Artificial Intelligence-Powered Materials Science [PDF]
Nano-Micro LettersA detailed exploration is provided of how artificial intelligence (AI) and machine learning techniques are applied across various aspects of materials science. Major challenges in AI-driven materials science are evaluated.
Xingcai Zhang, Xiaopeng Bai
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Opportunities and Challenges for Machine Learning in Materials Science [PDF]
Annual Review of Materials Research, 2020 Advances in machine learning have impacted myriad areas of materials science, such as the discovery of novel materials and the improvement of molecular simulations, with likely many more important developments to come.
Dane Morgan, Ryan Jacobs
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Machine learning for molecular and materials science
Nature, 2018 Keith T. Butler +2 more
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Narrow-gap Semiconducting Superhard Amorphous Carbon with Superior Toughness [PDF]
, 2021 1Center for High Pressure Science (CHiPS), State Key Laboratory of Metastable Materials Science and Technology, Yanshan University, Qinhuangdao, Hebei 066004, China 2Hebei Key Laboratory of Microstructural Material Physics, School of Science, Yanshan ...
Shuangshuang Zhang +48 more
semanticscholar +1 more source
14 examples of how LLMs can transform materials science and chemistry: a reflection on a large language model hackathon [PDF]
Digital Discovery, 2023 Large-language models (LLMs) such as GPT-4 caught the interest of many scientists. Recent studies suggested that these models could be useful in chemistry and materials science. To explore these possibilities, we organized a hackathon.
K. Jablonka +52 more
semanticscholar +1 more source
Graph neural networks for materials science and chemistry [PDF]
Communications Materials, 2022 Machine learning plays an increasingly important role in many areas of chemistry and materials science, being used to predict materials properties, accelerate simulations, design new structures, and predict synthesis routes of new materials. Graph neural
Patrick Reiser +10 more
semanticscholar +1 more source
Quantum-centric supercomputing for materials science: A perspective on challenges and future directions [PDF]
Future generations computer systems, 2023 Computational models are an essential tool for the design, characterization, and discovery of novel materials. Hard computational tasks in materials science stretch the limits of existing high-performance supercomputing centers, consuming much of their ...
Yuri Alexeev +127 more
semanticscholar +1 more source
Advances of machine learning in materials science: Ideas and techniques [PDF]
Frontiers of Physics, 2023 In this big data era, the use of large dataset in conjunction with machine learning (ML) has been increasingly popular in both industry and academia. In recent times, the field of materials science is also undergoing a big data revolution, with large ...
S. Chong +3 more
semanticscholar +1 more source
Recent advances and applications of deep learning methods in materials science [PDF]
npj Computational Materials, 2021 Deep learning (DL) is one of the fastest-growing topics in materials data science, with rapidly emerging applications spanning atomistic, image-based, spectral, and textual data modalities.
Kamal Choudhary +12 more
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Interpretable and Explainable Machine Learning for Materials Science and Chemistry [PDF]
Accounts of Materials Research, 2021 While the uptake of data-driven approaches for materials science and chemistry is at an exciting, early stage, to realise the true potential of machine learning models for successful scientific discovery, they must have qualities beyond purely predictive
Felipe Oviedo +3 more
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