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Materials discovery in combinatorial and high-throughput synthesis and processing: A new Frontier for SPM
Published
Author(s)
Boris Slautin, Yungtao Liu, Kamyar Barakati, Yu Liu, Reece Emery, Seungbum Hong, Astita Dubey, Vladimir Shvartsman, Doru Lupascu, Sheryl Sanchez, Mahshid Ahmadi, Yunseok Kim, Evgheni Strelcov, Keith Brown, Philip Rack, Sergei Kalinin
Abstract
For over three decades, scanning probe microscopy (SPM) has been a key method for exploring material structures and functionalities at nanometer and often atomic scales in ambient, liquid, and vacuum environments. Historically, SPM applications have predominantly been downstream, with images and spectra serving as a qualitative source of data on the microstructure and properties of materials, and in rare cases of fundamental physical knowledge. However, the fast growing developments in accelerated material synthesis via self-driving labs and established applications such as combinatorial spread libraries are poised to change this paradigm. Rapid synthesis demands matching capabilities to probe structure and functionalities of materials on small scales and with high throughput, which are characteristically inherent to SPM. Here, we overview SPM methods applicable to these emerging applications and emphasize their quantitativeness, focusing on piezoresponse force microscopy, electrochemical strain microscopy, conductive, and surface photovoltage measurements. We discuss the challenges and opportunities ahead, asserting that SPM will play a crucial role in closing the loop from material prediction and synthesis to characterization.
Slautin, B.
, Liu, Y.
, Barakati, K.
, Liu, Y.
, Emery, R.
, Hong, S.
, Dubey, A.
, Shvartsman, V.
, Lupascu, D.
, Sanchez, S.
, Ahmadi, M.
, Kim, Y.
, Strelcov, E.
, Brown, K.
, Rack, P.
and Kalinin, S.
(2025),
Materials discovery in combinatorial and high-throughput synthesis and processing: A new Frontier for SPM, Applied Physics Reviews, [online], https://doi.org/10.1063/5.0259851, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=959679
(Accessed October 6, 2025)