Results 161 to 170 of about 11,756,121 (296)

Chemical Literature

, 1971
Quarterly supplement for "the regular journals of the American Chemical Society," containing annotated bibliographies of chemical documentation literature as well as information about meetings, conferences, awards, scholarships, and other news from the ...
American Chemical Society. Division of Chemical Literature., Rush, James E.   +1 more
core  

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

Strigolactones: From Fundamental Biology to Applications in Tree Breeding. [PDF]

Physiol Plant
Ma Z, Li W, Guo Q, Tibesigwa DG, Meng Q, Wang F.   +5 more
europepmc   +1 more source

Chemical Literature

, 1975
Quarterly supplement for "the regular journals of the American Chemical Society," containing annotated bibliographies of chemical documentation literature as well as information about meetings, conferences, awards, scholarships, and other news from the ...
American Chemical Society. Division of Chemical Literature., Revesz, Gabrielle S.   +1 more
core  

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

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

Physiological and Horticultural Responses of HLB-Affected 'Valencia' Orange Trees to Trunk-Injected Oxytetracycline and Streptomycin Formulations Differing in Adjuvant and pH. [PDF]

Antibiotics (Basel)
Silva I, Pereira J, Santra S, Albrecht U.   +3 more
europepmc   +1 more source

Functional Disorder at the Neural Interface: How Disordered Nanostructures Promote Proper Growth and Differentiation in In Vitro Neural Cultures

Advanced Science, EarlyView.
This work provides a practical guide for neuroengineers to design advanced neural interfaces, embracing and tailoring the concept of functional disorder. By bridging 2D and 3D in vitro models, this work highlights how non‐periodic, spatially heterogeneous, multiscale nanotopography can enable more physiologically relevant platforms for studying neural ...
F. Maita, E. Palmieri, I. Lucarini, L. Montaina, L. Maiolo   +4 more
wiley   +1 more source

PhyloNaP: a user-friendly database of phylogeny for natural product-producing enzymes. [PDF]

Bioinformatics
Korenskaia A, Adamek M, Szenei J, Vader L, Blin K, Weber T, Ziemert N.   +6 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