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Science, 1980 The effects of accelerated aging on a pigmented elastomer were evaluated by using a weathering chamber. Silastic 44210, a maxillofacial material with proven color and physical property stability, was chosen for pigmentation with 11 maxillofacial pigments.
R. Yu, A. Koran, R.G. Craig
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Self-Driving Laboratories for Chemistry and Materials Science [PDF]
Chem RevSelf-driving laboratories (SDLs) promise an accelerated application of the scientific method. Through the automation of experimental workflows, along with autonomous experimental planning, SDLs hold the potential to greatly accelerate research in ...
Gary Tom +15 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
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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 +51 more
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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 +126 more
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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
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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
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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.
K. Choudhary +12 more
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Deep Potentials for Materials Science [PDF]
Materials Futures, 2022 To fill the gap between accurate (and expensive) ab initio calculations and efficient atomistic simulations based on empirical interatomic potentials, a new class of descriptions of atomic interactions has emerged and been widely applied; i.e., machine
T. Wen +4 more
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Machine-Learning Interatomic Potentials for Materials Science [PDF]
Social Science Research Network, 2021 Large-scale atomistic computer simulations of materials rely on interatomic potentials providing computationally efficient predictions of energy and Newtonian forces. Traditional potentials have served in this capacity for over three decades. Recently, a
Y. Mishin
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