Machine learning for molecular and materials science

Citations

Cited by:

1. Yuanyuan Jiang & Zongwei Yang & Jiali Guo & Hongzhen Li & Yijing Liu & Yanzhi Guo & Menglong Li & Xuemei Pu, 2021. "Coupling complementary strategy to flexible graph neural network for quick discovery of coformer in diverse co-crystal materials," Nature Communications, Nature, vol. 12(1), pages 1-14, December.
2. Zhiyuan Han & An Chen & Zejian Li & Mengtian Zhang & Zhilong Wang & Lixue Yang & Runhua Gao & Yeyang Jia & Guanjun Ji & Zhoujie Lao & Xiao Xiao & Kehao Tao & Jing Gao & Wei Lv & Tianshuai Wang & Jinji, 2024. "Machine learning-based design of electrocatalytic materials towards high-energy lithium||sulfur batteries development," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
3. Hung Vo Thanh & Sajad Ebrahimnia Taremsari & Benyamin Ranjbar & Hossein Mashhadimoslem & Ehsan Rahimi & Mohammad Rahimi & Ali Elkamel, 2023. "Hydrogen Storage on Porous Carbon Adsorbents: Rediscovery by Nature-Derived Algorithms in Random Forest Machine Learning Model," Energies, MDPI, vol. 16(5), pages 1-19, February.
4. Magdalena Osial & Agnieszka Pregowska, 2022. "The Application of Artificial Intelligence in Magnetic Hyperthermia Based Research," Future Internet, MDPI, vol. 14(12), pages 1-17, November.
5. Han Li & Ruotian Zhang & Yaosen Min & Dacheng Ma & Dan Zhao & Jianyang Zeng, 2023. "A knowledge-guided pre-training framework for improving molecular representation learning," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
6. Deeptha Ishwar & Rupa Haldavnekar & Krishnan Venkatakrishnan & Bo Tan, 2022. "Minimally invasive detection of cancer using metabolic changes in tumor-associated natural killer cells with Oncoimmune probes," Nature Communications, Nature, vol. 13(1), pages 1-20, December.
7. Kai Li & Jifeng Wang & Yuanyuan Song & Ying Wang, 2023. "Machine learning-guided discovery of ionic polymer electrolytes for lithium metal batteries," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
8. Bin Xing & Timothy J. Rupert & Xiaoqing Pan & Penghui Cao, 2024. "Neural network kinetics for exploring diffusion multiplicity and chemical ordering in compositionally complex materials," Nature Communications, Nature, vol. 15(1), pages 1-10, December.
9. Yilei Wu & Chang-Feng Wang & Ming-Gang Ju & Qiangqiang Jia & Qionghua Zhou & Shuaihua Lu & Xinying Gao & Yi Zhang & Jinlan Wang, 2024. "Universal machine learning aided synthesis approach of two-dimensional perovskites in a typical laboratory," Nature Communications, Nature, vol. 15(1), pages 1-10, December.
10. Zeyin Yan & Dacong Wei & Xin Li & Lung Wa Chung, 2024. "Accelerating reliable multiscale quantum refinement of protein–drug systems enabled by machine learning," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
11. Tian Xie & Arthur France-Lanord & Yanming Wang & Jeffrey Lopez & Michael A. Stolberg & Megan Hill & Graham Michael Leverick & Rafael Gomez-Bombarelli & Jeremiah A. Johnson & Yang Shao-Horn & Jeffrey C, 2022. "Accelerating amorphous polymer electrolyte screening by learning to reduce errors in molecular dynamics simulated properties," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
12. Li, Yi & Liu, Kailong & Foley, Aoife M. & Zülke, Alana & Berecibar, Maitane & Nanini-Maury, Elise & Van Mierlo, Joeri & Hoster, Harry E., 2019. "Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 113(C), pages 1-1.
13. Vishu Gupta & Kamal Choudhary & Francesca Tavazza & Carelyn Campbell & Wei-keng Liao & Alok Choudhary & Ankit Agrawal, 2021. "Cross-property deep transfer learning framework for enhanced predictive analytics on small materials data," Nature Communications, Nature, vol. 12(1), pages 1-10, December.
14. Huziel E. Sauceda & Luis E. Gálvez-González & Stefan Chmiela & Lauro Oliver Paz-Borbón & Klaus-Robert Müller & Alexandre Tkatchenko, 2022. "BIGDML—Towards accurate quantum machine learning force fields for materials," Nature Communications, Nature, vol. 13(1), pages 1-16, December.
15. O. V. Mythreyi & M. Rohith Srinivaas & Tigga Amit Kumar & R. Jayaganthan, 2021. "Machine-Learning-Based Prediction of Corrosion Behavior in Additively Manufactured Inconel 718," Data, MDPI, vol. 6(8), pages 1-16, July.
16. Chao Liang & Yilimiranmu Rouzhahong & Caiyuan Ye & Chong Li & Biao Wang & Huashan Li, 2023. "Material symmetry recognition and property prediction accomplished by crystal capsule representation," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
17. Niklas W. A. Gebauer & Michael Gastegger & Stefaan S. P. Hessmann & Klaus-Robert Müller & Kristof T. Schütt, 2022. "Inverse design of 3d molecular structures with conditional generative neural networks," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
18. Yasuhiro Yoshikai & Tadahaya Mizuno & Shumpei Nemoto & Hiroyuki Kusuhara, 2024. "Difficulty in chirality recognition for Transformer architectures learning chemical structures from string representations," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
19. Manuel Cordova & Pinelopi Moutzouri & Sten O. Nilsson Lill & Alexander Cousen & Martin Kearns & Stefan T. Norberg & Anna Svensk Ankarberg & James McCabe & Arthur C. Pinon & Staffan Schantz & Lyndon Em, 2023. "Atomic-level structure determination of amorphous molecular solids by NMR," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
20. Sukriti Manna & Troy D. Loeffler & Rohit Batra & Suvo Banik & Henry Chan & Bilvin Varughese & Kiran Sasikumar & Michael Sternberg & Tom Peterka & Mathew J. Cherukara & Stephen K. Gray & Bobby G. Sumpt, 2022. "Learning in continuous action space for developing high dimensional potential energy models," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
21. Özhan Şimşek, 2024. "Machine Learning Offers Insights into the Impact of In Vitro Drought Stress on Strawberry Cultivars," Agriculture, MDPI, vol. 14(2), pages 1-17, February.
22. Yan Duan & Lorena E. Rosaleny & Joana T. Coutinho & Silvia Giménez-Santamarina & Allen Scheie & José J. Baldoví & Salvador Cardona-Serra & Alejandro Gaita-Ariño, 2022. "Data-driven design of molecular nanomagnets," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
23. Sarmad Dashti Latif & Ali Najah Ahmed, 2023. "A review of deep learning and machine learning techniques for hydrological inflow forecasting," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(11), pages 12189-12216, November.
24. Shang Zhu & Bharath Ramsundar & Emil Annevelink & Hongyi Lin & Adarsh Dave & Pin-Wen Guan & Kevin Gering & Venkatasubramanian Viswanathan, 2024. "Differentiable modeling and optimization of non-aqueous Li-based battery electrolyte solutions using geometric deep learning," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
25. Zhang, Jianyu & Lu, Wei, 2022. "Sparse data machine learning for battery health estimation and optimal design incorporating material characteristics," Applied Energy, Elsevier, vol. 307(C).
26. Jose Antonio Garrido Torres & Vahe Gharakhanyan & Nongnuch Artrith & Tobias Hoffmann Eegholm & Alexander Urban, 2021. "Augmenting zero-Kelvin quantum mechanics with machine learning for the prediction of chemical reactions at high temperatures," Nature Communications, Nature, vol. 12(1), pages 1-9, December.
27. Shufeng Kong & Francesco Ricci & Dan Guevarra & Jeffrey B. Neaton & Carla P. Gomes & John M. Gregoire, 2022. "Density of states prediction for materials discovery via contrastive learning from probabilistic embeddings," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
28. Cong, Jian & Ma, Tianzeng & Chang, Zheshao & Zhang, Qiangqiang & Akhatov, Jasurjon S. & Fu, Mingkai & Li, Xin, 2023. "Neural network and experimental thermodynamics study of YCrO3-δ for efficient solar thermochemical hydrogen production," Renewable Energy, Elsevier, vol. 213(C), pages 1-10.
29. Gang Wang & Shinya Mine & Duotian Chen & Yuan Jing & Kah Wei Ting & Taichi Yamaguchi & Motoshi Takao & Zen Maeno & Ichigaku Takigawa & Koichi Matsushita & Ken-ichi Shimizu & Takashi Toyao, 2023. "Accelerated discovery of multi-elemental reverse water-gas shift catalysts using extrapolative machine learning approach," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
30. Yang Shi & Junyu Ren & Guanyu Chen & Wei Liu & Chuqi Jin & Xiangyu Guo & Yu Yu & Xinliang Zhang, 2022. "Nonlinear germanium-silicon photodiode for activation and monitoring in photonic neuromorphic networks," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
31. Snehi Shrestha & Kieran James Barvenik & Tianle Chen & Haochen Yang & Yang Li & Meera Muthachi Kesavan & Joshua M. Little & Hayden C. Whitley & Zi Teng & Yaguang Luo & Eleonora Tubaldi & Po-Yen Chen, 2024. "Machine intelligence accelerated design of conductive MXene aerogels with programmable properties," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
32. Ribeiro, Haroldo V. & Lopes, Diego D. & Pessa, Arthur A.B. & Martins, Alvaro F. & da Cunha, Bruno R. & Gonçalves, Sebastián & Lenzi, Ervin K. & Hanley, Quentin S. & Perc, Matjaž, 2023. "Deep learning criminal networks," Chaos, Solitons & Fractals, Elsevier, vol. 172(C).
33. Xuan-Kun Li & Jian-Xu Ma & Xiang-Yu Li & Jun-Jie Hu & Chuan-Yang Ding & Feng-Kai Han & Xiao-Min Guo & Xi Tan & Xian-Min Jin, 2024. "High-efficiency reinforcement learning with hybrid architecture photonic integrated circuit," Nature Communications, Nature, vol. 15(1), pages 1-10, December.
34. Mojan Omidvar & Hangfeng Zhang & Achintha Avin Ihalage & Theo Graves Saunders & Henry Giddens & Michael Forrester & Sajad Haq & Yang Hao, 2024. "Accelerated discovery of perovskite solid solutions through automated materials synthesis and characterization," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
35. Xinyu Chen & Yufeng Xie & Yaochen Sheng & Hongwei Tang & Zeming Wang & Yu Wang & Yin Wang & Fuyou Liao & Jingyi Ma & Xiaojiao Guo & Ling Tong & Hanqi Liu & Hao Liu & Tianxiang Wu & Jiaxin Cao & Sitong, 2021. "Wafer-scale functional circuits based on two dimensional semiconductors with fabrication optimized by machine learning," Nature Communications, Nature, vol. 12(1), pages 1-8, December.
36. Wang, Zhengxin & Peng, Xinggan & Xia, Ao & Shah, Akeel A. & Yan, Huchao & Huang, Yun & Zhu, Xianqing & Zhu, Xun & Liao, Qiang, 2023. "Comparison of machine learning methods for predicting the methane production from anaerobic digestion of lignocellulosic biomass," Energy, Elsevier, vol. 263(PD).
37. Ferraro, Carla & Hemsley, Alicia & Sands, Sean, 2023. "Embracing diversity, equity, and inclusion (DEI): Considerations and opportunities for brand managers," Business Horizons, Elsevier, vol. 66(4), pages 463-479.
38. Yi, Yong & Wang, Liming & Chen, Zhengying, 2021. "Adaptive global kernel interval SVR-based machine learning for accelerated dielectric constant prediction of polymer-based dielectric energy storage," Renewable Energy, Elsevier, vol. 176(C), pages 81-88.
39. Shan, Pengyue & Bai, Xue & Jiang, Qi & Chen, Yunjian & Lu, Sen & Song, Pei & Jia, Zepeng & Xiao, Taiyang & Han, Yang & Wang, Yazhou & Liu, Tong & Cui, Hong & Feng, Rong & Kang, Qin & Liang, Zhiyong & , 2023. "Bilayer MN4-O-MN4 by bridge-bonded oxygen ligands: Machine learning to accelerate the design of bifunctional electrocatalysts," Renewable Energy, Elsevier, vol. 203(C), pages 445-454.
40. Kihoon Bang & Doosun Hong & Youngtae Park & Donghun Kim & Sang Soo Han & Hyuck Mo Lee, 2023. "Machine learning-enabled exploration of the electrochemical stability of real-scale metallic nanoparticles," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
41. Kangming Li & Daniel Persaud & Kamal Choudhary & Brian DeCost & Michael Greenwood & Jason Hattrick-Simpers, 2023. "Exploiting redundancy in large materials datasets for efficient machine learning with less data," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
42. Pessa, Arthur A.B. & Zola, Rafael S. & Perc, Matjaž & Ribeiro, Haroldo V., 2022. "Determining liquid crystal properties with ordinal networks and machine learning," Chaos, Solitons & Fractals, Elsevier, vol. 154(C).
43. Oscar Méndez-Lucio & Christos A. Nicolaou & Berton Earnshaw, 2024. "MolE: a foundation model for molecular graphs using disentangled attention," Nature Communications, Nature, vol. 15(1), pages 1-9, December.
44. Juan-Ni Wu & Tong Wang & Yue Chen & Li-Juan Tang & Hai-Long Wu & Ru-Qin Yu, 2024. "t-SMILES: a fragment-based molecular representation framework for de novo ligand design," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
45. Xinyu Chen & Shuaihua Lu & Qian Chen & Qionghua Zhou & Jinlan Wang, 2024. "From bulk effective mass to 2D carrier mobility accurate prediction via adversarial transfer learning," Nature Communications, Nature, vol. 15(1), pages 1-9, December.