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机器学习预测材料性质: 难点与改进策略 | 龚盛 麻省理工学院 | 青年科学半月谈
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Despite the widespread applications of machine learning models in materials science, in many cases the performance of machine learning models is not sufficiently accurate enough to meet the needs of materials design. In this presentation, we propose and apply a series of strategies to exam and improve upon the performance of machine learning models for specific materials problems. First, we exam whether current deep representation learning models for atomistic systems can capture all human knowledge of crystal structures, and find that current graph neural networks can capture knowledge of local atomic environments but cannot capture periodicity of crystalline materials. As an initial solution, we hybridize human knowledge with deep representation learning models, and find that the hybridization can lead to improvement for predicting materials properties, especially vibrational properties. Then, for situations where the datasets of target materials properties are small while there are large relevant materials datasets, we propose to use transfer learning and multi-fidelity learning to transfer information between the large and small datasets to facilitate the learning of target properties. We use experimentally measured formation enthalpy and lattice thermal conductivity as case studies to exam the usefulness of information transfer and understand where and why information transfer helps. The machine learning models introduced in this presentation not only deepen human understanding of where and how machine learning can be used to facilitate materials development, but also lead to the discovery of new materials systems and new insights, such as new candidate thermoelectric materials and new insights into the evaluation of the stability of materials.
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