<ul><li>Simulation-to-Real (Sim2Real) transfer learning is gaining attention in materials science as a solution to the scarcity of experimental data.</li><li>A transfer learning scheme from first-principles calculations to experiments based on chemistry-informed domain transformation is proposed.</li><li>The proposed method efficiently bridges the simulation space (source domain) and the experimental data space (target domain) using prior knowledge of chemistry and the relationship between source and target quantities.</li><li>The transfer learning model exhibits high accuracy and data efficiency, even with a small number of target data, helping to save the number of trials in real laboratories.</li></ul>

Transfer learning from first-principles calculations to experiments with chemistry-informed domain transformation

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