Papers citing Machine learning for molecular and materials science
Mapping binary copolymer property space with neural networks
Chemical Science · 2019
Liam Wilbraham, Reiner Sebastian Sprick, Kim E. Jelfs, Martijn A. Zwijnenburg
Read original paper ↗ ·Prediction of Mohs Hardness with Machine Learning Methods Using Compositional Features
ACS symposium series · 2019
Joy C. Garnett
Read original paper ↗ ·Rapid, comprehensive screening of ionic liquids towards sustainable applications
Sustainable Energy & Fuels · 2019
Vishwesh Venkatraman, Sigvart Evjen, Kallidanthiyil Chellappan Lethesh, Jaganathan Joshua Raj, Hanna Katariina Knuutila, Anne Fiksdahl
Read original paper ↗ ·Reaction mechanisms at the homogeneous–heterogeneous frontier: insights from first-principles studies on ligand-decorated metal nanoparticles
Catalysis Science & Technology · 2019
Manuel Á. Ortuño, Núria López
Read original paper ↗ ·Recent Advancements in Computing Reliable Binding Free Energies in Drug Discovery Projects
Challenges and advances in computational chemistry and physics · 2019
N. Arul Murugan, Vasanthanathan Poongavanam, U. Deva Priyakumar
Read original paper ↗ ·Structures and evolution of metal oxide nanoclusters: Bottom-up genetic algorithm and fragment-based energy decomposition model
Annual reports in computational chemistry · 2019
Mingyang Chen
Read original paper ↗ ·The inverse design of structural color using machine learning
Nanoscale · 2019
Zhao Huang, Xin Liu, Jianfeng Zang
Read original paper ↗ ·The Materials Science Procedural Text Corpus: Annotating Materials Synthesis Procedures with Shallow Semantic Structures
Linguistic Annotation Workshop · 2019
Sheshera Mysore, Zach Jensen, Edward Kim, Kejun Huang, Haw-Shiuan Chang, Emma Strubell, Jeffrey Flanigan, Andrew McCallum, Elsa Olivetti
Read original paper ↗ ·The Structural Information Filtered Features Potential for Machine Learning calculations of energies and forces of atomic systems.
Venue not supplied by source · 2019
Jorge Arturo Hernandez Zeledon
Read original paper ↗ ·Neural Network Analysis of Dynamic Fracture in a Layered Material
MRS Advances · 2019
Pankaj Rajak, Rajiv K. Kalia, Aiichiro Nakano, Priya D. Vashishta
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 ↗ ·Finding the Right Bricks for Molecular Legos: A Data Mining Approach to Organic Semiconductor Design
Chemistry of Materials · 2019
Christian Künkel, Christoph Schober, Johannes T. Margraf, Karsten Reuter, Harald Oberhofer
Read original paper ↗ ·Material design of indium-based compounds: Possible candidates for charge, valence, and bond disproportionation and superconductivity
Physical Review Materials · 2019
Chang‐Jong Kang, Gabriel Kotliar
Read original paper ↗ ·A meta-analysis of catalytic literature data reveals property-performance correlations for the OCM reaction
Nature Communications · 2019
Roman Schmack, Alexandra Friedrich, Evgenii V. Kondratenko, Jörg Polte, Axel Werwatz, Ralph Kraehnert
Read original paper ↗ ·Metallogels: Availability, Applicability, and Advanceability
Advanced Materials · 2019
Huiqiong Wu, Jun Zheng, Anna‐Lena Kjøniksen, Wei Wang, Zhang Na, Jianmin Ma
Read original paper ↗ ·Unraveling the Factors Behind the Efficiency of Hydrogen Evolution in Endohedrally Doped C60 Structures via Ab Initio Calculations and Insights from Machine Learning Models
Advanced Theory and Simulations · 2019
Hassan A. Tahini, Xin Tan, Sean Campbell Smith
Read original paper ↗ ·Overcoming Distrust in Solid State Simulations: Adding Error Bars to Computational Data
The Journal of Physical Chemistry C · 2019
Francesca Peccati, Rubén Laplaza, Julia Contreras‐García
Read original paper ↗ ·A machine learning approach to thermal conductivity modeling: A case study on irradiated uranium-molybdenum nuclear fuels
Computational Materials Science · 2019
Elizabeth J. Kautz, Alexander R. Hagen, Jesse Johns, Douglas E. Burkes
Read original paper ↗ ·Machine-learning approach to the design of OSDAs for zeolite beta
Proceedings of the National Academy of Sciences · 2019
Frits F. D. Daeyaert, Fengdan Ye, Michael W. Deem
Read original paper ↗ ·Emerging Theory, Materials, and Screening Methods: New Opportunities for Promoting Thermoelectric Performance
Annalen der Physik · 2019
Yulou Ouyang, Zhongwei Zhang, Dengfeng Li, Jie Chen, Gang Zhang
Read original paper ↗ ·Workflows Allowing Creation of Journal Article Supporting Information and Findable, Accessible, Interoperable, and Reusable (FAIR)-Enabled Publication of Spectroscopic Data
ACS Omega · 2019
Agustín Barba, Santiago Dominguez, Carlos Cobas, David P. Martinsen, Charles Romain, Henry S. Rzepa, Felipe Seoane
Read original paper ↗ ·Designing interfaces in energy materials applications with first-principles calculations
npj Computational Materials · 2019
Keith T. Butler, Gopalakrishnan Sai Gautam, Pieremanuele Canepa
Read original paper ↗ ·Expanding the horizon of automated metamaterials discovery via quantum annealing
arXiv (Cornell University) · 2019
Koki Kitai, Jiang Guo, Shenghong Ju, Shu Tanaka, Koji Tsuda, Junichiro Shiomi, Ryo Tamura
Read original paper ↗ ·From DFT to machine learning: recent approaches to materials science–a review
Journal of Physics Materials · 2019
Gabriel Ravanhani Schleder, A. C. M. Padilha, Carlos Mera Acosta, Marcio Costa, A. Fazzio
Read original paper ↗ ·Concepts for improving hydrogen storage in nanoporous materials
International Journal of Hydrogen Energy · 2019
Darren P. Broom, C. J. Webb, George S. Fanourgakis, George E. Froudakis, Pantelis N. Trikalitis, Michael Hirscher
Read original paper ↗ ·Application of radial basis neural network to transform viscoelastic to elastic properties for materials with multiple thermal transitions
Journal of Materials Science · 2019
Xianbo Xu, Nïkhil Gupta
Read original paper ↗ ·Benchmarking machine learning methods for comprehensive chemical fingerprinting and pattern recognition
Journal of Chromatography A · 2019
Stephen E. Reichenbach, Cláudia Alcaraz Zini, Karine Primieri Nicolli, Juliane Elisa Welke, Chiara Emilia Irma Cordero, Qingping Tao
Read original paper ↗ ·Data-enabled structure–property mappings for lanthanide-activated inorganic scintillators
Journal of Materials Science · 2019
Ghanshyam Pilania, Xiang-Yang Liu, Zhehui Wang
Read original paper ↗ ·Machine-learning guided discovery of a new thermoelectric material
Scientific Reports · 2019
Yuma Iwasaki, Ichiro Takeuchi, Valentin Stanev, Aaron Gilad Kusne, Masahiko Ishida, Akihiro Kirihara, Kazuki Ihara, Ryohto Sawada, Koichi Terashima, Hiroko Someya, Ken‐ichi Uchida, Eiji Saitoh, Shinichi Yorozu
Read original paper ↗ ·A catalyst selection method for hydrogen production through Water-Gas Shift Reaction using artificial neural networks
Journal of Environmental Management · 2019
Fábio Machado Cavalcanti, Martín Schmal, Reinaldo Giudici, Rita M.B. Alves
Read original paper ↗ ·Advances of machine learning in molecular modeling and simulation
Current Opinion in Chemical Engineering · 2019
Mojtaba Haghighatlari, Johannes Hachmann
Read original paper ↗ ·Solving vapor-liquid flash problems using artificial neural networks
Fluid Phase Equilibria · 2019
Jonah P. Poort, Mahinder Ramdin, Jan Van Kranendonk, Thijs J. H. Vlugt
Read original paper ↗ ·Materials development by interpretable machine learning
arXiv (Cornell University) · 2019
Yuma Iwasaki, Sawada Ryoto, Valentin Stanev, Masahiko Ishida, Akihiro Kirihara, Yasutomo Omori, Hiroko Someya, Ichiro Takeuchi, Eiji Saitoh, Shinichi Yorozu
Read original paper ↗ ·Acceleration of Interface Structure Searching via Machine Learning
Vacuum and Surface Science · 2019
Shin Kiyohara, Teruyasu Mizoguchi
Read original paper ↗ ·Deep learning for molecular generation and optimization - a review of the state of the art
arXiv (Cornell University) · 2019
Daniel C. Elton, Zois Boukouvalas, Mark Fuge, Peter W. Chung
Read original paper ↗ ·Training Neural Nets To Learn Reactive Potential Energy Surfaces Using Interactive Quantum Chemistry in Virtual Reality
The Journal of Physical Chemistry A · 2019
Silvia Amabilino, Lars Andersen Bratholm, Simon J. Bennie, Alain C. Vaucher, Markus Reiher, David R. Glowacki
Read original paper ↗ ·Machine learning for composite materials
MRS Communications · 2019
Chun‐Teh Chen, Grace X. Gu
Read original paper ↗ ·Rapid identification of two-dimensional materials via machine learning assisted optic microscopy
Journal of Materiomics · 2019
Yuhao Li, Yangyang Kong, Jinlin Peng, Chuanbin Yu, Zhi Li, Penghui Li, Yunya Liu, Cun‐Fa Gao, Rong Wu
Read original paper ↗ ·Informed Machine Learning - Towards a Taxonomy of Explicit Integration of Knowledge into Machine Learning.
arXiv (Cornell University) · 2019
Laura von Rueden, Sebastian Mayer, Jochen Garcke, Christian Bauckhage, Jannis Schücker
Read original paper ↗ ·Convolutional Neural Networks for Crystal Material Property Prediction Using Hybrid Orbital-Field Matrix and Magpie Descriptors
Crystals · 2019
Zhuo Cao, Yabo Dan, Zheng Xiong, Chengcheng Niu, Xiang Li, Songrong Qian, Jianjun Hu
Read original paper ↗ ·A universal density matrix functional from molecular orbital-based machine learning: Transferability across organic molecules
The Journal of Chemical Physics · 2019
Lixue Cheng, Matthew Welborn, Anders S. Christensen, Thomas F. Miller
Read original paper ↗ ·Machine learning approaches and their current application in plant molecular biology: A systematic review
Plant Science · 2019
José Cleydson F. Silva, Ruan M. Teixeira, Fabyano Fonseca e Silva, Sérgio Hermínio Brommonschenkel, Elizabeth P. B. Fontes
Read original paper ↗ ·Structure prediction drives materials discovery
Nature Reviews Materials · 2019
Артем Ромаевич Оганов, Chris J. Pickard, Qiang Zhu, Richard J. Needs
Read original paper ↗ ·New frontiers for the materials genome initiative
npj Computational Materials · 2019
Juan Pablo, Nicholas E. Jackson, Michael A. Webb, Long‐Qing Chen, Joel E. Moore, Dane D. Morgan, Ryan M. Jacobs, Tresa M. Pollock, Darrell G. Schlom, Eric S. Toberer, James G. Analytis, Ismaïla Dabo, Dean M. DeLongchamp, Gregory A. Fiete, Gregory M. Grason, Geoffroy Hautier, Yifei Mo, Krishna Rajan, Evan J. Reed, Efrain E. Rodriguez, Vladan Stevanović, Jin Suntivich, Katsuyo S. Thornton, Ji‐Cheng Zhao
Read original paper ↗ ·Machine learning for molecular dynamics with strongly correlated electrons
Physical review. B./Physical review. B · 2019
Hidemaro Suwa, Justin S. Smith, Nicholas Lubbers, Cristian D. Batista, Gia-Wei Chern, Kipton M. Barros
Read original paper ↗ ·Estimation of the breadth of CD4bs targeting HIV antibodies by molecular modeling and machine learning
PLoS Computational Biology · 2019
Simone Conti, Martin Karplus
Read original paper ↗ ·Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals
Chemistry of Materials · 2019
Chi Chen, Weike Ye, Yunxing Zuo, Chen Zheng, Shyue Ping Ong
Read original paper ↗ ·Optically Digitalized Holography: A Perspective for All-Optical Machine Learning
Engineering · 2019
Miṅ Gu, Xinyuan Fang, Haoran Ren, Elena Goi
Read original paper ↗ ·Machine learning for early prediction of circulatory failure in the intensive care unit
arXiv (Cornell University) · 2019
Stephanie L. Hyland, Martin Faltys, Matthias Hüser, Xinrui Lyu, Thomas Gumbsch, Cristóbal Esteban, Christian Bock, Max Horn, Michael Moor, Bastian Rieck, Marc Zimmermann, Dean A. Bodenham, Karsten Borgwardt, Gunnar Rätsch, Tobias M. Merz
Read original paper ↗ ·Multifidelity Information Fusion with Machine Learning: A Case Study of Dopant Formation Energies in Hafnia
ACS Applied Materials & Interfaces · 2019
Rohit Batra, Ghanshyam Pilania, Blas Pedro Uberuaga, Rampi Ramprasad
Read original paper ↗ ·