Materials Informatics Reveals Unexplored Structure Space in Cuprate Superconductors

Goodall, Rhys E. A.; Zhu, Bonan; MacManus‐Driscoll, Judith L.; Lee, Alpha A.

doi:10.1002/adfm.202104696

Cited by 6 publications

(5 citation statements)

References 43 publications

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“…This, coupled with the solid-state literature and nascent Inorganic Crystal Structure Database, resulted in a host of experiments targeted at potential novel materials that were both feasible and unreported. More recently, ML-based approaches have been applied to better explore the space of cuprate-like compounds. , However, even simple database queries and linear regressions (combined with DFT estimates of stability) suffice to identify potential compounds that fill-in the gaps in the distribution of observed apical and in-plane Cu–O distance distributions for this class of compounds …”

Section: Recommendations Toward ML For Exceptional Materialsmentioning

confidence: 99%

“…186,187 However, even simple database queries and linear regressions (combined with DFT estimates of stability) suffice to identify potential compounds that fill-in the gaps in the distribution of observed apical and inplane Cu−O distance distributions for this class of compounds. 188 Alternatively, one can focus on previously unobserved outcomes. The information entropy of the observed property distribution can be useful for identifying outcome imbalances, and active learning used to prioritize new samples to correct these imbalances, recently demonstrated in the context of formation energy/structure biases of intermetallic compounds.…”

Section: Vc Sample What Can Be Made and How To Make It � Defer Optimi...mentioning

confidence: 99%

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In Pursuit of the Exceptional: Research Directions for Machine Learning in Chemical and Materials Science

Schrier,

Norquist,

Buonassisi

et al. 2023

J. Am. Chem. Soc.

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Exceptional molecules and materials with one or more extraordinary properties are both technologically valuable and fundamentally interesting, because they often involve new physical phenomena or new compositions that defy expectations. Historically, exceptionality has been achieved through serendipity, but recently, machine learning (ML) and automated experimentation have been widely proposed to accelerate target identification and synthesis planning. In this Perspective, we argue that the data-driven methods commonly used today are well-suited for optimization but not for the realization of new exceptional materials or molecules. Finding such outliers should be possible using ML, but only by shifting away from using traditional ML approaches that tweak the composition, crystal structure, or reaction pathway. We highlight case studies of high-T c oxide superconductors and superhard materials to demonstrate the challenges of ML-guided discovery and discuss the limitations of automation for this task. We then provide six recommendations for the development of ML methods capable of exceptional materials discovery: (i) Avoid the tyranny of the middle and focus on extrema; (ii) When data are limited, qualitative predictions that provide direction are more valuable than interpolative accuracy; (iii) Sample what can be made and how to make it and defer optimization; (iv) Create room (and look) for the unexpected while pursuing your goal; (v) Try to fill-in-the-blanks of input and output space; (vi) Do not confuse human understanding with model interpretability. We conclude with a description of how these recommendations can be integrated into automated discovery workflows, which should enable the discovery of exceptional molecules and materials.

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Section: Recommendations Toward ML For Exceptional Materialsmentioning

confidence: 99%

Section: Vc Sample What Can Be Made and How To Make It � Defer Optimi...mentioning

confidence: 99%

In Pursuit of the Exceptional: Research Directions for Machine Learning in Chemical and Materials Science

Schrier,

Norquist,

Buonassisi

et al. 2023

J. Am. Chem. Soc.

View full text Add to dashboard Cite

show abstract

“…License: CC BY-NC-ND 4.0 identify potential compounds that fill-in the gaps in the distribution of observed apical and in-plane Cu-O distance distributions for this class of compounds. 188 Alternatively, one can focus on previously unobserved outcomes. The information entropy of the observed property distribution can be useful for identifying outcome imbalances, and active learning used to prioritize new samples to correct these imbalances, recently demonstrated in the context of formation energy/structure biases of intermetallic compounds.…”

Section: Try To Fill-in-the-blanks Of Input and Output Spacementioning

confidence: 99%

In Pursuit of the Exceptional: Research Directions for Machine Learning in Chemical and Materials Science

Schrier,

Norquist,

Buonassisi

et al. 2023

Preprint

View full text Add to dashboard Cite

Exceptional molecules and materials with one or more extraordinary properties are both technologically valuable and fundamentally interesting because they often involve new physical phenomena or new compositions that defy expectations. Historically, exceptionality has been achieved through serendipity, but recently, machine learning (ML) and automated experimentation have been widely proposed to accelerate target identification and synthesis planning. In this Perspective, we argue that the data-driven methods commonly used today are well-suited for optimization but not for realizing new exceptional materials or molecules. Finding such outliers should be possible using ML, but only by shifting away from using traditional ML approaches that tweak the composition, crystal structure, or reaction pathway. We highlight case studies of high-Tc oxide superconductors and superhard materials to demonstrate the challenges of ML-guided discovery and discuss the limitations of automation for this task. We then provide six recommendations for the development of ML methods capable of exceptional materials discovery: (i) Avoid the tyranny of the middle and focus on extrema; (ii) When data is limited, qualitative predictions that provide direction are more valuable than interpolative accuracy; (iii) Sample what can be made and how to make it, and defer optimization; (iv) Create room (and look) for the unexpected while pursuing your goal; (v) Try to fill-in-the-blanks of input and output space; (vi) Do not confuse human understanding with model interpretability. We conclude with a description of how these recommendations can be integrated into automated discovery workflows that should enable the discovery of exceptional molecules and materials.

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“…Materials informatics has shown that the cuprate class of superconductors contains a highly unexplored materials space that has yet to be explored [18]. This is why formulae from the SuperCon database are used for the training of our model.…”

Section: A Datamentioning

confidence: 99%

Discovering Chemically Novel, High-Temperature Superconductors

Seegmiller

Baird

Sayeed

et al. 2023

Preprint

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One of the biggest unsolved problems in condensed matter physics is what mechanism causes high-temperature superconductivity and if there is a material that can exhibit superconductivity at both room temperature and atmospheric pressure. Among the many important properties of a superconductor, the critical temperature (Tc) or transition temperature is the point at which a material transitions into a superconductive state. In this implementation, machine learning is used to predict the critical temperatures of chemically unique compounds in an attempt to identify new chemically novel, high-temperature superconductors. The training data set (SuperCon) consists of known superconductors and their critical temperatures, and the testing data set (NOMAD) consists of around 700,000 novel chemical formulae. The chemical formulae in these data sets are first passed through a collection of rapid screening tools, SMACT, to check for chemical validity. Next, the DiSCoVeR algorithm is used to train on the SuperCon data to form a model, and then screens through batches of the formulae in the NOMAD data set. Having a combination of a chemical distance metric, density-aware dimensionality reduction, clustering, and a regression model, the DiSCoVeR algorithm serves as a tool to identify and assess these superconducting compositions [1]. This research and implementation resulted in the screening of chemically novel compositions exhibiting critical temperatures upwards of 150 K, which correlates to superconductors in the cuprate class. This implementation demonstrates a process of performing machine learning-assisted superconductor screening (while exploring chemically distinct spaces) which can be utilized in the materials discovery process.

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Materials Informatics Reveals Unexplored Structure Space in Cuprate Superconductors

Cited by 6 publications

References 43 publications

In Pursuit of the Exceptional: Research Directions for Machine Learning in Chemical and Materials Science

In Pursuit of the Exceptional: Research Directions for Machine Learning in Chemical and Materials Science

In Pursuit of the Exceptional: Research Directions for Machine Learning in Chemical and Materials Science

Discovering Chemically Novel, High-Temperature Superconductors

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