Bayesian optimization (BO) can accelerate material design requiring timeconsuming experiments. However, although most material designs require tuning of multiple properties, the efficiency of multi-objective (MO) BO in timeconsuming experimental material design remains unclear, due to the complexity of handling multiple objectives. This study introduces MO BO method that efficiently achieves predefined goals and shows that by focusing on achieving the goals, BO can efficiently accelerate realistic MO design problems with small efforts. Benchmarks showed that the proposed BO method dramatically reduced the number of experiments needed to achieve goals relative to a baseline method. Virtual MO inverse design experiments with realistic material design problems were also performed, during which the proposed method could achieve goals within only around ten experiments in average and showed over 1000-fold acceleration relative to the random sampling for the most difficult case. The introduction of goal-oriented BO will precede real-world application of BO.
Deep
neural networks (DNNs) represent promising approaches to molecular
machine learning (ML). However, their applicability remains limited
to single-component materials and a general DNN model capable of handling
various multicomponent molecular systems with composition data is
still elusive, while current ML approaches for multicomponent molecular
systems are still molecular descriptor-based. Here, a general DNN
architecture extending existing molecular DNN models to multicomponent
systems called MEIA is proposed. Case studies showed that the MEIA
architecture could extend two exiting molecular DNN models to multicomponent
systems with the same procedure, and that the obtained models that
could learn both the molecular structure and composition information
with equal or better accuracies compared to a well-used molecular
descriptor-based model in the best model for each case study. Furthermore,
the case studies also showed that, for ML tasks where the molecular
structure information plays a minor role, the performance improvements
by DNN models were small; while for ML tasks where the molecular structure
information plays a major role, the performance improvements by DNN
models were large, and DNN models showed notable predictive accuracies
for an extremely sparse dataset, which cannot be modeled without the
molecular structure information. The enhanced predictive ability of
DNN models for sparse datasets of multicomponent systems will extend
the applicability of ML in the multicomponent material design. Furthermore,
the general capability of MEIA to extend DNN models to multicomponent
systems will provide new opportunities to utilize the progress of
actively developed single-component DNNs for the modeling of multicomponent
systems.
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