Papers citing Multi-fidelity machine learning models for accurate bandgap predictions of solids
Application of materials informatics on crystalline materials for two-body terms approximation
Computational Materials Science · 2019
Van-Doan Nguyen, Tien-Lam Pham, Hieu‐Chi Dam
Read original paper ↗ ·Concepts of Artificial Intelligence for Computer-Assisted Drug Discovery
Chemical Reviews · 2019
Xin Yu Yang, Yifei Wang, Ryan Byrne, Gisbert Schneider, Shengyong Yang
Read original paper ↗ ·Alternative materials for perovskite solar cells from materials informatics
Physical Review Materials · 2019
Shohei Kanno, Yutaka Imamura, Masahiko Hada
Read original paper ↗ ·Machine learning models for the lattice thermal conductivity prediction of inorganic materials
Computational Materials Science · 2019
Lihua Chen, Tran Doan Huan, Rohit Batra, Chiho Kim, Rampi Ramprasad
Read original paper ↗ ·Making machine learning a useful tool in the accelerated discovery of transition metal complexes
Wiley Interdisciplinary Reviews Computational Molecular Science · 2019
Heather J. Kulik
Read original paper ↗ ·Recent advances and applications of machine learning in solid-state materials science
npj Computational Materials · 2019
Jonathan Schmidt, Mário R. G. Marques, Silvana Botti, Miguel A. L. Marques
Read original paper ↗ ·Predicting Adsorption Energies Using Multifidelity Data
Journal of Chemical Theory and Computation · 2019
Huijie Tian, Srinivas Rangarajan
Read original paper ↗ ·Machine learning prediction of accurate atomization energies of organic molecules from low-fidelity quantum chemical calculations
MRS Communications · 2019
Logan T. Ward, Ben J. Blaiszik, Ian T Foster, Rajeev S. Assary, Badri Narayanan, Larry A. Curtiss
Read original paper ↗ ·Phillips-Inspired Machine Learning for Band Gap and Exciton Binding Energy Prediction
The Journal of Physical Chemistry Letters · 2019
Jiechun Liang, Xi Zhu
Read original paper ↗ ·A multi-fidelity information-fusion approach to machine learn and predict polymer bandgap
Computational Materials Science · 2019
Abhirup Patra, Rohit Batra, Anand R. Chandrasekaran, Chiho Kim, Tran Doan Huan, Rampi Ramprasad
Read original paper ↗ ·Machine learning–assisted molecular design and efficiency prediction for high-performance organic photovoltaic materials
Science Advances · 2019
Wenbo Sun, Yujie Zheng, Ke Yang, Qi Zhang, Akeel Abbas Shah, Zhou Wu, Yuyang Sun, Liang Feng, Dongyang Chen, Zeyun Xiao, Shirong Lu, Yong Li, Kuan Sun
Read original paper ↗ ·Predicting charge density distribution of materials using a local-environment-based graph convolutional network
Physical review. B./Physical review. B · 2019
Sheng Rong Gong, Tian Xie, Taishan Zhu, Shuo Wang, Eric Fadel, Yawei Li, Jeffrey C. Grossman
Read original paper ↗ ·Application of Computational Biology and Artificial Intelligence Technologies in Cancer Precision Drug Discovery
BioMed Research International · 2019
N. Nagasundaram, Edward K.Y. Yapp, Nguyen Quoc Khanh Le, Balu Kamaraj, Abeer M. Al‐Subaie, Hui‐Yuan Yeh
Read original paper ↗ ·Crowd-sourcing materials-science challenges with the NOMAD 2018 Kaggle competition
npj Computational Materials · 2019
Christopher A. Sutton, Luca M. Ghiringhelli, T. Yamamoto, Yury V. Lysogorskiy, Lars Blumenthal, Thomas Hammerschmidt, Jacek R. Gołębiowski, Xiang‐Yue Liu, Angelo Ziletti, Matthias Scheffler
Read original paper ↗ ·Machine Learning Stability and Bandgaps of Lead‐Free Perovskites for Photovoltaics
Advanced Theory and Simulations · 2019
Jared C. Stanley, Felix Mayr, Alessio Gagliardi
Read original paper ↗ ·Predicting HSE band gaps from PBE charge densities via neural network functionals
Journal of Physics Condensed Matter · 2019
Levi C. Lentz, Alexie M. Kolpak
Read original paper ↗ ·Bayesian Global Optimization applied to the design of shape-memory alloys
Elsevier eBooks · 2020
Dezhen Xue, Yuan Tian, Ruihao Yuan, Turab Lookman
Read original paper ↗ ·Learning with Small Databases
Synthesis lectures on materials and optics · 2020
Ghanshyam Pilania, Prasanna V. Balachandran, James E. Gubernatis, Turab Lookman
Read original paper ↗ ·Local structure order parameters and site fingerprints for quantification of coordination environment and crystal structure similarity
RSC Advances · 2020
Nils Edvin Richard Zimmermann, Anubhav Jain
Read original paper ↗ ·Machine Learning Augmented Discovery of Chalcogenide Double Perovskites for Photovoltaics
Advanced Theory and Simulations · 2020
Michael L. Agiorgousis, Yi‐Yang Sun, Duk‐Hyun Choe, Damien J. West, Shengbai Zhang
Read original paper ↗ ·Materials Representations
Synthesis lectures on materials and optics · 2020
Ghanshyam Pilania, Prasanna V. Balachandran, James E. Gubernatis, Turab Lookman
Read original paper ↗ ·Multi-Fidelity Learning
Synthesis lectures on materials and optics · 2020
Ghanshyam Pilania, Prasanna V. Balachandran, James E. Gubernatis, Turab Lookman
Read original paper ↗ ·Quantum Machine Learning in Chemistry and Materials
Springer eBooks · 2020
Bing Huang, Nadine O. Symonds, O. Anatole von Lilienfeld
Read original paper ↗ ·Searching for stable perovskite solar cell materials using materials genome techniques and high-throughput calculations
Journal of Materials Chemistry C · 2020
Qingde Sun, Wan‐Jian Yin, Su‐Huai Wei
Read original paper ↗ ·The Materials Simulation Toolkit for Machine learning (MAST-ML): An automated open source toolkit to accelerate data-driven materials research
Computational Materials Science · 2020
Ryan M. Jacobs, Tam Mayeshiba, Ben Afflerbach, Luke Harold Miles, Max Williams, Matthew Turner, Raphael A. Finkel, Dane D. Morgan
Read original paper ↗ ·A Critical Review of Machine Learning of Energy Materials
Advanced Energy Materials · 2020
Chi Chen, Yunxing Zuo, Weike Ye, Xiangguo Li, Zhi Deng, Shyue Ping Ong
Read original paper ↗ ·Materials informatics approach to understand aluminum alloys
Science and Technology of Advanced Materials · 2020
Ryo Tamura, Makoto Watanabe, Hiroaki Mamiya, Kota Washio, Masao Yano, Katsunori Danno, Akira Kato, Tetsuya Shoji
Read original paper ↗ ·Fundamental band gap and alignment of two-dimensional semiconductors explored by machine learning*
Chinese Physics B · 2020
Zhen Zhu, Baojuan Dong, Huaihong Guo, Teng Kuei Yang, Zhidong Zhang
Read original paper ↗ ·Analysis of an Ultra-Short True Time Delay Line Optical Reservoir Computer
Journal of Lightwave Technology · 2020
Nathan McDonald, Alex Hollingsworth, Steven Beninati, Chjan C. Lim, Wei Ji, Chengyu Shi, Michael L. Agiorgousis, Shengbai Zhang, Zhaoran Huang
Read original paper ↗ ·Machine learning: Accelerating materials development for energy storage and conversion
InfoMat · 2020
An Chen, Xu Zhang, Zhen Ping Zhou
Read original paper ↗ ·High-dimensional potential energy surfaces for molecular simulations: from empiricism to machine learning
Machine Learning Science and Technology · 2020
Oliver T Unke, Debasish Koner, Sarbani Patra, Silvan Käser, Markus Meuwly
Read original paper ↗ ·Quantum Chemistry in the Age of Machine Learning
The Journal of Physical Chemistry Letters · 2020
Pavlo O. Dral
Read original paper ↗ ·Data-Based Methods for Materials Design and Discovery: Basic Ideas and General Methods
Synthesis lectures on materials and optics · 2020
Ghanshyam Pilania, Prasanna V. Balachandran, James E. Gubernatis, Turab Lookman
Read original paper ↗ ·Data integration for accelerated materials design via preference learning
New Journal of Physics · 2020
Xiaolin Sun, Zhufeng Hou, Masato Sumita, Shinsuke Ishihara, Ryo Tamura, Koji Tsuda
Read original paper ↗ ·Enhanced variable-fidelity surrogate-based optimization framework by Gaussian process regression and fuzzy clustering
Computer Methods in Applied Mechanics and Engineering · 2020
Kuo Tian, Zengcong Li, Lei Huang, Kaifan Du, Liangliang Jiang, Bo Wang
Read original paper ↗ ·Machine learning for multi-fidelity scale bridging and dynamical simulations of materials
Journal of Physics Materials · 2020
Rohit Batra, Subramanian K. R. S. Sankaranarayanan
Read original paper ↗ ·Opportunities and Challenges for Machine Learning in Materials Science
Annual Review of Materials Research · 2020
Dane Morgan, Ryan Jacobs
Read original paper ↗ ·Crystal Site Feature Embedding Enables Exploration of Large Chemical Spaces
Matter · 2020
Hitarth Choubisa, Mikhail Askerka, Kevin Ryczko, Oleksandr Voznyy, Kyle Mills, Isaac Tamblyn, Edward Hartley Sargent
Read original paper ↗ ·Machine Learning in Chemistry
ACS in focus · 2020
Jon Paul Janet, Heather J. Kulik
Read original paper ↗ ·Quantum Chemistry-Informed Active Learning to Accelerate the Design and Discovery of Sustainable Energy Storage Materials
Chemistry of Materials · 2020
Hieu Anh Doan, Garvit Agarwal, Hai Qian, Michael John Counihan, Joaquín Rodríguez‐López, Jeffrey S. Moore, Rajeev S. Assary
Read original paper ↗ ·Active Learning A Neural Network Model For Gold Clusters & Bulk From Sparse First Principles Training Data
ChemCatChem · 2020
Troy D. Loeffler, Sukriti Manna, Tarak K. Patra, Henry Chan, Badri Narayanan, Subramanian K. R. S. Sankaranarayanan
Read original paper ↗ ·Big-Data Science in Porous Materials: Materials Genomics and Machine Learning
Chemical Reviews · 2020
Kevin Maik Jablonka, Daniele Ongari, Seyed Mohamad Moosavi, Berend Smit
Read original paper ↗ ·Exploring chemical compound space with quantum-based machine learning
Nature Reviews Chemistry · 2020
O. Anatole von Lilienfeld, Klaus‐Robert Müller, Alexandre Tkatchenko
Read original paper ↗ ·Machine learning and excited-state molecular dynamics
Machine Learning Science and Technology · 2020
Julia Westermayr, Philipp Marquetand
Read original paper ↗ ·Predicting Crystallization Tendency of Polymers Using Multifidelity Information Fusion and Machine Learning
The Journal of Physical Chemistry B · 2020
Shruti Venkatram, Rohit Batra, Lihua Chen, Chiho Kim, Madeline Shelton, Rampi Ramprasad
Read original paper ↗ ·Tuning the Conductivity Type in Monolayer WS2 and MoS2 by Sulfur Vacancies
physica status solidi (RRL) - Rapid Research Letters · 2020
Jing Yang, Fabio Bussolotti, Hiroyo Kawai, Kuan Eng Johnson Goh
Read original paper ↗ ·Thousands of reactants and transition states for competing E2 and S N 2 reactions
Machine Learning Science and Technology · 2020
Guido Falk von Rudorff, Stefan Heinen, Marco Bragato, O. Anatole von Lilienfeld
Read original paper ↗ ·An artificial intelligence-aided virtual screening recipe for two-dimensional materials discovery
npj Computational Materials · 2020
Murat Cihan Sorkun, Séverin Astruc, J. M. V. A. Koelman, Süleyman Er
Read original paper ↗ ·Extracting Knowledge from DFT: Experimental Band Gap Predictions Through Ensemble Learning
Integrating materials and manufacturing innovation · 2020
Steven K. Kauwe, Taylor Welker, Taylor D. Sparks
Read original paper ↗ ·Cost-effective search for lower-error region in material parameter space using multifidelity Gaussian process modeling
Physical Review Materials · 2020
Shion Takeno, Yuhki Tsukada, Hitoshi Fukuoka, Toshiyuki Koyama, M. Shiga, Masayuki Karasuyama
Read original paper ↗ ·