Novel Ultrabright and Air‐Stable Photocathodes Discovered from Machine Learning and Density Functional Theory Driven Screening

Abstract

The high brightness, low emittance electron beams achieved in modern X‐ray free‐electron lasers (XFELs) have enabled powerful X‐ray imaging tools, allowing molecular systems to be imaged at picosecond time scales and sub‐nanometer length scales. One of the most promising directions for increasing the brightness of XFELs is through the development of novel photocathode materials. Whereas past efforts aimed at discovering photocathode materials have typically employed trial‐and‐error‐based iterative approaches, this work represents the first data‐driven screening for high brightness photocathode materials. Through screening over 74 000 semiconducting materials, a vast photocathode dataset is generated, resulting in statistically meaningful insights into the nature of high brightness photocathode materials. This screening results in a diverse list of photocathode materials that exhibit intrinsic emittances that are up to 4x lower than currently used photocathodes. In a second effort, multiobjective screening is employed to identify the family of M2O (M = Na, K, Rb) that exhibits photoemission properties that are comparable to the current state‐of‐the‐art photocathode materials, but with superior air stability. This family represents perhaps the first intrinsically bright, visible light photocathode materials that are resistant to reactions with oxygen, allowing for their transport and storage in dry air environments.

Electron Sources for Accelerators

“…Theoretical and ab-initio modeling of photoemisison can allow virtual investigation of 10s of thousands of potential photocathode materials in a high throughput manner through the use of advanced materials databases and high performance computing. This can act as an efficient way of down selecting materials for detailed experimental investigations [25,26]. Finally, testing the performance of novel photocathode candidate materials in electron guns under realistic operating conditions is critical.…”

Discovery of Stable Surfaces with Extreme Work Functions by High‐Throughput Density Functional Theory and Machine Learning

“…Further, the work function model established in this work enabled the discovery of new ultra-bright photocathode materials reported in Refs. [72,73].…”

Exploring the Cs-Te phase space via high-throughput density-functional theory calculations beyond the generalized-gradient approximation

“…For these tasks, traditional trialand-error procedures are expensive and ineffective. New approaches from computational material science, exploiting the results of ab initio simulations to gain insight into the material properties, [7][8][9][10] have recently emerged as viable alternatives and/or complements to this empirical approach. The availability of open-access quantum material databases since the last few years [11][12][13][14][15] represents a valuable resource to find potential candidates for the targeted applications.…”

“…Likewise, the development of automated workflows for density-functional theory (DFT) calculations [16][17][18] in connection with machine learning techniques [19][20][21] has opened even broader perspectives to identify and design materials with suitable characteristics as photocathodes. 8 Despite their great potential, available schemes for highthroughput screening inevitably suffer from the intrinsic limitations of DFT with the local-density approximation or the generalized-gradient approximation (GGA) for the exchangecorrelation (xc) potential. The choice of these functionals is traditionally driven by the need to optimize the computational effort when exploring large configurational spaces including several thousands of entries.…”

“…Underestimation of band gaps is the most serious consequence of this approach but additional drawbacks manifest themselves in the formation energies as well as in the density of states, 22,23 especially when defects are present in the systems. 24 While the consolidated awareness of the pitfalls of semi-local DFT enables the application of effective workarounds, 8 such corrections are typically empirical and do not cover the most serious cases in which DFT fails reproducing the electronic structure of the materials even qualitatively. On the other hand, many-body perturbation theory, 25 the state-of-the-art method to compute electronic and optical excitations in solids, cannot be conveniently applied to high-throughput calculations yet.…”