Results 231 to 240 of about 88,237 (314)

Machine Learning Design of Tungsten Alloys With Strength–Ductility Synergy

Materials Genome Engineering Advances, EarlyView.
This study integrates machine learning with solid solution softening knowledge to design multielement tungsten alloys with strength‐ductility synergy. ML models identify promising compositions within W‐ Ta ‐ Re system that exhibit pronounced chemical short‐range order, resulting in exceptional mechanical performance. Experimental validation through the
Juan Ding, Jiayi Liu, Jiatao Zhou, Baishan Chen, Yunzhu Ma, Wensheng Liu, Yufeng Huang, Chaoping Liang   +7 more
wiley   +1 more source

A Review and Experimental Analysis of Supervised Learning Systems and Methods for Protein-Protein Interaction Detection. [PDF]

Int J Mol Sci
Taha K.
europepmc   +1 more source

AI/ML Enabled High‐Throughput Design and Synthesis for Energetic Molecules

Materials Genome Engineering Advances, EarlyView.
Novel design methods for a special kind of functional molecules — the energetic molecules—are summarized. Both classic Machine Learning (ML) and Artificial Intelligence (AI) generative models are utilized for the high‐throughput design of these energetic molecules, and a sort of high‐energy low‐sensitivity molecules are obtained.
Wen Qian
wiley   +1 more source

Radiomics-enhanced ¹⁸F-AV45 PET/MRI for integrative assessment and centiloid estimation of amyloid-β burden in Alzheimer's disease. [PDF]

Eur Radiol Exp
Yuan Z, Zhang J, Zhou Z, Chen X, Qi N, Wang W, Cheng X, Lin Y, Zhao J.   +8 more
europepmc   +1 more source

Distinguishing Intrinsic Descriptors of Grain Boundary Segregation Between Metallic and Non‐Metallic Solutes in BCC Fe via Machine Learning

Materials Genome Engineering Advances, EarlyView.
A combined DFT–interpretable machine learning framework identifies the intrinsic descriptors governing grain boundary segregation in BCC Fe. Geometric effects dominate metallic solutes, whereas electronic bonding controls nonmetallic solutes, exhibiting an overall contrasting trend Voronoi volume–segregation energy relationships.
Xinyuan Zhang, Feiyang Wang, Honghui Wu, Xiaoye Zhou, Shuize Wang, Junheng Gao, Haitao Zhao, Chaolei Zhang, Yuhe Huang, Jun Lu, Xinping Mao   +10 more
wiley   +1 more source

Unified comparison of machine learning paradigms for blood transfusion prediction in pediatric congenital heart surgery. [PDF]

iScience
Yin MW, Li J, Huang J, Zhu Z, Chen BH, Shi Z, Jiang Q, Chen XJ, Yu G.   +8 more
europepmc   +1 more source

Physics‐Informed Generative Machine Learning for Designing Crack‐Free γ′‐Strengthened Ni‐Based Superalloys for Laser Powder Bed Fusion

Materials Genome Engineering Advances, EarlyView.
This research proposes a physics‐informed generative machine learning framework to design SHA800, a crack‐free γ′‐strengthened nickel‐based superalloy for laser powder bed fusion, achieving a 43% γ′ volume fraction and 587 HV0.2 hardness. ABSTRACT Fabricating γ′‐strengthened nickel‐based superalloys via laser powder bed fusion (LPBF) faces significant ...
Kai Guo, Dongwei Guan, Tao Hu, Yaqing Hou, Xiaoqun Li, Zhongnan Bi, Songzhe Xu, Hao Yu, Wei Xu, Weidong Xuan, Chaoyue Chen, Zhongming Ren   +11 more
wiley   +1 more source

Predictive modelling of duodenal stump leakage after gastric cancer and long-term oncological outcomes. [PDF]

Transl Cancer Res
Shu X, Wu A, Wang X, Yuan C, Zhang G, Xiong J, Chen W, Wang G, Li Z, Jie Z.   +9 more
europepmc   +1 more source

Symbolic Regression‐Guided Feature Engineering for Predicting Magnetization in Cu‐Based Alloys Under Data‐Scarce Conditions

Materials Genome Engineering Advances, EarlyView.
A symbolic regression approach (SISSO) with physics‐informed feature engineering achieves high‐accuracy prediction of magnetic properties in Cu‐based alloys under data‐scarce conditions. The framework offers an interpretable and transferable strategy for accelerated alloy design.
Buyang Ma, Jiejie Li, Qiuju Zhang, Fenghua Chen, Bo Li, Ziqi Tian, Liang Chen   +6 more
wiley   +1 more source