Fundamental differences between materials originate from the unique nature of their constituent chemical elements. Before specific differences emerge according to the precise ratios of elements in a given crystal structure, a material can be represented by the set of its constituent chemical elements. By working at the level of the periodic table, assessment of materials at the level of their phase fields reduces the combinatorial complexity to accelerate screening, and circumvents the challenges associated with composition-level approaches such as poor extrapolation within phase fields, and the impossibility of exhaustive sampling. This early stage discrimination combined with evaluation of novelty of phase fields aligns with the outstanding experimental challenge of identifying new areas of chemistry to investigate, by prioritising which elements to combine in a reaction. Here, we demonstrate that phase fields can be assessed with respect to the maximum expected value of a target functional property and ranked according to chemical novelty. We develop and present PhaseSelect, an end-to-end machine learning model that combines the representation, classification, regression and ranking of phase fields. First, PhaseSelect constructs elemental characteristics from the co-occurrence of chemical elements in computationally and experimentally reported materials, then it employs attention mechanisms to learn representation for phase fields and assess their functional performance. At the level of the periodic table, PhaseSelect quantifies the probability of observing a functional property, estimates its value within a phase field and also ranks a phase field novelty, which we demonstrate with significant accuracy for three avenues of materials applications for high-temperature superconductivity, high-temperature magnetism, and targeted bandgap energy.

翻译：材料之间的根本差异源于其组成化学元素的独特性质。在给定晶体结构中元素精确比例产生特定差异之前，材料可通过其组成化学元素集合来表征。通过在元素周期表层面开展研究，从相图层面评估材料可降低组合复杂性以加速筛选，并规避了组分层面方法的相关挑战，例如相图内插值能力差以及无法实现穷举采样的问题。这种早期判别结合相图新颖性评估，与通过优先选择反应中需组合的元素来识别新化学领域的实验挑战相契合。在此，我们证明了相图可针对目标功能特性的最大期望值进行评估，并根据化学新颖性进行排序。我们开发并提出了PhaseSelect——一种端到端机器学习模型，该模型整合了相图的表征、分类、回归与排序功能。首先，PhaseSelect通过化学元素在计算与实验报道材料中的共现关系构建元素特征，随后采用注意力机制学习相图的表征并评估其功能性能。在元素周期表层面，PhaseSelect可量化观察到功能特性的概率，估计其在相图中的数值，并对相图新颖性进行排序——我们通过高温超导、高温磁性和目标带隙能这三个材料应用方向展示了该方法的显著准确性。