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Although the convergence of high-performance computing, automation, and machine learning has significantly altered the materials design timeline, transformative advances in functional materials and acceleration of their design will require…

Progress in functional materials discovery has been accelerated by advances in high throughput materials synthesis and by the development of high-throughput computation. However, a complementary robust and high throughput structural…

Materials Science · Physics 2021-11-30 Jiadong Dan , Xiaoxu Zhao , Shoucong Ning , Jiong Lu , Kian Ping Loh , N. Duane Loh , Stephen J. Pennycook

Historically, materials discovery has been driven by a laborious trial-and-error process. The growth of materials databases and emerging informatics approaches finally offer the opportunity to transform this practice into data- and…

Materials Science · Physics 2017-06-28 Olexandr Isayev , Corey Oses , Cormac Toher , Eric Gossett , Stefano Curtarolo , Alexander Tropsha

The Probe-Particle Model combine theories designed for the simulation of scanning probe microscopy experiments, employing non-reactive, flexible tip apices to achieve sub-molecular resolution. In the article we present the latest version of…

Mesoscale and Nanoscale Physics · Physics 2024-07-02 Niko Oinonen , Aliaksandr V. Yakutovich , Aurelio Gallardo , Martin Ondracek , Prokop Hapala , Ondrej Krejci

Piezoresponse force microscopy (PFM) is a powerful tool widely used to characterize piezoelectricity and ferroelectricity at the nanoscale. However, it is necessary to distinguish microscopic mechanisms between piezoelectricity and…

Materials Science · Physics 2018-04-26 Junxi Yu , Ehsan Nasr Esfahani , Qingfeng Zhu , Dongliang Shan , Tingting Jia , Shuhong Xie , Jiangyu Li

Quantitative description of finite-temperature properties of displacive ferroelectrics, and in particular the critical behavior, is of fundamental importance to both theory and device design, going beyond the Landau-Ginzburg approach, which…

Materials Science · Physics 2026-05-05 F. Yang , L. Q. Chen

This study introduces BIRDSHOT, an integrated Bayesian materials discovery framework designed to efficiently explore complex compositional spaces while optimizing multiple material properties. We applied this framework to the CoCrFeNiVAl…

Machine learning (ML) models utilizing structure-based features provide an efficient means for accurate property predictions across diverse chemical spaces. However, obtaining equilibrium crystal structures typically requires expensive…

Materials Science · Physics 2021-04-22 Yunxing Zuo , Mingde Qin , Chi Chen , Weike Ye , Xiangguo Li , Jian Luo , Shyue Ping Ong

Self-driving labs (SDLs), employing automation and machine learning (ML) to accelerate experimental procedures, have enormous potential in the discovery of new materials. However, in thin film science, SDLs are mainly restricted to…

Machine Learning · Computer Science 2026-01-12 Sanna Jarl , Jens Sjölund , Robert J. W. Frost , Anders Holst , Jonathan J. S. Scragg

Under a sufficiently high applied electric field, a non-polar antiferroelectric material, such as \ce{PbZrO3}, can undergo a rapid transformation to a polar ferroelectric phase. While this behavior is promising for energy storage and…

Scanning tunnelling microscopy (STM) with a functionalized tip apex reveals the geometric and electronic structure of a sample within the same experiment. However, the complex nature of the signal makes images difficult to interpret and has…

Materials Science · Physics 2023-12-18 Lauri Kurki , Niko Oinonen , Adam S. Foster

The optimization of expensive-to-evaluate black-box functions over combinatorial structures is an ubiquitous task in machine learning, engineering and the natural sciences. The combinatorial explosion of the search space and costly…

Machine Learning · Statistics 2018-10-11 Ricardo Baptista , Matthias Poloczek

Electromechanical coupling is ubiquitous in nature and underpins the functionality of materials and systems as diverse as ferroelectric and multiferroic materials, electrochemical devices, and biological systems, and strain-based scanning…

Materials Science · Physics 2014-04-10 Qian Nataly Chen , Yun Ou , Feiyue Ma , Jiangyu Li

Rapid emergence of the multimodal imaging in scanning probe, electron, and optical microscopies have brought forth the challenge of understanding the information contained in these complex data sets, targeting both the intrinsic…

Materials Science · Physics 2021-10-14 Yongtao Liu , Maxim Ziatdinov , Sergei V. Kalinin

Underlying the rapidly increasing photovoltaic efficiency and stability of metal halide perovskites (MHPs) is the advance in the understanding of the microstructure of polycrystalline MHP thin film. Over the past decade, intense efforts…

Realizing high-throughput aberration-corrected Scanning Transmission Electron Microscopy (STEM) exploration of atomic structures requires rapid tuning of multipole probe correctors while compensating for the inevitable drift of the optical…

Machine Learning · Computer Science 2026-01-28 Utkarsh Pratiush , Austin Houston , Richard Liu , Gerd Duscher , Sergei Kalinin

Fast scanning probe microscopy enabled via machine learning allows for a broad range of nanoscale, temporally resolved physics to be uncovered. However, such examples for functional imaging are few in number. Here, using piezoresponse force…

The presence of electrostatic forces and associated artifacts complicates the interpretation of piezoresponse force microscopy (PFM) and electrochemical strain microscopy (ESM). Eliminating these artifacts provides an opportunity for…

Mesoscale and Nanoscale Physics · Physics 2021-12-20 Jason P. Killgore , Larry Robins , Liam Collins

Transition metal chromophores with earth-abundant transition metals are an important design target for their applications in lighting and non-toxic bioimaging, but their design is challenged by the scarcity of complexes that simultaneously…

Chemical Physics · Physics 2022-09-16 Chenru Duan , Aditya Nandy , Gianmarco Terrones , David W. Kastner , Heather J. Kulik

Molecular optimization, which aims to discover improved molecules from a vast chemical search space, is a critical step in chemical development. Various artificial intelligence technologies have demonstrated high effectiveness and…

Chemical Physics · Physics 2024-11-26 Xin Xia , Yajie Zhang , Xiangxiang Zeng , Xingyi Zhang , Chunhou Zheng , Yansen Su
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