  Applied machine learning (ML) has rapidly spread throughout the physical
sciences; in fact, ML-based data analysis and experimental decision-making has
become commonplace. We suggest a shift in the conversation from proving that ML
can be used to evaluating how to equitably and effectively implement ML for
science.We advocate a shift from a "more data, more compute" mentality to a
model-oriented approach that prioritizes using machine learning to support the
ecosystem of computational models and experimental measurements.We also
recommend an open conversation about dataset bias to stabilize productive
research through careful model interrogation and deliberate exploitation of
known biases. Further, we encourage the community to develop ML methods that
connect experiments with theoretical models to increase scientific
understanding rather than incrementally optimizing materials. Moreover we
envision a future of radical materials innovations enabled by computational
creativity tools combined with online visualization and analysis tools that
support active outside-the-box thinking inside the scientific knowledge
feedback loop. Finally, as a community we must acknowledge ethical issues that
can arise from blindly following machine learning predictions and the issues of
social equity that will arise if data, code, and computational resources are
not readily available to all.
