Opportunities and challenges of text mining in materials research

Kononova, Olga; He, Tanjin; Huo, Haoyan; Trewartha, Amalie; Olivetti, Elsa; Ceder, Gerbrand

doi:10.1016/j.isci.2021.102155

Cited by 105 publications

(104 citation statements)

References 109 publications

(146 reference statements)

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“…To overcome this shortcoming, NLP efforts have been developed and applied to collect and curate synthesis data in relevant literature, such as synthesis conditions and resulting phases 25,98,99,160 . However, because of inherent biases in published results, the lack of negative examples (i.e., failed synthesis attempts) limits the ability to learn which factors contribute to an experiment's success 100,161 . Interestingly, the potential of coupling NLP with autonomous synthesis platforms has been demonstrated by the work of Mehr et al, where a fully integrated system was built to carry out the synthesis of organic compounds with experimental parameters parsed directly from the literature 162 .…”

Section: Decision Makingmentioning

confidence: 99%

Toward autonomous design and synthesis of novel inorganic materials

et al. 2021

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Section: Decision Makingmentioning

confidence: 99%

Toward autonomous design and synthesis of novel inorganic materials

et al. 2021

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“…ML has now started to be gradually and thoroughly applied to materials science and has already brought many promising applications to SSE research. In the case of LIB SSE prediction, we can see that ML algorithms perform well: helping researchers extend datasets by text mining from the literatures [ 36 , 80 ], providing new tools for screening SSE with high mechanical properties or high ionic conductivity. In terms of predicting materials, the reduction of computational cycles is undoubted great importance.…”

Section: Views and Conclusionmentioning

confidence: 99%

Smart Materials Prediction: Applying Machine Learning to Lithium Solid-State Electrolyte

Chen

Liu³

et al. 2022

Materials

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Traditionally, the discovery of new materials has often depended on scholars’ computational and experimental experience. The traditional trial-and-error methods require many resources and computing time. Due to new materials’ properties becoming more complex, it is difficult to predict and identify new materials only by general knowledge and experience. Material prediction tools based on machine learning (ML) have been successfully applied to various materials fields; they are beneficial for modeling and accelerating the prediction process for materials that cannot be accurately predicted. However, the obstacles of disciplinary span led to many scholars in materials not having complete knowledge of data-driven materials science methods. This paper provides an overview of the general process of ML applied to materials prediction and uses solid-state electrolytes (SSE) as an example. Recent approaches and specific applications to ML in the materials field and the requirements for building ML models for predicting lithium SSE are reviewed. Finally, some current obstacles to applying ML in materials prediction and prospects are described with the expectation that more materials scholars will be aware of the application of ML in materials prediction.

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“…Another emerging application of ML that is tangentially related to SSB research is the text-mining of scientific publications (Kononova et al, 2021). Olivetti and coworkers demonstrated that text-mining can be used for extracting synthesis prescriptions from the literature and that this approach can be applied to the prediction of synthesis conditions for solid electrolytes (Mahbub et al, 2020;Olivetti et al, 2020).…”

Section: Figure | (A)mentioning

confidence: 99%

Accelerated Atomistic Modeling of Solid-State Battery Materials With Machine Learning

et al. 2021

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Materials for solid-state batteries often exhibit complex chemical compositions, defects, and disorder, making both experimental characterization and direct modeling with first principles methods challenging. Machine learning (ML) has proven versatile for accelerating or circumventing first-principles calculations, thereby facilitating the modeling of materials properties that are otherwise hard to access. ML potentials trained on accurate first principles data enable computationally efficient linear-scaling atomistic simulations with an accuracy close to the reference method. ML-based property-prediction and inverse design techniques are powerful for the computational search for new materials. Here, we give an overview of recent methodological advancements of ML techniques for atomic-scale modeling and materials design. We review applications to materials for solid-state batteries, including electrodes, solid electrolytes, coatings, and the complex interfaces involved.

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Opportunities and challenges of text mining in materials research

Cited by 105 publications

References 109 publications

Toward autonomous design and synthesis of novel inorganic materials

Toward autonomous design and synthesis of novel inorganic materials

Smart Materials Prediction: Applying Machine Learning to Lithium Solid-State Electrolyte

Accelerated Atomistic Modeling of Solid-State Battery Materials With Machine Learning

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