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Accelerated search for materials with targeted properties by adaptive design.

Xue D, Balachandran PV, Hogden J, Theiler J, Xue D, Lookman T - Nat Commun (2016)

Bottom Line: Here we show how an adaptive design strategy, tightly coupled with experiments, can accelerate the discovery process by sequentially identifying the next experiments or calculations, to effectively navigate the complex search space.We synthesize and characterize 36 predicted compositions (9 feedback loops) from a potential space of ∼800,000 compositions.Of these, 14 had smaller ΔT than any of the 22 in the original data set.

View Article: PubMed Central - PubMed

Affiliation: Theoretical Division, Los Alamos National Laboratory, MS-B262, Los Alamos, New Mexico 87545, USA.

ABSTRACT
Finding new materials with targeted properties has traditionally been guided by intuition, and trial and error. With increasing chemical complexity, the combinatorial possibilities are too large for an Edisonian approach to be practical. Here we show how an adaptive design strategy, tightly coupled with experiments, can accelerate the discovery process by sequentially identifying the next experiments or calculations, to effectively navigate the complex search space. Our strategy uses inference and global optimization to balance the trade-off between exploitation and exploration of the search space. We demonstrate this by finding very low thermal hysteresis (ΔT) NiTi-based shape memory alloys, with Ti50.0Ni46.7Cu0.8Fe2.3Pd0.2 possessing the smallest ΔT (1.84 K). We synthesize and characterize 36 predicted compositions (9 feedback loops) from a potential space of ∼800,000 compositions. Of these, 14 had smaller ΔT than any of the 22 in the original data set.

No MeSH data available.


Related in: MedlinePlus

Our adaptive design loop.(a) Prior knowledge, including data from previous experiments and physical models, and relevant features are used to describe the materials. This information is used within a machine learning framework to make predictions that include error estimates. The results are used by an experimental design tool (for example, Global optimization) that suggests new experiments (synthesis and characterization) performed in this work, with the dual goals of model improvement and materials discovery. The results feed into a database, which provides input for the next iteration of the design loop. The green arrows represent the step-wise approach of the state-of-art using experiments or calculations, although few studies have demonstrated feedback. The red star shows that although sample number 3 is not the best predicted choice relative to sample 4, the ‘expected improvement' by selecting it is greater than other choices due to the large uncertainty. (b) Our loop, as executed in practice specific to the design problem featured in this work, is as follows: (i) an initial alloy experimental data set with known thermal dissipation ΔT and features or materials descriptors serves as input to the inference model. (ii) The model is trained and cross-validated with the initial alloy data. (iii) A data set of unexplored alloys defines the total search space of probable candidates. The trained model in (ii) is applied to all the alloys in (iii), to predict their ΔT. (iv) The design chooses the ‘best' four candidates for synthesis and characterization. (v) The new alloys, with their measured ΔT, augment the initial data set to further improve the inference and design. The four alloys for experiments are chosen iteratively by augmenting four times the initial data set with each new predicted alloy from the inference and design.
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f1: Our adaptive design loop.(a) Prior knowledge, including data from previous experiments and physical models, and relevant features are used to describe the materials. This information is used within a machine learning framework to make predictions that include error estimates. The results are used by an experimental design tool (for example, Global optimization) that suggests new experiments (synthesis and characterization) performed in this work, with the dual goals of model improvement and materials discovery. The results feed into a database, which provides input for the next iteration of the design loop. The green arrows represent the step-wise approach of the state-of-art using experiments or calculations, although few studies have demonstrated feedback. The red star shows that although sample number 3 is not the best predicted choice relative to sample 4, the ‘expected improvement' by selecting it is greater than other choices due to the large uncertainty. (b) Our loop, as executed in practice specific to the design problem featured in this work, is as follows: (i) an initial alloy experimental data set with known thermal dissipation ΔT and features or materials descriptors serves as input to the inference model. (ii) The model is trained and cross-validated with the initial alloy data. (iii) A data set of unexplored alloys defines the total search space of probable candidates. The trained model in (ii) is applied to all the alloys in (iii), to predict their ΔT. (iv) The design chooses the ‘best' four candidates for synthesis and characterization. (v) The new alloys, with their measured ΔT, augment the initial data set to further improve the inference and design. The four alloys for experiments are chosen iteratively by augmenting four times the initial data set with each new predicted alloy from the inference and design.

Mentions: Our iterative feedback loop is schematically shown in Fig. 1a and includes the use of inference, uncertainties, global optimization and feedback from experiments (collectively referred to as ‘adaptive design'). In Fig. 1b we show how we exercise the loop, the key ingredients of which are as follows: (i) a training data set of alloys, each described by features and with a desired property (that is, ΔT) that has been measured; (ii) an inference model (regressor) that uses the training data to learn the feature–property relationship, with associated uncertainties; (iii) the trained model is applied to the search space of unexplored alloy compositions (for which the property has not been measured), to predict ΔT with associated uncertainties; (iv) design or global optimization (selector) that provides the next candidate alloy for experiment by balancing the trade-off between exploitation (choosing the material with the best predicted property) and exploration (using the predicted uncertainties to study regions of search space where the model is less accurate); and (v) feedback from experiments allowing the subsequent iterative improvement of the inference model.


Accelerated search for materials with targeted properties by adaptive design.

Xue D, Balachandran PV, Hogden J, Theiler J, Xue D, Lookman T - Nat Commun (2016)

Our adaptive design loop.(a) Prior knowledge, including data from previous experiments and physical models, and relevant features are used to describe the materials. This information is used within a machine learning framework to make predictions that include error estimates. The results are used by an experimental design tool (for example, Global optimization) that suggests new experiments (synthesis and characterization) performed in this work, with the dual goals of model improvement and materials discovery. The results feed into a database, which provides input for the next iteration of the design loop. The green arrows represent the step-wise approach of the state-of-art using experiments or calculations, although few studies have demonstrated feedback. The red star shows that although sample number 3 is not the best predicted choice relative to sample 4, the ‘expected improvement' by selecting it is greater than other choices due to the large uncertainty. (b) Our loop, as executed in practice specific to the design problem featured in this work, is as follows: (i) an initial alloy experimental data set with known thermal dissipation ΔT and features or materials descriptors serves as input to the inference model. (ii) The model is trained and cross-validated with the initial alloy data. (iii) A data set of unexplored alloys defines the total search space of probable candidates. The trained model in (ii) is applied to all the alloys in (iii), to predict their ΔT. (iv) The design chooses the ‘best' four candidates for synthesis and characterization. (v) The new alloys, with their measured ΔT, augment the initial data set to further improve the inference and design. The four alloys for experiments are chosen iteratively by augmenting four times the initial data set with each new predicted alloy from the inference and design.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4835535&req=5

f1: Our adaptive design loop.(a) Prior knowledge, including data from previous experiments and physical models, and relevant features are used to describe the materials. This information is used within a machine learning framework to make predictions that include error estimates. The results are used by an experimental design tool (for example, Global optimization) that suggests new experiments (synthesis and characterization) performed in this work, with the dual goals of model improvement and materials discovery. The results feed into a database, which provides input for the next iteration of the design loop. The green arrows represent the step-wise approach of the state-of-art using experiments or calculations, although few studies have demonstrated feedback. The red star shows that although sample number 3 is not the best predicted choice relative to sample 4, the ‘expected improvement' by selecting it is greater than other choices due to the large uncertainty. (b) Our loop, as executed in practice specific to the design problem featured in this work, is as follows: (i) an initial alloy experimental data set with known thermal dissipation ΔT and features or materials descriptors serves as input to the inference model. (ii) The model is trained and cross-validated with the initial alloy data. (iii) A data set of unexplored alloys defines the total search space of probable candidates. The trained model in (ii) is applied to all the alloys in (iii), to predict their ΔT. (iv) The design chooses the ‘best' four candidates for synthesis and characterization. (v) The new alloys, with their measured ΔT, augment the initial data set to further improve the inference and design. The four alloys for experiments are chosen iteratively by augmenting four times the initial data set with each new predicted alloy from the inference and design.
Mentions: Our iterative feedback loop is schematically shown in Fig. 1a and includes the use of inference, uncertainties, global optimization and feedback from experiments (collectively referred to as ‘adaptive design'). In Fig. 1b we show how we exercise the loop, the key ingredients of which are as follows: (i) a training data set of alloys, each described by features and with a desired property (that is, ΔT) that has been measured; (ii) an inference model (regressor) that uses the training data to learn the feature–property relationship, with associated uncertainties; (iii) the trained model is applied to the search space of unexplored alloy compositions (for which the property has not been measured), to predict ΔT with associated uncertainties; (iv) design or global optimization (selector) that provides the next candidate alloy for experiment by balancing the trade-off between exploitation (choosing the material with the best predicted property) and exploration (using the predicted uncertainties to study regions of search space where the model is less accurate); and (v) feedback from experiments allowing the subsequent iterative improvement of the inference model.

Bottom Line: Here we show how an adaptive design strategy, tightly coupled with experiments, can accelerate the discovery process by sequentially identifying the next experiments or calculations, to effectively navigate the complex search space.We synthesize and characterize 36 predicted compositions (9 feedback loops) from a potential space of ∼800,000 compositions.Of these, 14 had smaller ΔT than any of the 22 in the original data set.

View Article: PubMed Central - PubMed

Affiliation: Theoretical Division, Los Alamos National Laboratory, MS-B262, Los Alamos, New Mexico 87545, USA.

ABSTRACT
Finding new materials with targeted properties has traditionally been guided by intuition, and trial and error. With increasing chemical complexity, the combinatorial possibilities are too large for an Edisonian approach to be practical. Here we show how an adaptive design strategy, tightly coupled with experiments, can accelerate the discovery process by sequentially identifying the next experiments or calculations, to effectively navigate the complex search space. Our strategy uses inference and global optimization to balance the trade-off between exploitation and exploration of the search space. We demonstrate this by finding very low thermal hysteresis (ΔT) NiTi-based shape memory alloys, with Ti50.0Ni46.7Cu0.8Fe2.3Pd0.2 possessing the smallest ΔT (1.84 K). We synthesize and characterize 36 predicted compositions (9 feedback loops) from a potential space of ∼800,000 compositions. Of these, 14 had smaller ΔT than any of the 22 in the original data set.

No MeSH data available.


Related in: MedlinePlus