MDPF Case Studies

1. Efficient data-driven exploration of new phosphors.
2. Accurate prediction of emission peak wavelength using machine learning.
3. Selection of new host candidates using AtomWork-Adv.

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1. Exploration of polymer materials that achieve both heat-resistance and transparency using PoLyInfo.
2. "Exception search" method selectively extracting polymers with known structures but unknown properties.
3. Rapid materials development at the laboratory level utilizing scientific knowledge, computational chemistry, and reported experimental information.

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1. Existing polymer degradability datasets are not always sufficient, and differences in measurement environments make direct comparison difficult.
2. Prepared three types of datasets that cannot be directly compared in terms of degradability data, integrated the datasets using a ranking-based machine learning method, and constructed a degradability prediction model.
3. Applied the constructed model to PoLyInfo data, effectively identifying polymers with high and low degradability.

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