We extracted all materials science related concepts from academic literature to create a "Map of Materials Science". Stay tuned for more..!
See the 'Materials Science Map'
We are collecting experimental powder X-ray diffraction data to create a free high-quality dataset with a broad range of samples and materials. Please visit our website in order to contribute your data and to learn more about becoming a co-author in the associated paper.
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MeganExplains features self-explaining graph neural network models that can predict various molecular properties in a transparent fashion. Besides the pure predictions, xAI practices such as saliency maps and counterfactuals are used to provide insight to the model's internal reasoning process
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How toxic is your favourite food additive? Don't know? How about we find out! We're taking you on a journey through molecular science and AI, using innovative, new technology to find answers to the real questions, like "Is water a metal?" or "How flammable is my neighbour?"
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A tool to predict the synthesis parameters of new MOFs with promising predicted properties. Our ML model is trained on a new MOF synthesis database based on automatic extraction of synthesis parameters from the literature. Even at an initial stage, it exhibits a good prediction performance, outperforming human expert predictions.
Predict MOF synthesis conditions here