FAIR data enabling new horizons for materials research

Scheffler, Matthias; Aeschlimann, Martin; Albrecht, M.; Bereau, Tristan; Bungartz, Hans‐Joachim; Felser, Claudia; Greiner, Mark; Groß, Axel; Koch, Christoph T.; Kremer, Kurt; Nagel, Wolfgang E.; Scheidgen, Markus; Wöll, Christof; Draxl, Claudia

doi:10.1038/s41586-022-04501-x

Cited by 120 publications

(78 citation statements)

References 28 publications

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“…We anticipate that continuing advances in computational resources, high-throughput methods, automated workflows and infrastructure, − as well as ML approaches may prove useful for surmounting many of these practical difficulties. In particular, bottom-up methods will greatly benefit from standardized repositories that provide access to large scale AA simulation data , and, thus, eliminate a key activation barrier in developing and testing bottom-up methods for complex systems. However, new fundamental challenges will also arise because the intuition and approximations that apply well for simple systems may not necessarily transfer to the more complex systems that are of interest.…”

Section: Conclusion: Retro- and Prospectivesmentioning

confidence: 99%

Perspective: Advances, Challenges, and Insight for Predictive Coarse-Grained Models

Noid

2023

J. Phys. Chem. B

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By averaging over atomic details, coarse-grained (CG) models provide profound computational and conceptual advantages for studying soft materials. In particular, bottom-up approaches develop CG models based upon information obtained from atomically detailed models. At least in principle, a bottom-up model can reproduce all the properties of an atomically detailed model that are observable at the resolution of the CG model. Historically, bottom-up approaches have accurately modeled the structure of liquids, polymers, and other amorphous soft materials, but have provided lower structural fidelity for more complex biomolecular systems. Moreover, they have also been plagued by unpredictable transferability and a poor description of thermodynamic properties. Fortunately, recent studies have reported dramatic advances in addressing these prior limitations. This Perspective reviews this remarkable progress, while focusing on its foundation in the basic theory of coarse-graining. In particular, we describe recent insights and advances for treating the CG mapping, for modeling many-body interactions, for addressing the state-point dependence of effective potentials, and even for reproducing atomic observables that are beyond the resolution of the CG model. We also outline outstanding challenges and promising directions in the field. We anticipate that the synthesis of rigorous theory and modern computational tools will result in practical bottom-up methods that not only are accurate and transferable but also provide predictive insight for complex systems.

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Section: Conclusion: Retro- and Prospectivesmentioning

confidence: 99%

Perspective: Advances, Challenges, and Insight for Predictive Coarse-Grained Models

Noid

2023

J. Phys. Chem. B

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“…This project has also considered the needs of experimental and laboratory data management infrastructure to feed these community repositories. 73 The development of knowledge graphs to store and structure experimental data is a promising approach to implanting data sharing infrastructure. Knowledge graphs structure information as a connected graph, with data points and entities represented as graph nodes and properties or relationships represented as edges.…”

Section: Community Scale Data Managementmentioning

confidence: 99%

The laboratory of Babel: highlighting community needs for integrated materials data management

Pelkie

Pozzo

2023

Digital Discovery

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“…The large amount of stored data, in the long-term, enables datacentric analyses, including techniques such as clustering, machine learning, and symbolic regression [57]. All these techniques need data to uncover patterns, make predictions, or build new physical models [43].…”

Section: Crackpymentioning

confidence: 99%

“…In addition, it is important to ensure that the generated data meets the criteria of being Findable, Accessible, Interoperable, and Reusable (F.A.I.R.) so that it can be utilized for data-driven research in a sustainable manner [43].…”

Section: Introductionmentioning

confidence: 99%

Towards self-driving labs for experimental mechanics: a data-centric approach for fatigue crack analysis in aerospace materials

Strohmann

Melching

Paysan

et al. 2023

Preprint

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Today’s societal challenges require rapid response and smart materials solutions in almost all technical areas. Driven by these needs, data-driven research has emerged as an enabler for faster innovation cycles. In fields such as chemistry, materials science and life sciences, autonomous data generation and processing is already accelerating knowledge discovery. In contrast, in experimental mechanics, complex investigations like studying fatigue crack growth in structural materials have traditionally adhered to standardized procedures with limited adoption of the digital transformation. In this work, we present a novel infrastructure for data-centric experimental mechanics. The setup is demonstrated using a complex fatigue crack growth experiment for aerospace materials. Our methodology incorporates an open-source Python library that complements a multi-scale digital image correlation and robot-assisted test rig. Our novel approach significantly increases the information-to-cost ratio of fatigue crack growth experiments in aerospace materials compared to traditional experiments. Thus, serves as a catalyst for discovering new scientific knowledge and contributes to the data-driven acceleration of the deployment of new applications in the field of structural materials and structures.

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FAIR data enabling new horizons for materials research

Cited by 120 publications

References 28 publications

Perspective: Advances, Challenges, and Insight for Predictive Coarse-Grained Models

Perspective: Advances, Challenges, and Insight for Predictive Coarse-Grained Models

The laboratory of Babel: highlighting community needs for integrated materials data management

Towards self-driving labs for experimental mechanics: a data-centric approach for fatigue crack analysis in aerospace materials

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