TY - JOUR

T1 - Using generative adversarial networks to match experimental and simulated inelastic neutron scattering data

AU - Anker, Andy S.

AU - Butler, Keith T.

AU - Le, Manh Duc

AU - Perring, Toby G.

AU - Thiyagalingam, Jeyan

N1 - Funding Information: ASA would like to thank the Augustinus Foundation, the Fab-rikant Vilhelm Pedersen og hustrus Foundation, the Haynmann Foundation, the Henry og Mary Skovs Foundation, the Knud Højgaard Foundation, the Thomas B. Thriges Foundation, and the Viet Jacobsen Foundation for nancial support to this research project. This work was partially supported by wave 1 of the UKRI Strategic Priorities Fund under the EPSRC (Grant No. EP/T001569/1), particularly the “AI for Science” theme within that grant and The Alan Turing Institute. The simulated datasets were generated using computing resources provided by STFC Scientic Computing Department's SCARF cluster. Exp2SimGAN was trained using computing resources provided by STFC Scientic Computing Department's PEARL cluster. TGP thanks collaborators A. T. Boothroyd and D. Prabhakaran in ref. 43 for their permission to use the PCSMO experimental datasets. Funding Information: ASA would like to thank the Augustinus Foundation, the Fabrikant Vilhelm Pedersen og hustrus Foundation, the Haynmann Foundation, the Henry og Mary Skovs Foundation, the Knud Højgaard Foundation, the Thomas B. Thriges Foundation, and the Viet Jacobsen Foundation for financial support to this research project. This work was partially supported by wave 1 of the UKRI Strategic Priorities Fund under the EPSRC (Grant No. EP/T001569/1), particularly the “AI for Science” theme within that grant and The Alan Turing Institute. The simulated datasets were generated using computing resources provided by STFC Scientific Computing Department's SCARF cluster. Exp2SimGAN was trained using computing resources provided by STFC Scientific Computing Department's PEARL cluster. TGP thanks collaborators A. T. Boothroyd and D. Prabhakaran in ref. 43 for their permission to use the PCSMO experimental datasets. Publisher Copyright: © 2023 The Author(s). Published by the Royal Society of Chemistry.

PY - 2023

Y1 - 2023

N2 - Supervised machine learning (ML) models are frequently trained on large datasets of physics-based simulations with the aim of being applied to experimental data. However, ML models trained on simulated data often struggle to perform on experimental data, because there is a shift in the data caused by experimental effects that might be challenging to simulate. We introduce Exp2SimGAN, an unsupervised image-to-image ML model to match simulated and experimental data. Ideally, training Exp2SimGAN only requires a set of experimental data and a set of (not necessarily corresponding) simulated data. Once trained, it can convert a simulated dataset into one that resembles an experiment, and vice versa. We trained Exp2SimGAN on simulated resolution convolved and unconvolved INS spectra. Consequently, Exp2SimGAN can perform a resolution convolution and deconvolution of simulated two- and three-dimensional INS spectra. We demonstrate that this is sufficient for Exp2SimGAN to match simulated and experimental INS data, enabling the analysis of experimental INS data using supervised ML, which was previously not possible. Finally, we provide a domain of application measure for Exp2SimGAN, allowing us to assess the likelihood that Exp2SimGAN will be successful on a specific dataset. Exp2SimGAN is a step towards the analysis of experimental data using supervised ML models trained on physics-based simulations.

AB - Supervised machine learning (ML) models are frequently trained on large datasets of physics-based simulations with the aim of being applied to experimental data. However, ML models trained on simulated data often struggle to perform on experimental data, because there is a shift in the data caused by experimental effects that might be challenging to simulate. We introduce Exp2SimGAN, an unsupervised image-to-image ML model to match simulated and experimental data. Ideally, training Exp2SimGAN only requires a set of experimental data and a set of (not necessarily corresponding) simulated data. Once trained, it can convert a simulated dataset into one that resembles an experiment, and vice versa. We trained Exp2SimGAN on simulated resolution convolved and unconvolved INS spectra. Consequently, Exp2SimGAN can perform a resolution convolution and deconvolution of simulated two- and three-dimensional INS spectra. We demonstrate that this is sufficient for Exp2SimGAN to match simulated and experimental INS data, enabling the analysis of experimental INS data using supervised ML, which was previously not possible. Finally, we provide a domain of application measure for Exp2SimGAN, allowing us to assess the likelihood that Exp2SimGAN will be successful on a specific dataset. Exp2SimGAN is a step towards the analysis of experimental data using supervised ML models trained on physics-based simulations.

U2 - 10.1039/d2dd00147k

DO - 10.1039/d2dd00147k

M3 - Journal article

AN - SCOPUS:85168335688

VL - 2

SP - 578

EP - 590

JO - Digital Discovery

JF - Digital Discovery

SN - 2635-098X

IS - 3

ER -