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Computationally accelerated experimental materials characterization-drawing inspiration from high-throughput simulation workflows 认领 引用
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作者 Markus Stricker Lars Banko +5 位作者 Nik Sarazin Niklas Siemer Jan Janssen Lei Zhang Jörg Neugebauer Alfred Ludwig 《npj Computational Materials》 SCIE EI CSCD 2025年第1期4590-4598,共9页
Computational materials science increasingly benefits from data management,automation,and algorithm-based decision-making for the simulation of material properties and behavior.Experimental materials science also chan... Computational materials science increasingly benefits from data management,automation,and algorithm-based decision-making for the simulation of material properties and behavior.Experimental materials science also changes rapidly by incorporation of‘machine learning’in materials discovery campaigns.The benefits including automation,reproducibility,data provenance,and reusability of managed data,however,are not widely available in the experimental domain.We present an implementation of an Active Learning loop with an interface to an experimental measurement device in pyiron as a demonstrator how to combine experimental and simulated data in one framework.Apart from the acceleration provided through active learning,additional acceleration of the experimental characterization is achieved by using prior knowledge from density functional theory simulations as well as predictions based on text mining using correlations in word embeddings.With data from all domains in the same framework,an untapped potential for the acceleration of materials characterization and materials discovery campaigns becomes available. 展开更多
关键词 active learning loop w data management automation computational materials science data managementautomationand materials science high throughput simulation simulation material properties
An extensible open-source solution for research digitalisation in materials science 认领 引用 被引量：1
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作者 Victor Dudarev Lars Banko Alfred Ludwig 《npj Computational Materials》 SCIE EI CSCD 2025年第1期1272-1278,共7页
Information technology and data science development stimulate transformation in many fields of scientific knowledge.In recent years,a large number of specialised systems for information and knowledge management have b... Information technology and data science development stimulate transformation in many fields of scientific knowledge.In recent years,a large number of specialised systems for information and knowledge management have been created in materials science.However,the development and deployment of open adaptive systems for research support in materials science based on the acquisition,storage,and processing of different types of information remains unsolved.We propose MatInf-an extensible,open-source solution for research digitalisation in materials science based on an adaptive,flexible information management systemfor heterogeneous data sources.MatInf can be easily adapted to any materials science laboratory and is especially useful for collaborative projects between several labs.As an example,we demonstrate its application in high-throughput experimentation. 展开更多
关键词 data science development information technology research digitalization materials science data science development deployment open adaptive systems knowledge management
Deep learning for visualization and novelty detection in large X-ray diffraction datasets 认领 引用 被引量：6
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作者 Lars Banko Phillip M.Maffettone +2 位作者 Dennis Naujoks Daniel Olds Alfred Ludwig 《npj Computational Materials》 SCIE EI CSCD 2021年第1期942-947,共6页
We apply variational autoencoders(VAE)to X-ray diffraction(XRD)data analysis on both simulated and experimental thin-film data.We show that crystal structure representations learned by a VAE reveal latent information,... We apply variational autoencoders(VAE)to X-ray diffraction(XRD)data analysis on both simulated and experimental thin-film data.We show that crystal structure representations learned by a VAE reveal latent information,such as the structural similarity of textured diffraction patterns.While other artificial intelligence(AI)agents are effective at classifying XRD data into known phases,a similarly conditioned VAE is uniquely effective at knowing what it doesn’t know:it can rapidly identify data outside the distribution it was trained on,such as novel phases and mixtures.These capabilities demonstrate that a VAE is a valuable AI agent for aiding materials discovery and understanding XRD measurements both‘on-the-fly’and during post hoc analysis. 展开更多
关键词 VAE uniquely latent
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