On-the-fly Autonomous Control of Neutron Diffraction via Physics-Informed Bayesian Active Learning

McDannald, Austin; Frontzek, Matthias; Savici, A. T.; Doucet, M.; Rodriguez, Efrain E.; Meuse, Kate; Opsahl-Ong, Jessica; Samarov, Daniel V.; Takeuchi, Ichiro; Kusne, A. Gilad; Ratcliff, William

doi:10.48550/arxiv.2108.08918

Cited by 4 publications

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“…In practice, the BI is performed using sampling algorithms based on the Markov Chain Monte Carlo (MCMC) techniques. [46][47][48] Here, specifically, we are using the No-U-Turn Sampler (NUTS) algorithm as implemented in the NumPyro probabilistic programming library. [49] The posterior predictive mean and variance for a new point, x * , given the measured data, D, are then computed as…”

Section: Resultsmentioning

confidence: 99%

Hypothesis Learning in Automated Experiment: Application to Combinatorial Materials Libraries

et al. 2022

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Machine learning is rapidly becoming an integral part of experimental physical discovery via automated and high‐throughput synthesis, and active experiments in scattering and electron/probe microscopy. This, in turn, necessitates the development of active learning methods capable of exploring relevant parameter spaces with the smallest number of steps. Here, an active learning approach based on conavigation of the hypothesis and experimental spaces is introduced. This is realized by combining the structured Gaussian processes containing probabilistic models of the possible system's behaviors (hypotheses) with reinforcement learning policy refinement (discovery). This approach closely resembles classical human‐driven physical discovery, when several alternative hypotheses realized via models with adjustable parameters are tested during an experiment. This approach is demonstrated for exploring concentration‐induced phase transitions in combinatorial libraries of Sm‐doped BiFeO3 using piezoresponse force microscopy, but it is straightforward to extend it to higher‐dimensional parameter spaces and more complex physical problems once the experimental workflow and hypothesis generation are available.

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Section: Resultsmentioning

confidence: 99%

Hypothesis Learning in Automated Experiment: Application to Combinatorial Materials Libraries

et al. 2022

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“…A more advanced version of this was recently implemented for APS neutron scattering to identify magnetic dynamic parameters. 11 The second challenge provided to the student was one of hypothesis or model selection. The student was asked to use LEGOLAS to figure out the underlying model and its parameters if a set of possible models was provided.…”

Section: Use Of the Education Kitmentioning

confidence: 99%

The LEGOLAS Kit: A low-cost robot science kit for education with symbolic regression for hypothesis discovery and validation

et al. 2022

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is a founder of MEST, a company working on commercializing the LEGOLAS kit. The data sets generated during and/or analyzed during the current study are available from the authors on reasonable request. NIST Disclaimer: Certain commercial equipment, instruments, or materials are identified in this report in order to specify the experimental procedure adequately. Such identification is not intended to imply recommendation or endorsement by the National Institute of Standards and Technology, nor is it intended to imply that the materials or equipment identified are necessarily the best available for the purpose.

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“…Notable examples include determination of phase diagrams 33 , parameterization of ML force fields 34 , design of organometallic complexes 35 , and computational searches for CO 2 electrocatalytic alloys 36 . Notable demonstrations of active learning in the laboratory setting include determining the reaction conditions for polyoxometalate crystallization 37,38 , antisolvent vapor diffusion syntheses of halide perovskites 22,23 , electrocatalytic alloys for oxygen evolution reactions 39 , alloy phase mapping 15 , neutron scattering determinations of magnetic properties 40 , determination of material property curves 41 , and battery electrolyte optimization 16 . Active learning is typically framed in the context of parameterizing a single model applicable to the entire problem domain.…”

Section: Introductionmentioning

confidence: 99%

Active meta-learning for predicting and selecting perovskite crystallization experiments

Shekar

Nicholas

Najeeb

et al. 2022

The Journal of Chemical Physics

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Autonomous experimentation systems use algorithms and data from prior experiments to select and perform new experiments in order to meet a specified objective. In most experimental chemistry situations, there is a limited set of prior historical data available, and acquiring new data may be expensive and time consuming, which places constraints on machine learning methods. Active learning methods prioritize new experiment selection by using machine learning model uncertainty and predicted outcomes. Meta-learning methods attempt to construct models that can learn quickly with a limited set of data for a new task. In this paper, we applied the model-agnostic meta-learning (MAML) model and the Probabilistic LATent model for Incorporating Priors and Uncertainty in few-Shot learning (PLATIPUS) approach, which extends MAML to active learning, to the problem of halide perovskite growth by inverse temperature crystallization. Using a dataset of 1870 reactions conducted using 19 different organoammonium lead iodide systems, we determined the optimal strategies for incorporating historical data into active and meta-learning models to predict reaction compositions that result in crystals. We then evaluated the best three algorithms (PLATIPUS and active-learning k-nearest neighbor and decision tree algorithms) with four new chemical systems in experimental laboratory tests. With a fixed budget of 20 experiments, PLATIPUS makes superior predictions of reaction outcomes compared to other active-learning algorithms and a random baseline.

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On-the-fly Autonomous Control of Neutron Diffraction via Physics-Informed Bayesian Active Learning

Cited by 4 publications

References 0 publications

Hypothesis Learning in Automated Experiment: Application to Combinatorial Materials Libraries

Hypothesis Learning in Automated Experiment: Application to Combinatorial Materials Libraries

The LEGOLAS Kit: A low-cost robot science kit for education with symbolic regression for hypothesis discovery and validation

Active meta-learning for predicting and selecting perovskite crystallization experiments

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