Papers citing Multi-fidelity machine learning models for accurate bandgap predictions of solids
Multi-fidelity machine-learning with uncertainty quantification and Bayesian optimization for materials design: Application to ternary random alloys
The Journal of Chemical Physics · 2020
Anh Tran, Julien Tranchida, Timothy Michael Wildey, Aidan P. Thompson
Read original paper ↗ ·Machine Learning Discovery of Computational Model Efficacy Boundaries
Physical Review Letters · 2020
Michael S. Murillo, Mathieu Marciante, Liam G. Stanton
Read original paper ↗ ·Predicting band gaps and band-edge positions of oxide perovskites using DFT and machine learning
arXiv (Cornell University) · 2020
Wei Li, Zigeng Wang, Xia Xiao, Zhiqiang Zhang, Anderson Janotti, Sanguthevar Rajasekaran, Bharat Medasani
Read original paper ↗ ·Machine learning for halide perovskite materials
Nano Energy · 2020
Lei Zhang, Mu He, Shaofeng Shao
Read original paper ↗ ·Thermochemical Data Fusion Using Graph Representation Learning
Journal of Chemical Information and Modeling · 2020
Himaghna Bhattacharjee, Dionisios G. Vlachos
Read original paper ↗ ·Voxelized Atomic Structure Potentials: Predicting Atomic Forces with the Accuracy of Quantum Mechanics Using Convolutional Neural Networks
The Journal of Physical Chemistry Letters · 2020
Matthew C. Barry, Kristopher E. Wise, Surya R. Kalidindi, Satish Kumar
Read original paper ↗ ·Machine learning in materials design: Algorithm and application*
Chinese Physics B · 2020
Zhilong Song, Xiwen Chen, Fanbin Meng, Guanjian Cheng, Chen Wang, Zhongti Sun, Wan‐Jian Yin
Read original paper ↗ ·Leverage electron properties to predict phonon properties via transfer learning for semiconductors
Science Advances · 2020
Zeyu Liu, Meng Jiang, Tengfei Luo
Read original paper ↗ ·Machine-learning predictions of polymer properties with Polymer Genome
Journal of Applied Physics · 2020
Tran Doan Huan, Chiho Kim, Lihua Chen, Anand R. Chandrasekaran, Rohit Batra, Shruti Venkatram, Deepak Kamal, Jordan P. Lightstone, Rishi Gurnani, Pranav Shetty, Manav Ramprasad, Julia Laws, Madeline Shelton, Rampi Ramprasad
Read original paper ↗ ·Emerging materials intelligence ecosystems propelled by machine learning
Nature Reviews Materials · 2020
Rohit Batra, Le Song, Rampi Ramprasad
Read original paper ↗ ·Machine Learning for Electronically Excited States of Molecules
Chemical Reviews · 2020
Julia Westermayr, Philipp Marquetand
Read original paper ↗ ·Machine learning for analyzing and characterizing InAsSb-based nBn photodetectors
Machine Learning Science and Technology · 2020
Andreu Glasmann, Alexandros Kyrtsos, E. Bellotti
Read original paper ↗ ·Extraction of material properties through multi-fidelity deep learning from molecular dynamics simulation
Computational Materials Science · 2020
Mahmudul Islam, Md Shajedul Hoque Thakur, Satyajit Mojumder, Mohammad Nasim Hasan
Read original paper ↗ ·Tuplewise Material Representation Based Machine Learning for Accurate Band Gap Prediction
The Journal of Physical Chemistry A · 2020
Gyoung S. Na, Seunghun Jang, Yea‐Lee Lee, Hyunju Chang
Read original paper ↗ ·Screening of perovskite materials for solar cell applications by first-principles calculations
Materials & Design · 2020
Wenhua Pu, Wei Xiao, Jianwei Wang, Xiaowu Li, Ligen Wang
Read original paper ↗ ·Ab initio machine learning in chemical compound space
arXiv (Cornell University) · 2020
Bing Huang, O. Anatole von Lilienfeld
Read original paper ↗ ·Polymer informatics: Current status and critical next steps
Materials Science and Engineering R Reports · 2020
Lihua Chen, Ghanshyam Pilania, Rohit Batra, Tran Doan Huan, Chiho Kim, Christopher Kuenneth, Rampi Ramprasad
Read original paper ↗ ·A data-driven approach to predicting band gap, excitation, and emission energies for Eu 2+ -activated phosphors
Inorganic Chemistry Frontiers · 2021
Chaewon Park, Jin-Woong Lee, Minseuk Kim, Byung Do Lee, Satendra Pal Singh, Woon Bae Park, Kee‐Sun Sohn
Read original paper ↗ ·A machine learning approach using frequency descriptor for molecular property predictions
New Journal of Chemistry · 2021
Jialu Chen, Wenjun Xu, Ruiqin Q. Zhang
Read original paper ↗ ·Computational techniques for characterisation of electrically conductive MOFs: quantum calculations and machine learning approaches
Journal of Materials Chemistry C · 2021
Federica Zanca, Lawson T. Glasby, Sanggyu Chong, Siyu Chen, Jihan Kim, David Fairen‐Jiménez, Bartomeu Monserrat, Peyman Z. Moghadam
Read original paper ↗ ·Experimental design for the highly accurate prediction of material properties using descriptors obtained by measurement
Science and Technology of Advanced Materials Methods · 2021
Ryo Tamura, Yuki Takei, Shinichiro Imai, Maki Nakahara, Satoshi Shibata, Takashi Nakanishi, Masahiko Demura
Read original paper ↗ ·Machine Learning Accelerated Insights of Perovskite Materials
Springer series in materials science · 2021
Shuaihua Lu, Yilei Wu, Ming‐Gang Ju, Jinlan Wang
Read original paper ↗ ·Machine-learning-assisted low dielectric constant polymer discovery
Materials Chemistry Frontiers · 2021
Jiechun Liang, Shangqian Xu, Linfeng Hu, Yu Zhao, Xi Zhu
Read original paper ↗ ·MUMBO: MUlti-task Max-Value Bayesian Optimization
Lecture notes in computer science · 2021
Henry B. Moss, David S. Leslie, Paul Rayson
Read original paper ↗ ·Physically inspired deep learning of molecular excitations and photoemission spectra
Chemical Science · 2021
Julia Westermayr, Reinhard J. Maurer
Read original paper ↗ ·Structural and elastic properties of binary semiconductors from energy gaps
Applied Physics A · 2021
Anup Pattanaik, Sunil Kumar Tripathy, Poonam Naik, Deepak K. Meher
Read original paper ↗ ·Machine Learning Aided Design of Polymer with Targeted Band Gap Based on DFT Computation
The Journal of Physical Chemistry B · 2021
Pengcheng Xu, Tian Lu, Lifei Ju, Lumin Tian, Minjie Li, Wencong Lu
Read original paper ↗ ·Discovery of Lead-Free Hybrid Organic/Inorganic Perovskites Using Metaheuristic-Driven DFT Calculations
Chemistry of Materials · 2021
Byung Do Lee, Woon Bae Park, Jin-Woong Lee, Minseuk Kim, Myoungho Pyo, Kee‐Sun Sohn
Read original paper ↗ ·Learning properties of ordered and disordered materials from multi-fidelity data
Nature Computational Science · 2021
Chi Chen, Yunxing Zuo, Weike Ye, Xiangguo Li, Shyue Ping Ong
Read original paper ↗ ·Machine learning for advanced energy materials
Energy and AI · 2021
Liu Yun, Oladapo Christopher Esan, Zhefei Pan, Liang An
Read original paper ↗ ·Density functional theory-free descriptor for the practical discovery of perovskite catalysts
Computational Materials Science · 2021
Wan‐Jian Yin
Read original paper ↗ ·Exploring Heat-Shielding Nanoparticle-Based Materials via First-Principles Calculations and Transfer Learning
ACS Applied Nano Materials · 2021
Tomohiro Yoshida, Ryo Maezono, Kenta Hongo
Read original paper ↗ ·A bin and hash method for analyzing reference data and descriptors in machine learning potentials
Machine Learning Science and Technology · 2021
Martín Leandro Paleico, Jörg Behler
Read original paper ↗ ·Machine learning for materials design and discovery
Journal of Applied Physics · 2021
Rama Krishnan Vasudevan, Ghanshyam Pilania, Prasanna V. Balachandran
Read original paper ↗ ·A new model updating strategy with physics-based and data-driven models
Structural and Multidisciplinary Optimization · 2021
Yongyong Xiang, Baisong Pan, Luping Luo
Read original paper ↗ ·Graph-based deep learning frameworks for molecules and solid-state materials
Computational Materials Science · 2021
Weiyi Gong, Qimin Yan
Read original paper ↗ ·Design and engineering of high‐performance triboelectric nanogenerator for ubiquitous unattended devices
EcoMat · 2021
Hongmei Yang, Feng Ru Fan, Yi Xi, Wenzhuo Wu
Read original paper ↗ ·Machine learning in materials science: From explainable predictions to autonomous design
Computational Materials Science · 2021
Ghanshyam Pilania
Read original paper ↗ ·Material analysis and big data monitoring of sports training equipment based on machine learning algorithm
Neural Computing and Applications · 2021
Lei Zhang, Ning Li
Read original paper ↗ ·Polymer informatics with multi-task learning
Patterns · 2021
Christopher Kuenneth, Arunkumar Chitteth Rajan, Huan Tran, Lihua Chen, Chiho Kim, Rampi Ramprasad
Read original paper ↗ ·Machine learning classification of binary semiconductor heterostructures
Physical Review Materials · 2021
Samir Rom, Aishwaryo Ghosh, Anita Halder, Tanusri Saha‐Dasgupta
Read original paper ↗ ·Correlation analysis of materials properties by machine learning: illustrated with stacking fault energy from first-principles calculations in dilute fcc-based alloys
Journal of Physics Condensed Matter · 2021
Xiaoyu Chong, Shun‐Li Shang, Adam M. Krajewski, John D. Shimanek, Weihang Du, Yi Wang, Jing Feng, Dongwon Shin, Allison M. Beese, Zi‐Kui Liu
Read original paper ↗ ·Molecular excited states through a machine learning lens
Nature Reviews Chemistry · 2021
Pavlo O. Dral, Mario Barbatti
Read original paper ↗ ·Transfer learning based variable-fidelity surrogate model for shell buckling prediction
Composite Structures · 2021
Kuo Tian, Zengcong Li, Jiaxin Zhang, Lei Huang, Bo Wang
Read original paper ↗ ·Perspective on integrating machine learning into computational chemistry and materials science
The Journal of Chemical Physics · 2021
Julia Westermayr, Michael Gastegger, Kristof T. Schütt, Reinhard J. Maurer
Read original paper ↗ ·Computational discovery of energy materials in the era of big data and machine learning: A critical review
Materials Reports Energy · 2021
Ziheng Lu
Read original paper ↗ ·Hydrogen storage in MOFs: Machine learning for finding a needle in a haystack
Patterns · 2021
Lawson T. Glasby, Peyman Z. Moghadam
Read original paper ↗ ·Catalyze Materials Science with Machine Learning
ACS Materials Letters · 2021
Jaehyun Kim, Donghoon Kang, Sang‐Bum Kim, Ho Won Jang
Read original paper ↗ ·Autonomous experimentation systems for materials development: A community perspective
Matter · 2021
Eric A. Stach, Brian DeCost, Aaron Gilad Kusne, Jason R. Hattrick-Simpers, Keith A. Brown, Kristofer G. Reyes, Joshua Schrier, Simon J. L. Billinge, Tonio Buonassisi, Ian T Foster, Carla Pedro Gomes, John M. Gregoire, Apurva Mehta, Joseph H. Montoya, Elsa Olivetti, Chiwoo Park, Eli Rotenberg, Semion K. Saikin, Sylvia Smullin, Valentin Stanev, Benji Maruyama
Read original paper ↗ ·Bayesian Optimization in Materials Science: A Survey
arXiv (Cornell University) · 2021
Lars Kotthoff, Hud Wahab, Patrick Alfred Johnson
Read original paper ↗ ·