Search Results

Development of a pKa predictor (pKaLearn) by leveraging teaching experience to improve machine learning.

• Authors : Genzling, Jérôme¹ (AUTHOR); Luo, Ziling¹ (AUTHOR); Weiser, Benjamin¹ (AUTHOR)

• Source: Communications Chemistry. 3/26/2026, Vol. 9 Issue 1, p1-12. 12p.

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Accelerated Discovery of Ternary Gold Alloy Materials with Low Resistivity via an Interpretable Machine Learning Strategy.

• Authors : Wang X; Shanghai Materials Genome Institute, Shanghai University, 200444, Shanghai, P. R. China.; School of Physics and Electronic Science, East China Normal University, 200241, Shanghai, P. R. China.

Subjects: Gold Alloys* ; Machine Learning*

• Source: Chemistry, an Asian journal [Chem Asian J] 2022 Nov 16; Vol. 17 (22), pp. e202200771. Date of Electronic Publication: 2022 Sep 26.Publisher: Wiley-VCH Country of Publication: Germany NLM ID: 101294643 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1861-471X

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Rational Design of Perovskite Anode Materials in Solid Oxide Electrolysis Cells: Machine Learning-Assisted Prediction of Thermal Expansion Coefficients

• Authors : Ke Wang; Ming Lei; Xing-Gui Zhou

Subjects: Biochemistry; Medicine; Cell Biology

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Prediction of the Adsorption Behaviors of Radionuclides onto Bentonites Using a Machine Learning Method.

• Authors : Kim, Do-Hyeon; Lee, Jun-Yeop

Subjects: MACHINE learning; NUCLEAR energy; OXIDATION states

• Source: Minerals (2075-163X); Oct2022, Vol. 12 Issue 10, p1207-N.PAG, 12p

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Recreation of the periodic table with an unsupervised machine learning algorithm.

• Authors : Kusaba, Minoru; Liu, Chang; Koyama, Yukinori

Subjects: PERIODIC table of the elements; MACHINE learning; ELECTRONEGATIVITYMENDELEYEV, Dmitry Ivanovich, 1834-1907

• Source: Scientific Reports; 2/26/2021, Vol. 11 Issue 1, p1-10, 10p

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Universal and Interpretable Descriptor to Design Dual‐atom Catalysts for Multi‐Type C–C Coupling with Ultrahigh C2+ Yield.

• Authors : Gao, Yuming; Guo, Chenyi; Hong, Juncheng

Subjects: CATALYSTS; COUPLING reactions (Chemistry); BIMETALLIC catalysts

• Source: Advanced Science; Dec2025, Vol. 12 Issue 45, p1-12, 12p

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Automating structure–activity analysis for electrochemical nitrogen reduction catalyst design through multi-agent collaborations.

• Authors : Hu, Xu; Chen, Suya; Chen, Letian

Subjects: STRUCTURE-activity relationships; MACHINE learning; ELECTRODE efficiency

• Source: National Science Review; Nov2025, Vol. 12 Issue 11, p1-14, 14p

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Machine Learning-Driven Design and Optimization of Multi-Metal Nitride Hard Coatings via Multi-Arc Ion Plating Using Genetic Algorithm and Support Vector Regression.

• Authors : Gu, Yu; Wang, Jiayue; Zhang, Jun

Subjects: MACHINE learning; METAL nitrides; GENETIC algorithms

• Source: Materials (1996-1944); Aug2025, Vol. 18 Issue 15, p3478, 29p

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Toward General Design of Mn-Based Layered Oxide Cathodes for Sodium-Ion Batteries: From Thermodynamic Principles to Entropy Engineering.

• Authors : Dong, Li¹ (AUTHOR); Qian, Xiang-Yu¹ (AUTHOR); Xiong, Jian¹ (AUTHOR)

• Source: Molecules. Mar2026, Vol. 31 Issue 5, p836. 38p.

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Insights into Segregation and Aggregation in Dilute Atom Alloy Catalysts Using DFT and Machine Learning.

• Authors : Sison, Arnold D.; Quaynor, Michael M.N.A.; Navodye, S. A. Keishana

Subjects: DILUTE alloys; HETEROGENEOUS catalysis; HETEROGENEOUS catalysts

• Source: ChemCatChem; 4/4/2025, Vol. 17 Issue 7, p1-14, 14p

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