Results 131 to 140 of about 69,371,906 (219)

Sustainable Materials Design With Multi‐Modal Artificial Intelligence

Advanced Science, EarlyView.
Critical mineral scarcity, high embodied carbon, and persistent pollution from materials processing intensify the need for sustainable materials design. This review frames the problem as multi‐objective optimization under heterogeneous, high‐dimensional evidence and highlights multi‐modal AI as an enabling pathway.
Tianyi Xu, Tianshuo Wei, Yan Ge, Bo Peng, Yue Li, Maolin Wang, Peng Wen, Chao Yang, Ye Wei   +8 more
wiley   +1 more source

Rhizosphere Functional Plasticity and the Keystone Taxon <i>Sphingomonas</i> Facilitate Sweet Cherry Adaptation to Semi-Arid Stress. [PDF]

Plants (Basel)
Zhang L, Dong J, Zhao J, Jiang H, Zhang W.   +4 more
europepmc   +1 more source

Unifying Composition and Process Design: A Heterogeneous Graph Neural Network for Discovering High‐Performance Cu Alloys

Advanced Science, EarlyView.
By overcoming the fixed‐path limitations of conventional machine learning, a heterogeneous graph neural network fundamentally reconstructs material data representation. Integrating variable processing sequences with intrinsic elemental features, this framework enables exploratory optimization across high‐dimensional spaces.
Jie Yin, Qian Lei, Wei Yan, Yue Li, Tong Xie, Shuang Zhou, Chong Wang, Bram Hoex, Qiang Long, Caoyang Jiang, Min Song, Zhou Li, Zhangwei Wang   +12 more
wiley   +1 more source

Mitochondrial Genome Comparison and Phylogenetic Analysis of the Family Carabidae (Coleoptera: Adephaga). [PDF]

Biology (Basel)
Zhan J, Zhu P, Liu K, Shen R, Liang H, Wang X, Bai M.   +6 more
europepmc   +1 more source

Broadening Hard‐Magnet Discovery Beyond Symmetry Constraints via Unified Effective Anisotropy

Advanced Science, EarlyView.
A unified effective‐anisotropy descriptor (Keff) extends hard‐magnet screening across all seven crystal systems, beyond the uniaxial restriction of conventional searches. Machine‐learning screening of 9320 known ferromagnets and diffusion‐model generation together yield 38 rare‐earth‐free or ‐lean candidates with DFT‐validated magnetic hardness (κ > 1),
Hojae Kim, Hyeondeok Shin, Kyungju Nam, Seunghyo Noh, Donghwi Kim, Yousung Jung   +5 more
wiley   +1 more source

Foliar and Root Dynamic Analyses Reveal a Coordinated Defense Regulatory Network Against <i>Aeolesthes induta</i> Infestation in Tea Plants. [PDF]

Insects
Lu C, Zhang J, Chen K, Zhang X, Du X, Feng F, Cai P, Hong Y.   +7 more
europepmc   +1 more source

High‐Throughput Screening and Interpretable Machine Learning for Rational Design of Bimetallic Catalysts for Methane Activation

Advanced Science, EarlyView.
ABSTRACT Methane's efficient catalytic removal is vital for sustainable development. Bimetallic catalysts, though promising for methane activation, pose a design challenge due to their complex compositional space. This work introduces an integrated framework that combines high‐throughput density functional theory (DFT) and interpretable machine ...
Mingzhang Pan, Tian Zhang, Jiawei Dong, Yubao Xie, Kaifeng Zhong, Wei Guan, Haiqiao Wei, Changcheng Fu, Yaqiong Su   +8 more
wiley   +1 more source

What we (still don't) know about wood-degrading fungi: an overview of threats and utilization potentials. [PDF]

Appl Microbiol Biotechnol
Tamayo E, Bachmann F, Cheng T, Felle TK, Nussbaumer M, Benz JP.   +5 more
europepmc   +1 more source

Data‐Driven Modeling of Composition–Processing–Microstructure Relations for Recycled Aluminum Cast Alloys

Advanced Science, EarlyView.
Interpretable machine learning reveals how composition and processing govern the formation and microstructural burden of Fe‐rich intermetallic compounds in recycled Al–Si–Fe–Mn alloys. By separating morphology selection from morphology‐conditioned burden partitioning, this framework shows that identical Fe contents can yield different intermetallic ...
Jaemin Wang, Waleed Mohammed, Dierk Raabe   +2 more
wiley   +1 more source

Genome-Wide Superfamily Profiling Reveals Key Regulator <i>LsAP2/ERF10</i> That Governs Petal Morphology in <i>Lagerstroemia speciosa</i>. [PDF]

Plants (Basel)
Wan Z, Lin M, Huang Y, Yu C, Zhang Q, Pan H, Zheng T.   +6 more
europepmc   +1 more source