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RESEARCH PAPER · APL Materials · 2024

Material discovery and modeling acceleration via machine learning

Carmine Zuccarini, Karthikeyan Ramachandran, D.D. Jayaseelan

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Material discovery and modeling acceleration via machine learningOriginalAdvances in artificial intelligence for artificial metamaterials31Machine Learning for Custom-Designed Light Emitters in Biomedicine15Redefining electrolyte efficiency: bridging the gap with a systematic samarium–copper co-doping approach for optimized conductivity in advanced semiconductor ionic fuel cell13A guided review of machine learning in the design and application for pore nanoarchitectonics of carbon materials85Machine learning methods for predicting the durability behavior of earth mortars with date palm ash1Accelerating tribological material discovery: A machine learning framework with predictive insights2027Chapter 2 Machine learning applications in material science: unveiling innovative solutions1Numerical Investigation Using Machine Learning Process Combination of Bio PCM and Solar Salt for Thermal Energy Storage Applications7Transforming Computational Nanotechnology: Accelerating Material Discovery, Design, and Property Prediction through Soft Computing Techniques24AI-driven advances in the design of RTP and TADF luminescent material6Deep learning for simulating the evolution of condensed matter systems at the continuum scale: methods and applications1From molecular nanoarchitectonics to device integration: Coordination chemistry in next-generation photonic, electronic, and mechanical technologies7Waste derived graphene metal oxide composites for advanced supercapacitors: A review on modern energy storage6Machine learning-based prediction of microparticle dynamics in externally driven strongly-coupled dusty plasmas1The Role of AI in Advancing Scientific Research from Hypothesis to Publication1A look at various aspects of using artificial intelligence in technical creativity0EcoCurrentNet an integrated DNN-CatBoost model for predicting optoelectronic material performance under varying conditions1AI-driven design of multifunctional nanomaterials in revolutionizing high-temperature, high-power solutions for space technology: potentials, challenges and perspectives6Tailor-made adsorbent materials on machine learning instructions for efficient purification of heavy metal contaminants in wastewater1Integrating Density Functional Theory with Deep Neural Networks for Accurate Voltage Prediction in Alkali‐Metal‐Ion Battery Materials4Equivariant Graph Neural Networks with Self-Supervised Learning for Accelerated Solid-State Electrolyte Conductivity Prediction0Inspired by natural antioxidants: Design of high-performance phenolic antioxidants based on machine learning and its application in NR3Artificial Intelligence‐Enhanced Developments in Computational Fluid Dynamics0Recent advances in metal-free 2D material/graphitic carbon nitride heterostructures for photocatalytic degradation of organic pollutants2Hydrogen-centric energy storage and conversion systems for sustainable power applications2Exploring multiscale modelling combined with machine learning: from electronic structure to pore-scale dynamics in complex materials1Accelerating Discovery of WX2-Based Gas Sensors (X = S, Se, and Te) for C4F7N Decomposed Species via Density Functional Theory and Machine Learning16A hybrid machine learning approach for high throughput screening of thermodynamically stable double perovskites with optimal band gap0Training-free monitoring of SCF convergence using large language models: a case study of magnetic transition metal oxides with VASP0SurfGen: A generative framework for property-guided surfactant design by integrating inverse design and molecule optimization based models0

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  1. Advances in artificial intelligence for artificial metamaterials

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  2. Machine Learning for Custom-Designed Light Emitters in Biomedicine

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  3. Redefining electrolyte efficiency: bridging the gap with a systematic samarium–copper co-doping approach for optimized conductivity in advanced semiconductor ionic fuel cell

    Materials Futures · 2025-03-05

    Muhammad Shahid Sharif, Zuhra Tayyab, Sajid Rauf, Muhammad Ahsan Masood, M.A.K. Yousaf Shah, Muhammad Tayyab, Abdullah N. Alodhayb, Bin Zhu

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  4. A guided review of machine learning in the design and application for pore nanoarchitectonics of carbon materials

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  5. Machine learning methods for predicting the durability behavior of earth mortars with date palm ash

    Asian Journal of Civil Engineering · 2025-05-23

    Khaled Athmani, Kamal Saleh Almeasar, Elhoussine Atiki, Adel Hassan Yahya Habal, Bachir Taallah, Abdelhamid Guettala

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  6. Accelerating tribological material discovery: A machine learning framework with predictive insights

    Tribology International · 2025-06-05

    Ayna Zahoor, Atikur Rahman, Mohammad Mursaleen

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  7. 27Chapter 2 Machine learning applications in material science: unveiling innovative solutions

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    Leandro Rodrigues Oviedo, Maurício Dalla Costa Rodrigues da Silva, Pâmela Cristine Ladwig Muraro, Raisi Natalia Lenz Baldez, Tiago Moreno Volkmer, William Leonardo da Silva

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  8. Numerical Investigation Using Machine Learning Process Combination of Bio PCM and Solar Salt for Thermal Energy Storage Applications

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    Ravi Kumar Kottala, Sankaraiah Mogaligunta, Makham Satyanarayana Gupta, Seepana Praveenkumar, Ramakrishna Raghutu, Kiran Kumar Patro, Achanta Sampath Dakshina Murthy, Gurram Dharmaiah

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  9. Transforming Computational Nanotechnology: Accelerating Material Discovery, Design, and Property Prediction through Soft Computing Techniques

    ACS Applied Electronic Materials · 2025-06-26

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  10. AI-driven advances in the design of RTP and TADF luminescent material

    Chemical Physics Reviews · 2025-09-01

    Yaru Shi, Yiyang Li, Jihang Zhai, Yueqing Zhang, Baochuan Hu, Yu‐Cheng Gu, Xinmeng Chen, Lianrui Hu, Xiao Dong He

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  11. Deep learning for simulating the evolution of condensed matter systems at the continuum scale: methods and applications

    Journal of Physics Condensed Matter · 2025-09-19

    Daniele Lanzoni, Francesco Montalenti, Roberto Bergamaschini

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  12. From molecular nanoarchitectonics to device integration: Coordination chemistry in next-generation photonic, electronic, and mechanical technologies

    Coordination Chemistry Reviews · 2025-09-23

    Safir Ullah Khan, Munir Ullah Khan, Mohammed Alissa, Abdullah Ahmed Alghamdi, Suad A. Alghamdi, Mohammed A. Alshehri, Ghfren S. Aloraini, Abdullah Albelasi, Mohammed S. Alshammari, Ghada M. Alnafesah

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  13. Waste derived graphene metal oxide composites for advanced supercapacitors: A review on modern energy storage

    Journal of Energy Storage · 2025-09-23

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  14. Machine learning-based prediction of microparticle dynamics in externally driven strongly-coupled dusty plasmas

    Machine Learning Science and Technology · 2025-09-26

    Maria Luiza Mitu, Dorina Ticoş, Nicoleta Udrea, Adrian Scurtu, C. M. Ticoş

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  15. The Role of AI in Advancing Scientific Research from Hypothesis to Publication

    IEEE International Conference on Application of Information and Communication Technologies (AICT) · 2025-10-29

    Ameera F. Mohammad, Waleed Khalil Ahmed, Abdurrahman Ghaleb Almekhlafi, Salem Alzahmi, Ali Hassan Al-Marzouqi, Saed Alrabaee, Essam K. Zaneldin

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  16. A look at various aspects of using artificial intelligence in technical creativity

    Reporter of the Priazovskyi State Technical University Section Technical sciences · 2025-10-30

    L.O. Dan, L.O. Trofimova

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  17. EcoCurrentNet an integrated DNN-CatBoost model for predicting optoelectronic material performance under varying conditions

    Scientific Reports · 2025-11-24

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  18. AI-driven design of multifunctional nanomaterials in revolutionizing high-temperature, high-power solutions for space technology: potentials, challenges and perspectives

    Discover Nano · 2025-12-01

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  19. Tailor-made adsorbent materials on machine learning instructions for efficient purification of heavy metal contaminants in wastewater

    Environmental Functional Materials · 2025-12-09

    Jinwen Zeng, Minjie Qin, Mingzhe Jiang, Shaochi Wang, Xinyue Xie, Lijia Li, Wei Ren, Penghui Shao, Xiao Xiao, Xubiao Luo

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  20. Integrating Density Functional Theory with Deep Neural Networks for Accurate Voltage Prediction in Alkali‐Metal‐Ion Battery Materials

    Small Methods · 2026-01-01

    Sk Mujaffar Hossain, Namitha Anna Koshi, Seung‐Cheol Lee, G.P Das, Satadeep Bhattacharjee

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  21. Equivariant Graph Neural Networks with Self-Supervised Learning for Accelerated Solid-State Electrolyte Conductivity Prediction

    International Conference on Neural Networks, Information and Communication Engineering (NNICE) · 2026-01-23

    Tiantian Gao, Yufeng Wu

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  22. Inspired by natural antioxidants: Design of high-performance phenolic antioxidants based on machine learning and its application in NR

    Chemical Engineering Journal · 2026-01-26

    Xupeng Wang, Xupeng Wang, Wei Gao, Chaokun Huang, Yanlong Luo, Xiujuan Wang, Xiujuan Wang, Yichao Lin

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  23. Artificial Intelligence‐Enhanced Developments in Computational Fluid Dynamics

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    Tushar Sagar, Sachin Kumar, Dinesh Kumar Patel, Gaurav Nandan, Vipin Kumar Sharma

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  24. Recent advances in metal-free 2D material/graphitic carbon nitride heterostructures for photocatalytic degradation of organic pollutants

    Chemical Engineering Journal Green and Sustainable · 2026-03-19

    Tesfaye Abebe Geleta, Prashanth Venkatesan, Kun-Lin Yang, Ashkan Miri, Yang-hsin Shih

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  25. Hydrogen-centric energy storage and conversion systems for sustainable power applications

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  26. Exploring multiscale modelling combined with machine learning: from electronic structure to pore-scale dynamics in complex materials

    The European Physical Journal Special Topics · 2026-06-17

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  27. Accelerating Discovery of WX2-Based Gas Sensors (X = S, Se, and Te) for C4F7N Decomposed Species via Density Functional Theory and Machine Learning

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  28. A hybrid machine learning approach for high throughput screening of thermodynamically stable double perovskites with optimal band gap

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  29. Training-free monitoring of SCF convergence using large language models: a case study of magnetic transition metal oxides with VASP

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  30. SurfGen: A generative framework for property-guided surfactant design by integrating inverse design and molecule optimization based models

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