Papers citing Accelerating materials discovery with Bayesian optimization and graph deep learning
A Novel Simulation-Based Optimization Method for Autonomous Vehicle Path Tracking with Urban Driving Application
Mathematics · 2023-11-24
Yanzhan Chen, Fan Yu
Read original paper ↗ ·Scope of machine learning in materials research—A review
Applied Surface Science Advances · 2023-11-28
Md Hosne Mobarak, Mariam Akter Mimona, Md. Aminul Islam, Nayem Hossain, Fatema Tuz Zohura, Ibnul Imtiaz, Md Israfil Hossain Rimon
Read original paper ↗ ·Generative adversarial networks and diffusion models in material discovery
Digital Discovery · 2023-12-06
Michael Alverson, Sterling G. Baird, Ryan Murdock, Sin-Hang Enoch Ho, Jeremy A. Johnson, Taylor D. Sparks
Read original paper ↗ ·Boundary Conditions for Trustworthy Uncertainty Quantification in Materials AI: A Framework for Decision-Ready Confidence
Journal of Computational and Data-Driven Materials Engineering · 2024-01-01
Anna Kowalska, Piotr Nowak, Tomasz Zielinski, Katarzyna Mazur
Read original paper ↗ ·Explainable AI for Interpretable Structure–Property Maps in Polymer Blend Systems
Journal of Artificial Intelligence for Materials Science · 2024-01-01
Claire Martin, Julien Robert, Sophie Bernard
Read original paper ↗ ·How the AI-assisted discovery and synthesis of a ternary oxide highlights capability gaps in materials science
Chemical Science · 2024-01-01
Joseph H. Montoya, Carolyn A. Grimley, Muratahan Aykol, Colin Ophus, Hadas Sternlicht, Benjamin H. Savitzky, Andrew M. Minor, Steven B. Torrisi, Jackson Goedjen, Ching‐Chang Chung, Andrew H. Comstock, Shijing Sun
Read original paper ↗ ·Uncertainty-Aware Surrogate Modeling of High-Throughput DFT Data for Rapid Materials Screening
Journal of Artificial Intelligence for Materials Science · 2024-01-01
Daniel Fischer, Laura Meier, Thomas Braun
Read original paper ↗ ·Synchrotron radiation data-driven artificial intelligence approaches in materials discovery
Artificial Intelligence Chemistry · 2024-01-10
Qingmeng Li, Rongchang Xing, Linshan Li, Haodong Yao, Liyuan Wu, Lina Zhao
Read original paper ↗ ·Accelerating defect predictions in semiconductors using graph neural networks
APL Machine Learning · 2024-03-01
Md. Habibur Rahman, Prince Gollapalli, Panayotis Manganaris, Satyesh Kumar Yadav, Ghanshyam Pilania, Brian DeCost, Kamal Kumar Choudhary, Arun Mannodi‐Kanakkithodi
Read original paper ↗ ·An effective method for generating crystal structures based on the variational autoencoder and the diffusion model
Chinese Chemical Letters · 2024-03-08
Chen Chen, Jinzhou Zheng, Chaoqin Chu, Qinkun Xiao, Chaozheng He, Xi Fu
Read original paper ↗ ·Accelerated Bayesian Inference for Molecular Simulations using Local Gaussian Process Surrogate Models
Journal of Chemical Theory and Computation · 2024-03-29
Brennon L. Shanks, Harry W. Sullivan, Abdur R. Shazed, Michael P. Hoepfner
Read original paper ↗ ·Accelerated Data-Driven Discovery and Screening of Two-Dimensional Magnets Using Graph Neural Networks
The Journal of Physical Chemistry C · 2024-04-01
Ahmed Elrashidy, James Della-Giustina, Jia-An Yan
Read original paper ↗ ·An active machine learning approach for optimal design of magnesium alloys using Bayesian optimisation
Scientific Reports · 2024-04-09
M. Ghorbani, Mario Boley, Philip N. H. Nakashima, N. Birbilis
Read original paper ↗ ·Explainable machine learning to enable high-throughput electrical conductivity optimization and discovery of doped conjugated polymers
Knowledge-Based Systems · 2024-04-22
Ji Wei Yoon, Adithya Kumar, Pawan Kumar, Kedar Hippalgaonkar, J. Senthilnath, Vijila Chellappan
Read original paper ↗ ·Chapter 4: Physically informed deep learning networks for simulating microstructure evolution of 3D polycrystals
Unknown journal · 2024-04-30
Ashley Lenau, USDOE National Nuclear Security Administration (NNSA), The Ohio State Univ., Columbus, OH (United States), Reeju Pokharel, Alexander Scheinker, Stephen R. Niezgoda
Read original paper ↗ ·Review on automated 2D material design
2D Materials · 2024-05-02
Abdalaziz Rashid Al-Maeeni, Mikhail Lazarev, N. Kazeev, Kostya S. Novoselov, A. Ustyuzhanin
Read original paper ↗ ·Revolutionizing inverse design of ionic liquids through the multi-property prediction of over 300,000 novel variants using ensemble deep learning
Materials Science and Engineering R Reports · 2024-05-07
Tarek Lemaoui, Tarek Eid, Ahmad S. Darwish, Hassan A. Arafat, FAWZI A. BANAT, Inas Muen AlNashef
Read original paper ↗ ·Bridging multimodal data and battery science with machine learning
Matter · 2024-05-20
Yanbin Ning, Feng Yang, Yan Zhang, Zhuomin Qiang, Geping Yin, Jiajun Wang, Shuaifeng Lou
Read original paper ↗ ·Local-distortion-informed exceptional multicomponent transition-metal carbides uncovered by machine learning
npj Computational Materials · 2024-07-26
Jun Zhang, He Liu, Yaoxu Xiong, Shasha Huang, Biao Xu, Shihua Ma, Xuepeng Xiang, Haijun Fu, Ji‐Jung Kai, Zhenggang Wu, Shijun Zhao
Read original paper ↗ ·Mechanical Design Parameter Optimization through Graph-Based Bayesian Optimization and Pseudo Labeling
2022 IEEE 18th International Conference on Automation Science and Engineering (CASE) · 2024-08-28
Guoyan Li, Xiaoning Jin
Read original paper ↗ ·A deep generative modeling architecture for designing lattice-constrained perovskite materials
npj Computational Materials · 2024-08-30
Ericsson Tetteh Chenebuah, Michel Nganbe, Alain Tchagang
Read original paper ↗ ·High‐Throughput Computational Screening of Metal Sulfides for the Chemical Looping Elemental Decomposition of H 2 S
Small · 2024-09-16
Polina Tolstova, Rafia Ahmad, Adrian Cavazos Sepulveda, Luigi Cavallo
Read original paper ↗ ·Scalable crystal structure relaxation using an iteration-free deep generative model with uncertainty quantification
Nature Communications · 2024-09-17
Ziduo Yang, Yi-Ming Zhao, Xian Wang, Xiaoqing Liu, Xiuying Zhang, Yifan Li, Qiujie Lv, Calvin Yu‐Chian Chen, Lei Shen
Read original paper ↗ ·Graph-text contrastive learning of inorganic crystal structure toward a foundation model of inorganic materials
Science and Technology of Advanced Materials Methods · 2024-09-23
Keisuke Ozawa, Teppei Suzuki, Shunsuke Tonogai, Tomoya Itakura
Read original paper ↗ ·Discovery of high-performance dielectric materials with machine-learning-guided search
Cell Reports Physical Science · 2024-10-01
Janosh Riebesell, Todd Wesley Surta, Rhys E. A. Goodall, Michael W. Gaultois, Alpha A. Lee
Read original paper ↗ ·Phase Prediction via Crystal Structure Similarity in the Periodic Number Representation
Inorganic Chemistry · 2024-10-15
Cem Oran, Riccarda Caputo, Pierre Villars, Hasan Bilal Özcü, Feraye Hatice Canbaz, Adem Tekin
Read original paper ↗ ·Prediction methods for phonon transport properties of inorganic crystals: from traditional approaches to artificial intelligence
Nanoscale Horizons · 2024-10-29
Yi Wei, Zhixiang Liu, Guangzhao Qin
Read original paper ↗ ·Accelerating the Discovery of Binary Interconnect Materials: A Graph Neural Network and Diversity Ranking Workflow
SSRN Electronic Journal · 2025-01-01
Jiayi Tang, Liang Cao, Guanghui Xu, Manqi Dong, Junsheng Xia, Ming Li
Read original paper ↗ ·Artificial Intelligence and Generative Models for Materials Discovery: A Review
World Scientific Annual Review of Functional Materials · 2025-01-01
Albertus Denny Handoko, Riko I Made
Read original paper ↗ ·Artificial intelligence-driven revolution in nanozyme design: from serendipity to rational engineering
Materials Horizons · 2025-01-01
Yixin Yu, Mingzhen Zhang, Kelong Fan
Read original paper ↗ ·Machine Learning Algorithms for Applications in Materials Science I
Challenges and advances in computational chemistry and physics · 2025-01-01
Azizeh Abdolmaleki, Fereshteh Shiri, Shahin Ahmadi
Read original paper ↗ ·Machine Learning-Based Methods for Materials Inverse Design: A Review
Computers, materials & continua/Computers, materials & continua (Print) · 2025-01-01
Yingli Liu, Yuting Cui, Haihe Zhou, Tao Shen, Sheng Lei, Haibin Yuan, Jiancheng Yin
Read original paper ↗ ·Perspective: From Prediction to Design — A Position on Optimization-Centric Workflows in Materials ML
Journal of Computational and Data-Driven Materials Engineering · 2025-01-01
Mohammed Al-Farsi, Salim Al-Harthy, Nasser Al-Rawahi
Read original paper ↗ ·Reactivity descriptors for sulfur redox kinetics in lithium–sulfur batteries: from mechanistic insights to machine learning driven catalyst design
Chemical Society Reviews · 2025-01-01
Ziqing Yao, Yulu Zou, Shuangke Liu, Yujie Li, Qingpeng Guo, Chunman Zheng, Weiwei Sun
Read original paper ↗ ·Uncertainty in the era of machine learning for atomistic modeling
Digital Discovery · 2025-01-01
Federico Grasselli, Sanggyu Chong, Venkat Kapil, Silvia Bonfanti, Kevin Rossi
Read original paper ↗ ·Advanced Morphological and Material Engineering for High‐Performance Interfacial Iontronic Pressure Sensors
Advanced Science · 2025-01-22
Fengling Zhuo, Zhi Ding, Xi Yang, Fengjian Chu, Yulu Liu, Zhuoqing Gao, Hao Jin, Shurong Dong, Xiaozhi Wang, Jikui Luo
Read original paper ↗ ·Materials laboratories of the future for alloys, amorphous, and composite materials
MRS Bulletin · 2025-01-29
Sarbajit Banerjee, Ying Shirley Meng, Andrew M. Minor, Minghao Zhang, Nestor J. Zaluzec, Maria K. Y. Chan, Gerald T. Seidler, David W. McComb, Joshua Agar, Partha P. Mukherjee, Brent C. Melot, Karena W. Chapman, Beth S. Guiton, Robert F. Klie, Ian McCue, Paul M. Voyles, Ian G. Robertson, Ling Li, Miaofang Chi, Joel F. Destino, Arun Devaraj, Emmanuelle A. Marquis, Carlo U. Segre, Huinan Hannah Liu, Judith C. Yang, Kasra Momeni, Amit Misra, Niaz Abdolrahim, Julia E. Medvedeva, Wenjun Cai, Alp Sehirlioglu, Melike Dizbay-Onat, Apurva Mehta, Lori L. Graham-Brady, Benji Maruyama, Krishna Rajan, Jamie H. Warner, Mitra L. Taheri, Sergei V. Kalinin, B. Reeja‐Jayan, Udo D. Schwarz, Sindee L. Simon, Craig M. Brown
Read original paper ↗ ·Machine learning-assisted development of TMDs-type gas-sensitive materials for dissolved gases in oil-immersed transformer oils
Materials Today Chemistry · 2025-02-13
Qingbin Zeng, Mingxiang Wang, Yiyi Zhang, Jiefeng Liu, Min Gang Xu, Dachang Chen, Pengfei Jia
Read original paper ↗ ·Bayesian optimization of entropy-stabilized metal fluoride conversion cathodes and their synthesis
Journal of Power Sources · 2025-02-16
Aditya Sundar, Jehee Park, Haesun Park, Eungje Lee, Jae Jin Kim, Peter Zapol
Read original paper ↗ ·Recent Advances in High-Entropy Oxides for Photocatalytic Applications
ACS Materials Letters · 2025-02-17
Hanzo Tsubota, Andrei Jitianu, Go Kawamura
Read original paper ↗ ·Evolving biomaterials design from trial and error to intelligent innovation
Acta Biomaterialia · 2025-03-11
Ruiyue Hang, Ruiyue Hang, Xiaohong Yao, Long Bai, Ruiqiang Hang, Ruiqiang Hang
Read original paper ↗ ·Accelerated material discovery of high-performance Mg alloys via active learning and high throughput multi-objective informed Bayesian optimization
Materials Today Communications · 2025-04-09
Atwakyire Moses, Ying Gui, Ding Chen
Read original paper ↗ ·Generative deep learning for predicting ultrahigh lattice thermal conductivity materials
npj Computational Materials · 2025-04-10
Liben Guo, Yuanbin Liu, Zekun Chen, Hong-Ao Yang, Davide Donadio, Bing Cao
Read original paper ↗ ·Machine learning-assisted screening of transition metal-doped TMDs for binding energy and charge transfer prediction
Materials Today Communications · 2025-04-20
Pengfei Jia, Qingbin Zeng, Mingxiang Wang, Yiyi Zhang, Jiefeng Liu, Min Gang Xu, Dachang Chen
Read original paper ↗ ·Rapid Discovery of Graphene Nanoflakes with Desired Absorption Spectra Using DFT and Bayesian Optimization with Neural Network Kernel
The Journal of Physical Chemistry A · 2025-05-08
Şener Özönder, Hatice Kübra KÜÇÜKKARTAL
Read original paper ↗ ·AUGUR, a flexible and efficient optimization algorithm for identification of optimal adsorption sites
npj Computational Materials · 2025-05-17
Ioannis Kouroudis, Poonam Poonam, Neel Misciasci, Felix Mayr, Leon Müller, Zhaosu Gu, Alessio Gagliardi
Read original paper ↗ ·Predicting Lattice Thermal Conductivity in 2D Materials: Integrating Experiments, Theory, and Machine Learning
FlexTech · 2025-06-01
Jie Zhu, Yajing Sun
Read original paper ↗ ·A framework to evaluate machine learning crystal stability predictions
Nature Machine Intelligence · 2025-06-23
Janosh Riebesell, Rhys E. A. Goodall, Philipp Benner, Yuan Chiang, Bowen Deng, Gerbrand Ceder, Mark Asta, Alpha A. Lee, Anubhav Jain, Kristin Aslaug Persson
Read original paper ↗ ·Robust Lightweight Graph Neural Network Framework for Accelerating Crystal Structure Prediction
Journal of Chemical Information and Modeling · 2025-06-30
Rushikesh Pawar, Ashish Rout, Satadeep Bhattacharjee, Phani Motamarri
Read original paper ↗ ·Universal machine learning interatomic potentials poised to supplant DFT in modeling general defects in metals and random alloys
Machine Learning Science and Technology · 2025-06-30
Fei Shuang, Zixiong Wei, Kai Liu, Wei Gao, Poulumi Dey
Read original paper ↗ ·