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Explainable machine learning in materials science

npj Computational Materials 2022年第1期8卷 1951-1969页

作者： Xiaoting Zhong Brian Gallagher Shusen Liu bhavya kailkhura Anna Hiszpanski T.Yong-Jin Han Materials Science Division Lawrence Livermore National LaboratoryLivermoreCAUSA Center for Applied Scientific Computing Lawrence Livermore National LaboratoryLivermoreCAUSA

Machine learning models are increasingly used in materials studies because of their exceptional ***,the most accurate machine learning models are usually difficult to *** to this problem lie in explainable artificial intelligence(XAI),an emerging research field that addresses the explainability of complicated machine learning models like deep neural networks(DNNs).This article attempts to provide an entry point to XAI for materials *** are defined to clarify what explain means in the context of materials *** works are reviewed to show how XAI helps materials science *** and opportunities are also discussed.

关键词： explain learning plain

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Reliable and explainable machine-learning methods for accelerated material discovery

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npj Computational Materials 2019年第1期5卷 186-194页

作者： bhavya kailkhura Brian Gallagher Sookyung Kim Anna Hiszpanski T.Yong-Jin Han Center for Applied Scientific Computing Computing DirectorateLawrence Livermore National LaboratoryLivermoreCAUSA Materials Science Division Physical and Life Sciences DirectorateLawrence Livermore National LaboratoryLivermoreCAUSA

Despite ML’s impressive performance in commercial applications,several unique challenges exist when applying ML in materials science *** such a context,the contributions of this work are ***,we identify common pitfalls of existing ML techniques when learning from underrepresented/imbalanced material ***,we show that with imbalanced data,standard methods for assessing quality of ML models break down and lead to misleading ***,we find that the model’s own confidence score cannot be trusted and model introspection methods(using simpler models)do not help as they result in loss of predictive performance(reliability-explainability trade-off).Second,to overcome these challenges,we propose a general-purpose explainable and reliable machine-learning ***,we propose a generic pipeline that employs an ensemble of simpler models to reliably predict material *** also propose a transfer learning technique and show that the performance loss due to models’simplicity can be overcome by exploiting correlations among different material properties.A new evaluation metric and a trust score to better quantify the confidence in the predictions are also *** improve the interpretability,we add a rationale generator component to our framework which provides both model-level and decision-level ***,we demonstrate the versatility of our technique on two applications:(1)predicting properties of crystalline compounds and(2)identifying potentially stable solar cell *** also point to some outstanding issues yet to be resolved for a successful application of ML in material science.

关键词： explain overcome simpler

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