coon, Anthony Gitter
Read original paper ↗ ·Direct Steering of de novo Molecular Generation using Descriptor Conditional Recurrent Neural Networks (cRNNs)
ChemRxiv · 2019-09-23
Panagiotis-Christos Kotsias, Josep Arús‐Pous, Hongming Chen, Ola Engkvist, Christian Tyrchan, Esben Jannik Bjerrum
Read original paper ↗ ·Autonome Entdeckung in den chemischen Wissenschaften, Teil I: Fortschritt
Angewandte Chemie · 2019-09-25
Connor W. Coley, Natalie S. Eyke, Klavs F. Jensen
Read original paper ↗ ·Autonome Entdeckung in den chemischen Wissenschaften, Teil II: Ausblick
Angewandte Chemie · 2019-09-25
Connor W. Coley, Natalie S. Eyke, Klavs F. Jensen
Read original paper ↗ ·