Related papers: Predictions of photophysical properties of phospho…
Phosphorescent organic light-emitting diodes (PhOLEDs) are widely used in the display industry. In PhOLEDs, cyclometalated Ir(III) complexes are the most widespread triplet emitter dopants to attain red, e.g., Ir(piq)3 (piq =…
We review theories of phosphorescence in cyclometalated complexes. We focus primarily on pseudooctahedrally coordinated $t_{2g}^6$ metals (e.g., [Os(II)(bpy)$_3$]$^{2+}$, Ir(III)(ppy)$_3$ and Ir(III)(ptz)$_3$) as, for reasons that are…
Low cost, easily integrable photodetectors (PDs) for silicon (Si) photonics are still a bottleneck for photonic integrated circuits (PICs), especially for wavelengths above 1.8 ${\mu}$m. Multilayered platinum diselenide (PtSe$_2$) is a…
We introduce a local machine-learning method for predicting the electron densities of periodic systems. The framework is based on a numerical, atom-centred auxiliary basis, which enables an accurate expansion of the all-electron density in…
Photoactive iridium complexes are of broad interest due to their applications ranging from lighting to photocatalysis. However, the excited state property prediction of these complexes challenges ab initio methods such as time-dependent…
Most materials recently developed for room temperature phosphorescence (RTP) lack of practical relevance due to their inconvenient crystalline morphology. Using amorphous material systems instead, programmable luminescent tags (PLTs) based…
We introduce a computational framework leveraging universal machine learning interatomic potentials (MLIPs) to dramatically accelerate the calculation of photoluminescence (PL) spectra of atomic or molecular emitters with ab initio…
We present a comprehensive model to analyze, quantitatively, and predict the process of degradation of organic light-emitting diodes (OLEDs) considering all possible degradation mechanisms, i.e., polaron, exciton, exciton-polaron…
Despite major advances in oncology, many chemotherapeutic agents still cause severe side effects that reduce quality of life, motivating new approaches for early detection and targeted elimination of cancer cells. Luminescent transition…
We use machine learning models to predict ion density and electron temperature from visible emission spectra, in a high energy density pulsed-power-driven aluminum plasma, generated by an exploding wire array. Radiation transport…
Accurate prediction of pure component physiochemical properties is crucial for process integration, multiscale modeling, and optimization. In this work, an enhanced framework for pure component property prediction by using explainable…
A Gaussian approximation machine learning interatomic potential for platinum is presented. It has been trained on DFT data computed for bulk, surfaces and nanostructured platinum, in particular nanoparticles. Across the range of tested…
A key step in interpreting gas-phase ion mobility coupled with mass spectrometry (IM-MS) data for unknown structure prediction involves identifying the most favorable protonated structure. In the gas phase, the site of protonation is…
This work presents a machine learning approach to predict novel perovskite oxide materials in the Pr-Al-O and Pr-Sc-O compound families with the potential for photoluminescence applications. The predicted materials exhibit a large bandgap…
This paper shows how data-driven machine learning approaches can improve growth control, reproducibility, and physical insight in the pulsed laser deposition (PLD) growth of correlated oxides. Despite well-known relationships between growth…
Materials synthesis platforms that are designed for autonomous experimentation are capable of collecting multimodal diagnostic data that can be utilized for feedback to optimize material properties. Pulsed laser deposition (PLD) is emerging…
A precise detection of palladium (Pd) ions is a critical challenge with significant socio-economic implications across various industrial and chemical sectors. Due to its widespread use and poor biodegradability, Pd2+ accumulates in…
There has been a veritable explosion of methods and software to perform machine-learned regression on datasets of electronic energies and forces to develop high-dimensional machine learned potential energy surfaces (ML-PESs). A major, but…
Multipactor is a nonlinear electron avalanche phenomenon that can severely impair the performance of high-power radio frequency (RF) devices and accelerator systems. Accurate prediction of multipactor susceptibility across different…
Purpose: Accurate electronic stopping power data is crucial for calculating radiation-induced effects in various applications, from dosimetry and radiotherapy to particle physics. In this study, Stacking Ensemble Machine Learning (EML)…