Predicting glass structure by physics-informed machine learning

Bødker, Mikkel L.; Bauchy, Mathieu; Du, Tao; Mauro, John C.; Smedskjær, Morten Mattrup

doi:10.1038/s41524-022-00882-9

Cited by 17 publications

(16 citation statements)

References 59 publications

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“…Strategies of physics-informed machine learning − are one approach for this problem. A recent application of this approach to determining the structure of oxide glasses is described by Bødker et al However, this is less applicable to exceptional materials which involve new physics precluded by using existing models as constraints (e.g., using BCS theory to inform your ML model will hinder discovering cuprate superconductors). Feature selection corresponds to an implied constraint that only a small subset of the input variables determine the system performance.…”

Section: Recommendations Toward ML For Exceptional Materialsmentioning

confidence: 99%

“…Strategies of physics-informed machine learning 144−146 are one approach for this problem. A recent application of this approach to determining the structure of oxide glasses is described by Bødker et al 147 However, this is less applicable to exceptional materials which involve new physics precluded by using existing models as constraints (e.g., using BCS theory 95…”

Section: Recommendations Toward ML For Exceptional Materialsmentioning

confidence: 99%

See 1 more Smart Citation

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: Recommendations Toward ML For Exceptional Materialsmentioning

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

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“…Understanding phenomena across conditions or systems requires transferring what is learned from training for one task to another task. Bødker et al [467] shows a successful example in which a ML model predicts nonlinear composition-structure relationships for glass compositions outside its training set.…”

Section: Specificity Vs Universality (Transferability)mentioning

confidence: 99%

Soft matter roadmap^*

Barrat,

Del Gado,

Egelhaaf

et al. 2023

J. Phys. Mater.

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Soft materials are usually defined as materials made of mesoscopic entities, often self-organized, sensitive to thermal fluctuations and to weak perturbations. Archetypal examples are colloids, polymers, amphiphiles, liquid crystals, foams. The importance of soft materials in everyday commodity products, as well as in technological applications, is enormous, and controlling or improving their properties is the focus of many efforts. From a fundamental perspective, the possibility of manipulating soft material properties, by tuning interactions between constituents and by applying external perturbations, gives rise to an almost unlimited variety in physical properties. Together with the relative ease to observe and characterize them, this renders soft matter systems powerful model systems to investigate statistical physics phenomena, many of them relevant as well to hard condensed matter systems. Understanding the emerging properties from mesoscale constituents still poses enormous challenges, which have stimulated a wealth of new experimental approaches, including the synthesis of new systems with, e.g., tailored self-assembling properties, or novel experimental techniques in imaging, scattering or rheology. Theoretical and numerical methods, and coarse-grained models, have become central to predict physical properties of soft materials, while computational approaches that also use machine learning tools are playing a progressively major role in many investigations.This roadmap paper intends to give a broad overview of recent and possible future activities in the field of soft materials, with experts covering various developments and challenges in material synthesis and characterization, instrumental, simulation and theoretical methods as well as general concepts.

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“…Assisted by matrix rotation, principal component analysis (PCA, [ 13 ] ) and family trees, those models that are essential for the studied property are identified, and from their characteristic mean composition, the essential genes. Thereby, the absolute concentration of a gene is initially undetermined; other than with common regression analysis or similar machine learning approaches which are frequently applied in glass science, [ 14 ] relationships between the weighted sum of certain elemental components and a target property are not in the focus of the present study. The genes, however, may act as descriptors in physics‐informed ML models for glass property predictions.…”

Section: Introductionmentioning

confidence: 99%

Genome Mining in Glass Chemistry Using Linear Component Analysis of Ion Conductivity Data

2023

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Understanding the multivariate origin of physical properties is particularly complex for polyionic glasses. As a concept, the term genome has been used to describe the entirety of structure‐property relations in solid materials, based on functional genes acting as descriptors for a particular property, for example, for input in regression analysis or other machine‐learning tools. Here, the genes of ionic conductivity in polyionic sodium‐conducting glasses are presented as fictive chemical entities with a characteristic stoichiometry, derived from strong linear component analysis (SLCA) of a uniquely consistent dataset. SLCA is based on a twofold optimization problem that maximizes the quality of linear regression between a property (here: ionic conductivity) and champion candidates from all possible combinations of elements. Family trees and matrix rotation analysis are subsequently used to filter for essential elemental combinations, and from their characteristic mean composition, the essential genes. These genes reveal the intrinsic relationships within the multivariate input data. While they do not require a structural representation in real space, how possible structural interpretations agree with intuitive understanding of structural entities known from spectroscopic experiments is finally demonstrated.

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Predicting glass structure by physics-informed machine learning

Cited by 17 publications

References 59 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

Soft matter roadmap^*

Genome Mining in Glass Chemistry Using Linear Component Analysis of Ion Conductivity Data

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Predicting glass structure by physics-informed machine learning

Cited by 17 publications

References 59 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

Soft matter roadmap*

Genome Mining in Glass Chemistry Using Linear Component Analysis of Ion Conductivity Data

Contact Info

Product

Resources

About

Soft matter roadmap^*