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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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The Materials Science behind Sustainable Metals and Alloys
Chemical Reviews, 2023 Production of metals stands for 40% of all industrial greenhouse gas emissions, 10% of the global energy consumption, 3.2 billion tonnes of minerals mined, and several billion tonnes of by-products every year.
D. Raabe
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Benchmarking the performance of Bayesian optimization across multiple experimental materials science domains [PDF]
npj Computational Materials, 2021 Bayesian optimization (BO) has been leveraged for guiding autonomous and high-throughput experiments in materials science. However, few have evaluated the efficiency of BO across a broad range of experimental materials domains.
Qiaohao Liang +14 more
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First principles phonon calculations in materials science [PDF]
, 2015 Phonon plays essential roles in dynamical behaviors and thermal properties, which are central topics in fundamental issues of materials science. The importance of first principles phonon calculations cannot be overly emphasized. Phonopy is an open source
A. Togo, I. Tanaka
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Explainable machine learning in materials science
npj Computational Materials, 2022 Machine learning models are increasingly used in materials studies because of their exceptional accuracy. However, the most accurate machine learning models are usually difficult to explain.
Xiaoting Zhong +5 more
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Data quantity governance for machine learning in materials science
National Science Review, 2023 Data-driven machine learning (ML) is widely employed in the analysis of materials structure–activity relationships, performance optimization and materials design due to its superior ability to reveal latent data patterns and make accurate prediction ...
Yue Liu +6 more
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IOP Conference Series: Materials Science and Engineering
IOP Conference Series: Materials Science and Engineering, 2022 International Nuclear Science, Technology and Engineering Conference 2021 (iNuSTEC2021) “Nuclear Science and Technology for Socio-economic Development” Universiti Teknologi Malaysia (UTM) Skudai, Johor, Malaysia 10-12 October 2021 Editors Nahrul Khair ...
Dr. Muji Setiyo +190 more
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Patterns, 2022 Summary A bottleneck in efficiently connecting new materials discoveries to established literature has arisen due to an increase in publications. This problem may be addressed by using named entity recognition (NER) to extract structured summary-level ...
Amalie Trewartha +9 more
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Quantum Algorithms for Quantum Chemistry and Quantum Materials Science. [PDF]
Chemical Reviews, 2020 As we begin to reach the limits of classical computing, quantum computing has emerged as a technology that has captured the imagination of the scientific world.
B. Bauer +3 more
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Machine learning in materials science: From explainable predictions to autonomous design
Computational materials science, 2021 The advent of big data and algorithmic developments in the field of machine learning (and artificial intelligence, in general) have greatly impacted the entire spectrum of physical sciences, including materials science.
G. Pilania
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