Papers citing Inverse design in search of materials with target functionalities
A Conceptual Theory of Model-Science Interface: Where AI Outputs Become Experimental Inputs
Journal of Artificial Intelligence for Materials Science · 2022
Wei Chen, Li Zhang
Read original paper ↗ ·A Conceptual Typology of Scientific Surprise in AI-Guided Discovery
Journal of Artificial Intelligence for Materials Science · 2022
Claire Martin, Julien Robert, Sophie Bernard, Antoine Girard
Read original paper ↗ ·Inverse design for advanced building envelope materials, systems and operation
Elsevier eBooks · 2022
Roel Loonen, Samuel de Vries, Francesco Goia
Read original paper ↗ ·Inverse design of molecule–metal nanoparticle systems interacting with light for desired photophysical properties
Physical Chemistry Chemical Physics · 2022
Takafumi Shiraogawa, Giulia Dall’Osto, Roberto Cammi, Masahiro Ehara, Stefano Corni
Read original paper ↗ ·Machine learning assisted hierarchical filtering: a strategy for designing magnets with large moment and anisotropy energy
Journal of Materials Chemistry C · 2022
Arijit Dutta, Prasenjit Sen
Read original paper ↗ ·Optimization-Free Inverse Design of High-Dimensional Nanoparticle Electrocatalysts Using Multi-target Machine Learning
Lecture notes in computer science · 2022
Sichao Li, Jonathan Y. C. Ting, Amanda Susan Barnard
Read original paper ↗ ·Scientific Validation in Materials AI—A Critical Survey of Conceptual Approaches: A Review Study
Journal of Artificial Intelligence for Materials Science · 2022
Daniel Fischer, Laura Meier, Thomas Braun, Stefan Koch, Felix Roth
Read original paper ↗ ·Selected machine learning of HOMO–LUMO gaps with improved data-efficiency
Materials Advances · 2022
Bernard Mazouin, Alexandre A. Schoepfer, O. Anatole von Lilienfeld
Read original paper ↗ ·The Attention Economy of Materials AI: How Model Focus Shapes Scientific Attention Allocation
Journal of Artificial Intelligence for Materials Science · 2022
Michael Turner, Sophia Nguyen, David Clark, Emma Wilson
Read original paper ↗ ·The Problem of Scientific Regret in AI-Driven Materials Selection
Journal of Artificial Intelligence for Materials Science · 2022
Hiroshi Tanaka, Yuki Sato, Kenji Mori, Rina Okabe, Takashi Ito
Read original paper ↗ ·Why Invariance Becomes a Liability: Failure Modes of Rotationally Invariant GNNs for Anisotropic Elastic Constants
Journal of Computational and Data-Driven Materials Engineering · 2022
Michael Turner, Sophia Nguyen, David Clark, Emma Wilson
Read original paper ↗ ·Outlook
Royal Society of Chemistry eBooks · 2022
Daniel V. Esposito, Heinz Frei
Read original paper ↗ ·Screening and Understanding Lattice Silicon‐Controlled Catalytically Active Site Motifs from a Library of Transition Metal‐Silicon Intermetallics
Small · 2022
Hui Chen, Mingcheng Zhang, Kexin Zhang, Zhenyu Li, Xiao Liang, Xuan Ai, Xiaoxin Zou
Read original paper ↗ ·Autonomous Reaction Network Exploration in Homogeneous and Heterogeneous Catalysis
Topics in Catalysis · 2022
Miguel Steiner, Markus Reiher
Read original paper ↗ ·Experimental Synthesis of Theoretically Predicted Multivalent Ternary Nitride Materials
Chemistry of Materials · 2022
Andriy Zakutayev, Sage R. Bauers, Stephan Lany
Read original paper ↗ ·Review of computational approaches to predict the thermodynamic stability of inorganic solids
Journal of Materials Science · 2022
Christopher J. Bartel
Read original paper ↗ ·The materials tetrahedron has a “digital twin”
MRS Bulletin · 2022
Michael E. Deagen, L. Catherine Brinson, Richard A. Vaia, Linda S. Schadler
Read original paper ↗ ·Inverse design of 3d molecular structures with conditional generative neural networks
Nature Communications · 2022
Niklas W. A. Gebauer, Michael Gastegger, Stefaan S. P. Hessmann, Klaus‐Robert Müller, Kristof T. Schütt
Read original paper ↗ ·Application of Materials Genome Methods in Thermoelectrics
Frontiers in Materials · 2022
Yan Cao, Ye Sheng, Xin Li, Lili Xi, Jiong Yang
Read original paper ↗ ·Inverse Design of Materials by Machine Learning
Materials · 2022
Wang Jia, Yingxue Wang, Yanan Chen
Read original paper ↗ ·Lattice inversion potential with neural network corrections for metallic systems
Computational Materials Science · 2022
Feifeng Wu, Xianbao Duan, Ping Qian, Hang Min, Yanwei Wen, Rong Chen, Yunkun Zhao, Bin Shan
Read original paper ↗ ·An invertible crystallographic representation for general inverse design of inorganic crystals with targeted properties
Matter · 2022
Zekun Ren, Siyu Isaac Parker Tian, Juhwan Noh, Felipe Oviedo, Guangzong Xing, Jiali Li, Qiaohao Liang, Ruiming Zhu, Armin Gerhard Aberle, Shijing Sun, Xiaonan Wang, Yi Liu, Qianxiao Li, J. Senthilnath, Kedar Hippalgaonkar, Yousung Jung, Tonio Buonassisi
Read original paper ↗ ·Application of deep neural network learning in composites design
European Journal of Materials · 2022
Yinli Wang, Constantinos Soutis, Daisuke Ando, Yuji Sutou, Fumio Narita
Read original paper ↗ ·Distributed representations of atoms and materials for machine learning
npj Computational Materials · 2022
Luis M. Antunes, Ricardo Grau‐Crespo, Keith T. Butler
Read original paper ↗ ·Machine learning in energy storage materials
Interdisciplinary materials · 2022
Zhong‐Hui Shen, Hanxing Liu, Yang Shen, Jia‐Mian Hu, Long‐Qing Chen, Ce‐Wen Nan
Read original paper ↗ ·Recent progress in the machine learning-assisted rational design of alloys
International Journal of Minerals Metallurgy and Materials · 2022
Huadong Fu, Hong‐Tao Zhang, Changsheng Wang, Wei Yong, Jianxin Xie
Read original paper ↗ ·Recent advances and applications of deep learning methods in materials science
npj Computational Materials · 2022
Kamal Kumar Choudhary, Brian DeCost, Chi Chen, Anubhav Jain, Francesca Tavazza, Ryan Cohn, Cheol Woo Park, Alok Choudhary, Ankit Agrawal, Simon J. L. Billinge, Elizabeth A. Holm, Shyue Ping Ong, Chris M. Wolverton
Read original paper ↗ ·Unsupervised machine learning to classify crystal structures according to their structural distortion: A case study on Li-argyrodite solid-state electrolytes
Energy and AI · 2022
A. Gallo‐Bueno, Marine Reynaud, Montse Casas‐Cabanas, Javier Carrasco
Read original paper ↗ ·Heuristics and Uncertainty Quantification in Rational and Inverse Compound and Catalyst Design
Elsevier eBooks · 2022
Thomas Weymuth, Markus Reiher
Read original paper ↗ ·Data‐Driven Materials Innovation and Applications
Advanced Materials · 2022
Zhuo Wang, Zhehao Sun, Hang Yin, Xinghui Liu, Jinlan Wang, Haitao Zhao, Cheng Heng Pang, Tao Wu, Shuzhou Li, Zongyou Yin, Xue‐Feng Yu
Read original paper ↗ ·Accelerated Discovery of the Polymer Blends for Cartilage Repair through Data-Mining Tools and Machine-Learning Algorithm
Polymers · 2022
Anusha Mairpady, Abdel‐Hamid I. Mourad, Mohammad Sayem Mozumder
Read original paper ↗ ·Tailoring nanoporous graphene via machine learning: Predicting probabilities and formation times of arbitrary nanopore shapes
The Journal of Chemical Physics · 2022
Rahul Sheshanarayana, Ananth Govind Rajan
Read original paper ↗ ·High-throughput inverse design and Bayesian optimization of functionalities: spin splitting in two-dimensional compounds
Scientific Data · 2022
Gabriel M. Nascimento, Elton Ogoshi, A. Fazzio, Carlos Mera Acosta, Gustavo M. Dalpian
Read original paper ↗ ·Opportunities for machine learning to accelerate halide-perovskite commercialization and scale-up
Matter · 2022
Rishi E. Kumar, Armi Tiihonen, Shijing Sun, David P. Fenning, Zhe Liu, Tonio Buonassisi
Read original paper ↗ ·MatSciBERT: A materials domain language model for text mining and information extraction
npj Computational Materials · 2022
Tanishq Gupta, Mohd Faiz Mohammad Zaki, N. M. Anoop Krishnan, Mausam Mausam
Read original paper ↗ ·Safety-by-design using forward and inverse multi-target machine learning
Chemosphere · 2022
Sichao Li, Amanda Susan Barnard
Read original paper ↗ ·Accelerating Inhibitor Discovery for Multiple SARS-CoV-2 Targets with a Single, Sequence-Guided Deep Generative Framework
Research Square · 2022
Vijil Chenthamarakshan, Samuel C. Hoffman, David Owen, Petra Lukacik, Claire M. Strain-Damerell, D. Fearon, Tika R. Malla, Anthony Tumber, Christopher Schofield, Helen M. E. Duyvesteyn, Wanwisa Ddejnirattisai, Loïc Carrique, Thomas S. Walter, Gavin Robert Screaton, Tetiana Matviiuk, Aleksandra Mojsilović, Jason N. Crain, Martin Austin Walsh, David Ian Stuart, Payel Das
Read original paper ↗ ·Beyond T-graphene: Two-dimensional tetragonal allotropes and their potential applications
Applied Physics Reviews · 2022
Supriya Ghosal, Debnarayan Jana
Read original paper ↗ ·Human–Machine Interface
Royal Society of Chemistry eBooks · 2022
Read original paper ↗ ·Graph-Wise Common Latent Factor Extraction for Unsupervised Graph Representation Learning
Proceedings of the AAAI Conference on Artificial Intelligence · 2022
Thilini Cooray, Ngai‐Man Cheung
Read original paper ↗ ·Machine-Learning-Assisted Acceleration on High-Symmetry Materials Search: Space Group Predictions from Band Structures
The Journal of Physical Chemistry C · 2022
Bin Xi, Kin Fai Tse, Tsz-Fung Kok, Ho Ming Chan, Man Kit Chan, Ho Yin Chan, Kwan Yue Clinton Wong, Robin S. H. Yuen, Junyi Zhu
Read original paper ↗ ·Optimization of hybridized InAsSb/InGaSb semiconductor topological materials
Applied Physics Letters · 2022
Heather J. Haugan, Debanik Das, Sathwik Bharadwaj, L. R. Ram‐Mohan, Joseph P. Corbett, R. K. Smith, Jay Gupta, K. Mahalingam, Robert F. Bedford, Kurt G. Eyink
Read original paper ↗ ·Advances and challenges in DFT-based energy materials design
Chinese Physics B · 2022
Jun Yong Kang, Xie Zhang, Su‐Huai Wei
Read original paper ↗ ·Human- and machine-centred designs of molecules and materials for sustainability and decarbonization
Nature Reviews Materials · 2022
Jiayu Peng, Daniel Schwalbe‐Koda, Karthik Akkiraju, Tian Xie, Livia Giordano, Yang Yu, C. John Eom, Jaclyn R. Lunger, Daniel J. Zheng, Reshma R. Rao, Sokseiha Muy, Jeffrey C. Grossman, Karsten Reuter, Rafael Gómez‐Bombarelli, Yang Shao‐Horn
Read original paper ↗ ·Battery Materials Discovery and Smart Grid Management using Machine Learning
Batteries & Supercaps · 2022
Andrew Jun Yao Wong, Xin Zhou, Yanwei Lum, Zhenpeng Yao, Yang Choo Chua, Yonggang Wen, Zhi Wei Seh
Read original paper ↗ ·Why big data and compute are not necessarily the path to big materials science
Communications Materials · 2022
Naohiro Fujinuma, Brian DeCost, Jason R. Hattrick-Simpers, S. E. Lofland
Read original paper ↗ ·Exploration of Chemical Space for Designing Functional Molecules Accounting for Geometric Stability
The Journal of Physical Chemistry Letters · 2022
Takafumi Shiraogawa, Jun‐ya Hasegawa
Read original paper ↗ ·Machine learning for high-entropy alloys: Progress, challenges and opportunities
Progress in Materials Science · 2022
Xianglin Liu, Jiaxin Zhang, Zongrui Pei
Read original paper ↗ ·Inverse design of magnonic filter
Journal of Magnetism and Magnetic Materials · 2022
Z. R. Yan, Y. W. Xing, Xiufeng Han
Read original paper ↗ ·Bridging the gap between density functional theory and quantum materials
Nature Computational Science · 2022
Alex Zunger
Read original paper ↗ ·