CrySPY: a crystal structure prediction tool accelerated by machine learning

Yamashita, Tomoki; Kanehira, Shinichi; Sato, Nobuya; Kino, Hiori; Terayama, Kei; Sawahata, Hikaru; Sato, T.; Utsuno, Futoshi; Tsuda, Koji; Miyake, Takashi; Oguchi, Tamio

doi:10.1080/27660400.2021.1943171

Cited by 15 publications

(9 citation statements)

References 49 publications

(73 reference statements)

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“…USPEX 91,92) and XtalOpt, 93,94) based on genetic algorithms, CrySPY, 95,96) based on Bayesian optimization, and CALYPSO, 97,98) based on particle swarm optimization methods, are examples of typical structural search tools. Using genetic algorithms as an example, this section describes the stable structure searching method.…”

Section: Theoretical Phase Diagrammentioning

confidence: 99%

Current Status and Future Scope of Phase Diagram Studies

et al. 2023

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Research on alloy phase diagrams started in the middle of the 19th century and progressed into the laborious and time-consuming process of constructing phase diagrams through experiments and phenomenological calculations with thermodynamic analysis. More recently, phase diagram research has evolved into the computation of theoretical phase diagrams based on first-principles calculations and the development of new materials for high-throughput data-driven types of phase diagrams. This paper discusses the features and problems of each technique using a collection of recent papers, with the aim of describing future problems in this field.

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Section: Theoretical Phase Diagrammentioning

confidence: 99%

Current Status and Future Scope of Phase Diagram Studies

et al. 2023

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“…Additionally, we note that Comp/Order had the best predicted (Figure 3) and validated (Figure 2) performance out of the eight search space types. Size/Order is varied and exhibits a mix of characteristics from the Size-only and Order-only solutions: large particle-size bounds/sharp peaks and peaks with similar heights 12 , respectively. We note that Size/Order had the worst performance out of 12 Size/Order shows peaks with similar heights as opposed to the first-peak-low, second-peak-high trend.…”

Section: Solutions Visualized As Summed Distributionsmentioning

confidence: 99%

“…BO has been used to create and adaptively refine surrogate models for physics-based simulations whether acting directly as the surrogate model [2,4,5,7] or tuning hyperparameters of a surrogate model [3,10] among other applications such as experimental discovery [6][7][8] and crystal structure prediction [11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Effect of reducible and irreducible search space representations on adaptive design efficiency: a case study on maximizing packing fraction for solid rocket fuel propellant simulations

Baird

Hall

Sparks

2022

Preprint

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Would you rather search for a point inside of a line or a line inside of a rectangle? This is a type of solution degeneracy that often exists physics-based simulations and wetlab experiments, but constraining these degeneracies is often unsupported or difficult-to-implement in many optimization packages, requiring additional time and expertise. So, is the increase in efficiency worth the cost of implementation? We demonstrate that the compactness of a search space (to what extent degenerate solutions and nonsolutions are removed) can have a significant effect on Bayesian optimization search efficiency via the Ax platform. As our optimization task, 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. The degeneracies are reflected in the outcomes being identical when: 1. all particle sizes are multiplied by a constant factor, 2. the fractional prevalences of the particle types sum to unity, and 3. sets of log-normal distribution parameters are swapped with each other, respectively. This simulation provides fertile ground for assessing the impact of multiple constraints on search efficiency, with a total of eight search space types which ranges from none up to all three constraints imposed simultaneously. Contrary to intuition, we find that, on average, the most compact search space performs worse than the least compact search space ((0.692 ± 0.036) vs. (0.699 ± 0.016) , respectively) over 50 iterations due to the interactions of the non-linear size-invariance degeneracy with other degeneracy types. The most efficient search space in terms of both predicted and validated outcomes is the combination of the composition and permutation constraints, resulting in a mean packing fraction of (0.728±0.010) over 50 iterations, where randomly sampled volume fractions are typically no less than 0.6. We recommend that optimization practitioners in the physical sciences carefully consider the impact of removing search space degeneracies on search efficiency prior to running expensive optimization campaigns.

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“…BO has been used to create and adaptively refine surrogate models for physics-based simulations whether acting directly as the surrogate model [2,4,5,9,[12][13][14][15][16][17][18][19][20] or tuning hyperparameters of a surrogate model [3,21]. Other examples include experimental discovery [7,9,11] and crystal structure prediction [22][23][24][25]. A review of Bayesian optimization applied to materials science in general is given in Kotthoff et al [26] with a review of advanced Bayesian optimization methods applied to materials science given in Arróyave et al [27].…”

Section: Introductionmentioning

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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CrySPY: a crystal structure prediction tool accelerated by machine learning

Cited by 15 publications

References 49 publications

Current Status and Future Scope of Phase Diagram Studies

Current Status and Future Scope of Phase Diagram Studies

Effect of reducible and irreducible search space representations on adaptive design efficiency: a case study on maximizing packing fraction for solid rocket fuel propellant simulations

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

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