Advancing material property prediction: using physics-informed machine learning models for viscosity.

In materials science, accurately computing properties like viscosity, melting point, and glass transition temperatures solely through physics-based models is challenging. Data-driven machine learning (ML) also poses challenges in constructing ML models, especially in the material science domain where data is limited. To address this, we integrate physics-informed descriptors from molecular dynamics (MD) simulations to enhance the accuracy and interpretability of ML models. Our current study focuses on accurately predicting viscosity in liquid systems using MD descriptors. In this work, we curated a comprehensive dataset of over 4000 small organic molecules' viscosities from scientific literature, publications, and online databases. This dataset enabled us to develop quantitative structure-property relationships (QSPR) consisting of descriptor-based and graph neural network models to predict temperature-dependent viscosities for a wide range of viscosities. The QSPR models reveal that including MD descriptors improves the prediction of experimental viscosities, particularly at the small data set scale of fewer than a thousand data points. Furthermore, feature importance tools reveal that intermolecular interactions captured by MD descriptors are most important for viscosity predictions. Finally, the QSPR models can accurately capture the inverse relationship between viscosity and temperature for six battery-relevant solvents, some of which were not included in the original data set. Our research highlights the effectiveness of incorporating MD descriptors into QSPR models, which leads to improved accuracy for properties that are difficult to predict when using physics-based models alone or when limited data is available.

Organic radical emitters: nature of doublet excitons in emissive layers.

Organic radical emitters have received significant attention as a new route to efficient organic light-emitting diodes (OLEDs). The electronic structure of radical emitters allows bypassing the triplet harvesting issue in current OLED devices. However, the nature of doublet excited states remains elusive due to the complex nature of emissive layers. To date, the computational efforts have treated radical carrying materials as isolated entities in the gas phase. However, OLED materials are applied as thin solid films where intermolecular interactions significantly impact optoelectronic properties of the devices. Here, we combine molecular dynamics simulations and quantum chemical calculations to evaluate the effect of emitter-host interactions on the performance of radical-based emissive layers. Results demonstrate that intermolecular interactions remarkably modulate the electronic properties of the radicals in the thin solid films. The doublet excitons of isolated emitters demonstrate a hybrid character of charge-transfer (CT) and local-excitation (LE), while the emitter-host clusters present a significant CT character. Further, the impact of static and dynamic disorders on the hole-electron recombination is studied. Although the host-emitter interactions simultaneously decrease radiative rates and increase non-radiative rates, the latter rates are 100 times smaller than the former rates, allowing internal quantum efficiency to reach 100% for the doublet-based emission process. The results of this study highlight the significant impact of host-emitter interactions on radiative and non-radiative recombination processes and offer guidelines to tune these interactions for advancing radical-based OLEDs.

Design of Organic Electronic Materials With a Goal-Directed Generative Model Powered by Deep Neural Networks and High-Throughput Molecular Simulations.

In recent years, generative machine learning approaches have attracted significant attention as an enabling approach for designing novel molecular materials with minimal design bias and thereby realizing more directed design for a specific materials property space. Further, data-driven approaches have emerged as a new tool to accelerate the development of novel organic electronic materials for organic light-emitting diode (OLED) applications. We demonstrate and validate a goal-directed generative machine learning framework based on a recurrent neural network (RNN) deep reinforcement learning approach for the design of hole transporting OLED materials. These large-scale molecular simulations also demonstrate a rapid, cost-effective method to identify new materials in OLEDs while also enabling expansion into many other verticals such as catalyst design, aerospace, life science, and petrochemicals.

Active Learning Accelerates Design and Optimization of Hole-Transporting Materials for Organic Electronics.

Data-driven methods are receiving increasing attention to accelerate materials design and discovery for organic light-emitting diodes (OLEDs). Machine learning (ML) has enabled high-throughput screening of materials properties to suggest new candidates for organic electronics. However, building reliable predictive ML models requires creating and managing a high volume of data that adequately address the complexity of materials' chemical space. In this regard, active learning (AL) has emerged as a powerful strategy to efficiently navigate the search space by prioritizing the decision-making process for unexplored data. This approach allows a more systematic mechanism to identify promising candidates by minimizing the number of computations required to explore an extensive materials library with diverse variables and parameters. In this paper, we applied a workflow of AL that accounts for multiple optoelectronic parameters to identify materials candidates for hole-transport layers (HTL) in OLEDs. Results of this work pave the way for efficient screening of materials for organic electronics with superior efficiencies before laborious simulations, synthesis, and device fabrication.

Molecular Design Based on Donor-Weak Donor Scaffold for Blue Thermally-Activated Delayed Fluorescence Designed by Combinatorial DFT Calculations.

Quantum chemical calculations are necessary to develop advanced emitter materials showing thermally-activated delayed fluorescence (TADF) for organic light-emitting diodes (OLEDs). However, calculation costs become problematic when more accurate functionals were used, therefore it is judicious to use a multimethod approach for efficiency. Here we employed combinatorial chemistry in silico to develop the deep blue TADF materials with a new concept of homo-junction design. The homo-junction materials containing TADF candidates designed by calculation were synthesized and analyzed. We found that these materials showed the emission from charge transfer (CT) state, and the clear delayed emission was provided in solid state. Because the homo-junction TADF materials showed three exponential decayed emission in solid state, we employed novel four-state kinetic analysis.

Massive Theoretical Screen of Hole Conducting Organic Materials in the Heteroacene Family by Using a Cloud-Computing Environment.

Materials exhibiting higher mobilities than conventional organic semiconducting materials such as fullerenes and fused thiophenes are in high demand for applications in printed electronics. To discover new molecules in the heteroacene family that might show improved charge mobility, a massive theoretical screen of hole conducting properties of molecules was performed by using a cloud-computing environment. Over 7 000 000 structures of fused furans, thiophenes and selenophenes were generated and 250 000 structures were randomly selected to perform density functional theory (DFT) calculations of hole reorganization energies. The lowest hole reorganization energy calculated was 0.0548 eV for a fused thioacene having 8 aromatics rings. Hole mobilities of compounds with the lowest 130 reorganization energy were further processed by applying combined DFT and molecular dynamics (MD) methods. The highest mobility calculated was 1.02 and 9.65 cm2/(V s) based on percolation and disorder theory, respectively, for compounds containing selenium atoms with 8 aromatic rings. These values are about 20 times higher than those for dinaphthothienothiophene (DNTT).

Multifaceted peptide assisted one-pot synthesis of gold nanoparticles for plectin-1 targeted gemcitabine delivery in pancreatic cancer.

An astute modification of the plectin-1-targeting peptide KTLLPTP by introducing a C-terminal cysteine preceded by a tyrosine residue imparted a reducing property to the peptide. This novel property is then exploited to fabricate gold nanoparticles (GNP) via an in situ reduction of gold(iii) chloride in a one-pot, green synthesis. The modified peptide KTLLPTPYC also acts as a template to generate highly monodispersed, spherical GNPs with a narrow size distribution and improved stability. Plectin-1 is known to be aberrantly expressed in the surface of pancreatic ductal adenocarcinoma (PDAC) cells while showing cytoplasmic expression in normal cells. The synthesized GNPs are thus in situ surface modified with the peptides via the cysteine residue leaving the N-terminal KTLLPTP sequence free for targeting plectin-1. The visual molecular dynamics based simulations support the experimental observations like particle size, gemcitabine conjugation and architecture of the peptide-grafted nanoassembly. Additionally, GNPs conjugated to gemcitabine demonstrate significantly higher cytotoxicity in vitro in two established PDAC cell lines (AsPC-1 and PANC-1) and an admirable in vivo antitumor efficacy in a PANC-1 orthotopic xenograft model through selective uptake in PDAC tumor tissues. Altogether, this strategy represents a unique method for the fabrication of a GNP based targeted drug delivery platform using a multifaceted peptide that acts as reducing agent, template for GNP synthesis and targeting agent to display remarkable selectivity towards PDAC.