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Abstract The excited-state properties of molecular crystals are important for applications in organic electronic devices. TheGWapproximation and Bethe-Salpeter equation (GW+BSE) is the state-of-the-art method for calculating the excited-state properties of crystalline solids with periodic boundary conditions. We present the PAH101 dataset ofGW+BSE calculations for 101 molecular crystals of polycyclic aromatic hydrocarbons (PAHs) with up to  ~500 atoms in the unit cell. To the best of our knowledge, this is the firstGW+BSE dataset for molecular crystals. The data records include theGWquasiparticle band structure, the fundamental band gap, the static dielectric constant, the first singlet exciton energy (optical gap), the first triplet exciton energy, the dielectric function, and optical absorption spectra for light polarized along the three lattice vectors. The dataset can be used to (i) discover materials with desired electronic/optical properties, (ii) identify correlations between DFT andGW+BSE quantities, and (iii) train machine learned models to help in materials discovery efforts. 

Excited-state properties of crystalline organic semiconductors are key to organic electronic device applications. Machine learning (ML) models capable of predicting these properties could significantly accelerate materials discovery. We use the sure-independence-screening-and-sparsifying-operator (SISSO) ML algorithm to generate models to predict the first singlet excitation energy, which corresponds to the optical gap, the first triplet excitation energy, the singlet–triplet gap, and the singlet exciton binding energy of organic molecular crystals. To train the models we use the “PAH101” dataset of many-body perturbation theory calculations within the GW approximation and Bethe–Salpeter equation (GW+BSE) for 101 crystals of polycyclic aromatic hydrocarbons (PAHs). The best performing SISSO models yield predictions within about 0.2 eV of the GW+BSE reference values. SISSO models are selected based on considerations of accuracy and computational cost to construct materials screening workflows for each property. The screening targets are chosen to demonstrate typical use-cases relevant for organic electronic devices. We show that the workflows based on SISSO models can effectively screen out most of the materials that are not of interest and significantly reduce the number of candidates selected for further evaluation using computationally expensive excited-state theory. 

The rapid development and large body of literature on machine learning interatomic potentials (MLIPs) can make it difficult to know how to proceed for researchers who are not experts but wish to use these tools. The spirit of this review is to help such researchers by serving as a practical, accessible guide to the state-of-the-art in MLIPs. This review paper covers a broad range of topics related to MLIPs, including (i) central aspects of how and why MLIPs are enablers of many exciting advancements in molecular modeling, (ii) the main underpinnings of different types of MLIPs, including their basic structure and formalism, (iii) the potentially transformative impact of universal MLIPs for both organic and inorganic systems, including an overview of the most recent advances, capabilities, downsides, and potential applications of this nascent class of MLIPs, (iv) a practical guide for estimating and understanding the execution speed of MLIPs, including guidance for users based on hardware availability, type of MLIP used, and prospective simulation size and time, (v) a manual for what MLIP a user should choose for a given application by considering hardware resources, speed requirements, energy and force accuracy requirements, as well as guidance for choosing pre-trained potentials or fitting a new potential from scratch, (vi) discussion around MLIP infrastructure, including sources of training data, pre-trained potentials, and hardware resources for training, (vii) summary of some key limitations of present MLIPs and current approaches to mitigate such limitations, including methods of including long-range interactions, handling magnetic systems, and treatment of excited states, and finally (viii) we finish with some more speculative thoughts on what the future holds for the development and application of MLIPs over the next 3–10+ years. 

The excited-state properties of molecular crystals are important for applications in organic electronic devices. The GW approximation and Bethe-Salpeter equation (GW+BSE) is the state-of-the-art method for calculating the excited-state properties of crystalline solids with periodic boundary conditions. We present the PAH101 dataset of GW +BSE calculations for 101 molecular crystals of polycyclic aromatic hydrocarbons (PAHs) with up to ∼500 atoms in the unit cell. The data records include the GW quasiparticle band structure, the fundamental band gap, the static dielectric constant, the first singlet exciton energy (optical gap), the first triplet exciton energy, the dielectric function, and optical absorption spectra for light polarized along the three lattice vectors. In addition, the dataset includes the density functional theory (DFT) single-molecule and crystal features used in Liu et al. [npj Computational Materials, 8, 70 (2022)]. We envision the dataset being used to (i) identify correlations between DFT and GW +BSE quantities, (ii) discover materials with desired electronic/ optical properties in the dataset itself, and (iii) train machine-learned models to help in materials discovery efforts. We provide examples to illustrate these three use cases. 

The excited-state properties of molecular crystals are important for applications in organic electronic devices. The GW approximation and Bethe-Salpeter equation (GW +BSE) is the state-of-the-art method for calculating the excited-state properties of crystalline solids with periodic boundary conditions. We present the PAH101 dataset of GW +BSE calculations for 101 molecular crystals of polycyclic aromatic hydrocarbons (PAHs) with up to ∼500 atoms in the unit cell. The data records include the GW quasiparticle band structure, the fundamental band gap, the static dielectric constant, the first singlet exciton energy (optical gap), the first triplet exciton energy, the dielectric function, and optical absorption spectra for light polarized along the three lattice vectors. In addition, the dataset includes the density functional theory (DFT) single-molecule and crystal features used in Liu et al. [npj Computational Materials, 8, 70 (2022)]. We envision the dataset being used to (i) identify correlations between DFT and GW +BSE quantities, (ii) discover materials with desired electronic/ optical properties in the dataset itself, and (iii) train machine-learned models to help in materials discovery efforts. We provide examples to illustrate these three use cases. 

Integrating second order nonlinear (χ(2)) optical materials on chip is an ongoing challenge for Si photonics. Noncentrosymmetric molecular crystals have the potential to deliver high χ(2) nonlinearity with good thermal stability, but so far have been limited to growth from solution or the melt, which are both difficult to control and scale up in manufacturing. Here, we show that large (>100 μm) single crystal domains of the nonlinear molecule 2-[3-(4-hydroxystyryl)-5,5-dimethylcyclohex-2-enylidene] malononitrile (OH1) can be grown monolithically on either glass or Si via vacuum evaporation, followed by a short thermal annealing step. The crystallites are tens of nanometer thick and exhibit strong second harmonic generation with their primary χ(2) tensor component lying predominantly in plane. Remarkably, we find that a single domain can grow uninterrupted through nearby channels etched on a Si wafer, which may provide a path to integrate OH1 on Si or Si3N4 waveguides for a broad range of χ(2)-based photonic integrated circuit functionality.