Papers citing Statistical inference and adaptive design for materials discovery
Enhancing User Experience with AI-Generated CSS Through Machine Learning for Adaptive Web Design
Venue not supplied by source · 2024
Dheeraj Yadav, Rajesh Ojha, Priya Guruprakash Rao, Sreeprasad Govindankutty, Om Goel, Sangeet Vashishtha
Read original paper ↗ ·Design of high-temperature NiTiCuHf shape memory alloys with minimum thermal hysteresis using Bayesian optimization
Acta Materialia · 2024
John Broucek, Danial Khatamsaz, Cem Cakirhan, Sina Hossein Zadeh, M. Fan, Guillermo Vazquez, Kadri Can Atli, Xiaoning Qian, Raymundo Arróyave, İbrahim Karaman
Read original paper ↗ ·Machine learning-based prediction of the mechanical properties of β titanium shape memory alloys
Journal of Materials Research and Technology · 2024
Naoki Nohira, Taichi Ichisawa, Masaki Tahara, Itsuo Kumazawa, Hideki HOSODA
Read original paper ↗ ·Leveraging feature gradient for efficient acquisition function maximization in material composition design
Digital Discovery · 2025
Yuehui Xian, Yunfan Wang, Pengfei Dang, Xinquan Wan, Yumei Zhou, Xiangdong Ding, Jun Sun, Dezhen Xue
Read original paper ↗ ·Machine learning assisted development of Heusler alloys for high magnetic moment
Computational Materials Science · 2025
Kexin Liu, Kexin Liu, Bingxin Ge, Fangyu Liu, Fangyu Liu, Miaojie Feng, Yong Ji, Yan Li, Wu Lu, Xuefan Jiang, Yongsheng Liu
Read original paper ↗ ·Friction in Cylindrical Joints
Lubricants · 2025
Andrei Marius Mihalache, Vasile Merticaru, Vasile Ermolai, Oana Dodun, Gheorghe Nagîț, Adelina Hrițuc, Marius Ionuț Rîpanu, Laurențiu Slătineanu
Read original paper ↗ ·Materials Engineering and Artificial Intelligence Innovations: A Comprehensive Review
Journal of Materials Engineering and Performance · 2025
Khalid Bouiti, Youssra El Hamdouni, Najoua Labjar, Houda Labjar, Souad Najoua Lagmiri, Hamid Nasrellah, Souad El Hajjaji
Read original paper ↗ ·A machine learning approach to mitigating hardness degradation in the heat-affected zone of AA7075-T6 alloy
Materials Today Communications · 2025
Yizhou Lu, Shubhrodev Bhowmik, Benjamin Rhoads, Nilesh Kumar, Samrat Choudhury
Read original paper ↗ ·Data selection strategies for minimizing measurement time in materials characterization
Scientific Reports · 2025
Alexander Liehr, Kristina Dingel, Daniel Kottke, Sebastian Degener, David Meier, Bernhard Sick, Thomas Niendorf
Read original paper ↗ ·Machine learning approaches for intentional materials engineering
MRS Bulletin · 2025
Yu‐chen Karen Chen‐Wiegart, Norbert W. Huber, Kevin G. Yager
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
Şener Özönder, Hatice Kübra KÜÇÜKKARTAL
Read original paper ↗ ·Unlocking the black box beyond Bayesian global optimization for materials design using reinforcement learning
npj Computational Materials · 2025
Yuehui Xian, Xiangdong Ding, Xue Feng Jiang, Yumei Zhou, Jun Sun, Dezhen Xue, Turab Lookman
Read original paper ↗ ·Corrosion resistant performance prediction in high-entropy alloys: A framework for model, interpretation and multi-dimensional visualization
Corrosion Science · 2025
Guangxuan Song, Dongmei Fu, Weiwei Chang, Zhongheng Fu, Lingwei Ma, Dawei Zhang
Read original paper ↗ ·Characterization of Two Novel NiTiHf Shape Memory Alloys Designed by Machine Learning Utilizing Novel Experimental Techniques
Shape Memory and Superelasticity · 2025
Demircan Canadinç, Elmar Jonas Breitbach, A.A. Catal
Read original paper ↗ ·Optimization of print parameters for batch and continuous manufacturing of three-dimensional (3D) printed dosage forms using artificial intelligence and machine learning
Drug Delivery and Translational Research · 2025
Kshitij Chitnis, Yizhou Lu, Benjamin Rhoads, Leela Raghava Jaidev Chakka, Samrat Choudhury, Mohammed Maniruzzaman
Read original paper ↗ ·Accelerating materials discovery in heterogeneous composition-property design spaces via collaborative Bayesian optimization
Materials & Design · 2025
Tuba Dolar, Nicolò Maria della Ventura, Florent Mignerot, Zihan Wang, Hannah C. Howard, Christopher T. Kassner, Haydn N. G. Wadley, Daniel S. Gianola, Wei Chen
Read original paper ↗ ·Innovative technologies for succinic acid fermentation: A path to sustainable production
Chemical Engineering and Processing - Process Intensification · 2025
Diana Carolina Barriga-Gómez, Juan Federico Herrera‐Ruiz, Ricardo Morales-Rodríguez, Oscar Andrés Prado‐Rubio
Read original paper ↗ ·Interpretable machine learning-assisted discovery of BaTiO 3 -based ceramics with enhanced dielectric constant
Journal of Materials Chemistry C · 2026
J X Liu, Hua Hao, Xingzhong Liu, Jiahao Tian, Bangzhi Peng, Chunze Wei, Jinglong Fan, Hanxing Liu
Read original paper ↗ ·A machine learning strategy for rapid design of preparation parameters in zero-sample complex alloy
Transactions of Nonferrous Metals Society of China · 2026
Hui-qiang MA, Hong-tao ZHANG, Fu Hua-dong, Jing-tai SUN, Jian-xin XIE
Read original paper ↗ ·Bayesian Optimization of Catalysis with In-Context Learning
ACS Central Science · 2026
Mayk Caldas Ramos, Shane S. Michtavy, Andrew D. White, Marc D. Porosoff
Read original paper ↗ ·Interpretable Machine Learning Framework for Nb─Si Based Alloy Design with Enhanced Fracture Toughness
Advanced Science · 2026
Dezhi Chen, Chao Xu, Jingyue Yu, Qi Ming Wang, Hongze Fang, Shuo Yin, Turab Lookman, Ruirun Chen
Read original paper ↗ ·Unveiling bootstrap uncertainty bias: understanding bagging efficiency over Gaussian processes in materials active learning
npj Computational Materials · 2026
Zishuo Lan, Yiming Chen, Xiaobing Hu, junjie li, Dezhen Xue, Jincheng Wang
Read original paper ↗ ·Compact and interpretable strength–ductility prediction of heat-treatable aluminum alloys using physics-retained explainable multi-target machine learning
Engineering Research Express · 2026
S Kumar Reddy Mallidi, Tejinder Kaur, Ankita Chhikara, D. Rosy Salomi Victoria, Payala Krishnanjaneyulu
Read original paper ↗ ·Accelerated design of ultra-strong Ni–Al–Ti–Cr–V high-entropy alloys via interpretable CatBoost machine learning
Physica B Condensed Matter · 2026
Inzamam ul Haq, Kiran Riaz, Muhammad Faisal, Airu Wang, Ishfaq Ahmed, Guoqiang Xie
Read original paper ↗ ·