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Quantitative assessment of the structure of (Na2O)0.1-(V2O5)0.6-(TeO2)0.3 glass: An ab initio and machine learning interatomic potential study of oxide systems with mixed-valence钠氧-五氧化二钒-二氧化碲玻璃（(Na2O)0.1-(V2O5)0.6-(TeO2)0.3）结构的定量评估：一种基于第一性原理和机器学习的混合价态氧化物体系原子间势能研究
Shuaib, Firas; Piotrowski, Remi; Delaizir, Gaelle; Geffroy, Pierre-Marie; Wendji, Steve Dave Wansi; Massobrio, Carlo; Boero, Mauro; Ori, Guido; Thomas, Philippe; Masson, Olivier; Bouzid, Assil.png)
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收藏 Prediction of heavy metal adsorption by activated carbon using machine learningLamsiah, S.; Benhadj, M.; Alouiz, I.; Bouhadda, M.; Amarouch, M.Y.; Mazouzi, D.; Sennoune, M. 利用机器学习预测活性炭对重金属的吸附。Artif. Intell. Chem. 2026, 4, 100117. [Google Scholar] [CrossRef]Lamsiah, Said; Benhadj, Mouhcine; Alouiz, Imad; Bouhadda, Mohamed; Amarouch, Mohamed Yassine; Mazouzi, Driss; Sennoune, Mohamed 分享 收藏 A computational pipeline combining machine learning and molecular simulations identifies repurposed PI3Kα inhibitors from FDA-approved drugs结合机器学习和分子模拟的计算流程从FDA批准的药物中识别出重新利用的PI3Kα抑制剂Biniyam, Prince Danan; Kayeri, Naomi; Ayihi, Seth Junior; Haider, Syed Arslan; Dzitorwoko, Hope; Adu-Adjei, Kwabena; Osei, Michael; Ohene-Adu, Victoria; Frimpong, Portia; Penajah, Clifford Tignangnen; Amissah, Silas Boakye; Mazhar, Nimra; Amengor, Cedric Dzidzor Kodjo 分享 收藏 Finding low-energy experimental metal-organic frameworks for carbon capture via Bayesian optimization in the manifold subspace of geometric descriptors通过在几何描述符流形子空间中的贝叶斯优化寻找用于碳捕获的低能实验金属-有机框架Moreno, Clarence Shane; Penaso, Jemuel James; Valencia, Earl; Cortez, Callista Joyce; Ebriega, Bren Daniel; Pasaje, Jessa; Serognas, Renner Danielle; Panaligan, Allison Dale; De Leon, Rizalinda; Pilario, Karl Ezra 分享 收藏 
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