xtal2png: A Python package for representing crystal
structure as PNG files

Baird, Sterling G.; Jablonka, Kevin Maik; Alverson, Michael; Sayeed, Hasan M.; Khan, Mohammed Faris; Seegmiller, Colton C.; Smit, Berend; Sparks, Taylor D.

doi:10.21105/joss.04528

Cited by 13 publications

(13 citation statements)

References 15 publications

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“…Some of the most successful applications of machine learning have been in computer vision. 78 The primary reason for this is that while images contain a wealth of information, it is hard to extract good features that can then be fed into a model (e.g., training a linear regressor on a flat vector of all the pixel values will not work). Convolutional neural networks (CNNs) and related architectures are trainable feature extractors.…”

Section: Scheme 2 Example Of Using Aggregations In Mofdscribe Amentioning

confidence: 99%

An Ecosystem for Digital Reticular Chemistry

et al. 2023

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Section: Scheme 2 Example Of Using Aggregations In Mofdscribe Amentioning

confidence: 99%

An Ecosystem for Digital Reticular Chemistry

et al. 2023

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“…Al 2 O 3 ≡ 0.4Al + 0.6O where 0.4 + 0.6 = 1.0) [46][47][48][49][50][51][52][53][54][55][56][57] (referred to as "comp") or a set of parameters that exhibits permutation invariance (e.g. [45,47,58] (referred to as "order"). When no additional parameter constraints other than lower and upper limits are used, we refer to this as "Bounds-only".…”

Section: Reducible and Irreducible Search Spacesmentioning

confidence: 99%

Compactness Matters: Improving Bayesian Optimization Efficiency of Materials Formulations through Invariant Search Spaces

Baird

Hall

Sparks

2023

Preprint

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Would you rather search for a line inside a cube or a point inside a square? Physics-based simulations and wet-lab experiments often have symmetries (degeneracies) that allow reducing problem dimensionality or search space, but constraining these degeneracies is often unsupported or difficult to implement in many optimization packages, requiring additional time and expertise. So, are the possible improvements in efficiency worth the cost of implementation? We demonstrate that the compactness of a search space (to what extent and how degenerate solutions and non-solutions are removed) affects Bayesian optimization search efficiency. Here, we use the Adaptive Experimentation (Ax) Platform by Meta and a physics-based particle packing simulation with eight or nine tunable parameters, depending on the search space compactness. These parameters represent three truncated log-normal distributions of particle sizes which exhibit compositional-invariance and permutation-invariance characteristic of formulation problems (e.g., chemical formulas, composite materials, alloys). We assess a total of four search space types which range from none up to both constraint types imposed simultaneously. In general, the removal of degeneracy through problem reformulation (as seen by the optimizers surrogate model) improves optimization efficiency. We recommend that optimization practitioners in the physical sciences carefully consider the trade-off between implementation cost and search efficiency before running expensive optimization campaigns.

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“…Al 2 O 3 ≡ 0.4Al + 0.6O where 0.4 + 0.6 = 1.0) [39][40][41][42][43][44][45][46][47][48][49][50] (referred to as "comp"), or a set of parameters that exhibits permutation invariance (e.g. [36,40,51] (referred to as "order"). A ubiquitous example exists in image processing, where machine learning algorithms often rely on data augmentation to account for rotational invariance [52], a type of invariance which is not addressed in this work.…”

Section: Reducible and Irreducible Search Spacesmentioning

confidence: 99%

The most compact search space is not always the most efficient: A case study on maximizing solid rocket fuel packing fraction via constrained Bayesian optimization

Baird

Hall

Sparks

2022

Preprint

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Would you rather search for a line inside a cube or a point inside a square? This type of solution degeneracy often exists in physics-based simulations and wet-lab experiments, but constraining these degeneracies is often unsupported or difficult to implement in many optimization packages, requiring additional time and expertise. So, are the possible improvements in efficiency worth the cost of implementation? We demonstrate that the compactness of a search space (to what extent and how degenerate solutions and non-solutions are removed) can significantly affect Bayesian optimization search efficiency via the Ax platform. We use a physics-based particle packing simulation with seven to nine tunable parameters, depending on the search space compactness, that represent three truncated, discrete log-normal distributions of particle sizes. This physics-based simulation exhibits three qualitatively different degeneracy types: size-invariance, compositional-invariance, and permutation-invariance. We assess a total of eight search space types which range from none up to all three constraint types imposed simultaneously. We find that leaving the search space unconstrained leads to a large variance in the outcome and that on average, the most constrained search space is not always the most efficient. Likewise, the least constrained search space is not always the least efficient. We recommend that optimization practitioners in the physical sciences carefully consider the impact of removing search space degeneracies on search efficiency before running expensive optimization campaigns.

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xtal2png: A Python package for representing crystal structure as PNG files

Cited by 13 publications

References 15 publications

An Ecosystem for Digital Reticular Chemistry

An Ecosystem for Digital Reticular Chemistry

Compactness Matters: Improving Bayesian Optimization Efficiency of Materials Formulations through Invariant Search Spaces

The most compact search space is not always the most efficient: A case study on maximizing solid rocket fuel packing fraction via constrained Bayesian optimization

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