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RESEARCH PAPER · Wiley Interdisciplinary Reviews Computational Molecular Science · 2024

Computational design of energy‐related materials: From first‐principles calculations to machine learning

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9 direct citing papers in this saved network. Explore the next layer with second-order citations.

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Computational design of energy‐related materials: From first‐principles calculations to machine learningOriginalScaling Discovery Infrastructures: Computational Resource Allocation in Materials Engineering and Computational Data0A brief overview of deep generative models and how they can be used to discover new electrode materials8Two-dimensional Ag2MP2S6 (M=Cd, Zn) chalcogenides: Structural, electro-optical features, and photocatalytic potential6Elemental augmentation of machine learning interatomic potentials3Three decades of quantum science: how quantum chemistry transformed thermochemical database generation for benchmarking DFT and machine learning9De Novo Inverse Design Superhard C–N Compounds via Global Machine Learning Interatomic Potentials and Multiobjective Optimization Algorithm2Unveiling the Structural Stability and Optoelectronic Properties of Pristine and Janus Monolayers8Generative Models in Inorganic Crystals Discovery and Inverse Design0Data-Driven Discovery of Mg–Sr–X Ternary Anodes for High-Performance Mg–Air Batteries0

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  1. Scaling Discovery Infrastructures: Computational Resource Allocation in Materials Engineering and Computational Data

    Journal of Computational and Data-Driven Materials Engineering · 2024-01-01

    Carlos Vega, Maria Hernandez

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  2. A brief overview of deep generative models and how they can be used to discover new electrode materials

    Current Opinion in Electrochemistry · 2024-12-06

    Anders Hellman

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  3. Two-dimensional Ag2MP2S6 (M=Cd, Zn) chalcogenides: Structural, electro-optical features, and photocatalytic potential

    Journal of Alloys and Compounds · 2025-01-10

    Fatemeh Zamanian, Zabiollah Mahdavifar, Georg Schreckenbach

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  4. Elemental augmentation of machine learning interatomic potentials

    Computational Materials Today · 2025-02-04

    Haibo Xue, Guanjian Cheng, Wan‐Jian Yin

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  5. Three decades of quantum science: how quantum chemistry transformed thermochemical database generation for benchmarking DFT and machine learning

    Australian Journal of Chemistry · 2025-03-16

    Amir Karton

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  6. De Novo Inverse Design Superhard C–N Compounds via Global Machine Learning Interatomic Potentials and Multiobjective Optimization Algorithm

    The Journal of Physical Chemistry Letters · 2025-04-24

    Guanjian Cheng, Wan‐Jian Yin

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  7. Unveiling the Structural Stability and Optoelectronic Properties of Pristine and Janus Monolayers

    ACS Omega · 2025-05-08

    Abdur Rahman, Ghulam Hussain, Ashfaq Ahmad, Kashif Hussain, Zhengbiao Ouyang

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  8. Generative Models in Inorganic Crystals Discovery and Inverse Design

    Energy & environment materials · 2026-08-03

    Tao Li, Xiaolin Liu, Fei Wang, Yan Xiong, Yu Chen, Yan Zhao

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  9. Data-Driven Discovery of Mg–Sr–X Ternary Anodes for High-Performance Mg–Air Batteries

    Industrial & Engineering Chemistry Research · 2026-08-12

    Yingying Wang, Rujuan Xu, Shanshan Song, Xinke Qi, Li Wang

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