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Polymorphism in molecular crystals influences their properties and performance. Crystal structure prediction (CSP) can help explore the crystal structure landscape and discover potentially stable polymorphs computationally. We present a new version of the Genarris open-source code, which generates random molecular crystal structures in all space groups and applies physical constraints on intermolecular distances. The main new feature in Genarris 3.0 is the ``Rigid Press algorithm, which uses a regularized hard-sphere potential to compress the unit cell and achieve a maximally close-packed structure based on purely geometric considerations without performing any energy evaluations. In addition, Genarris 3.0 is interfaced with machine-learned interatomic potentials (MLIPs) to accelerate the exploration of the potential energy landscape. We present a new clustering and down-selection workflow that employs the MACE-OFF23(L) MLIPs to perform geometry optimization and energy ranking in the early stages. We use Genarris 3.0 to successfully predict the structure of six targets: aspirin, Target I and Target XXII from previous CSP blind tests, and the energetic materials HMX, CL-20, and DNI. We further analyze the performance of MACE-OFF23(L) compared to dispersion-inclusive density functional theory (DFT) for geometry relaxation and energy ranking. We find significant variability in the performance of MACE-OFF23(L) across chemically diverse targets with particularly poor performance for energetic materials, which is mitigated by our clustering and down-selection procedure. Genarris 3.0 can thus be used effectively to perform CSP and to generate molecular crystal datasets for training ML models. 

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. 

Identifying thermodynamically stable crystal structures remains a key challenge in materials chemistry. Computational crystal structure prediction (CSP) workflows typically rank candidate structures by lattice energy to assess relative stability. Approaches using self-consistent first-principles calculations become prohibitively expensive, especially when millions of energy evaluations are required for complex molecular systems with many atoms per unit cell. Here, we provide a detailed analysis of our methodology and results from the seventh blind test of crystal structure prediction organized by the Cambridge Crystallographic Data Centre (CCDC). We present an approach that significantly accelerates CSP by training target-specific machine learned interatomic potentials (MLIPs). AIMNet2 MLIPs are trained on density functional theory (DFT) calculations of molecular clusters, herein referred to as n-mers. We demonstrate that potentials trained on gas phase dispersion-corrected DFT reference data of n-mers successfully extend to crystalline environments, accurately characterizing the CSP landscape and correctly ranking structures by relative stability. Our methodology effectively captures the underlying physics of thermodynamic crystal stability using only molecular cluster data, avoiding the need for expensive periodic calculations. The performance of target-specific AIMNet2 interatomic potentials is illustrated across diverse chemical systems relevant to pharmaceutical, optoelectronic, and agrochemical applications, demonstrating their promise as efficient alternatives to full DFT calculations for routine CSP tasks. 

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. 

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. 

In organic light-emitting diodes (OLEDs), only 25% of electrically generated excitons are in a singlet state, S1, and the remaining 75% are in a triplet state, T1. In thermally activated delayed fluorescence (TADF) chromophores the transition from the nonradiative T1 state to the radiative S1 state can be thermally activated, which improves the efficiency of OLEDs. Chromophores with inverted energy ordering of S1 and T1 states, S1 < T1, are superior to TADF chromophores, thanks to the absence of an energy barrier for the transition from T1 to S1. We benchmark the performance of time-dependent density functional theory using different exchange-correlation functionals and find that scaled long-range corrected double-hybrid functionals correctly predict the inverted singlet–triplet gaps of N-substituted phenalene derivatives. We then show that the inverted energy ordering of S1 and T1 is an intrinsic property of graphitic carbon nitride flakes. A design strategy of new chromophores with inverted singlet–triplet gaps is proposed. The color of emitted light can be fine-tuned through flake size and amine substitution on flake vertices. 

Thermally activated delayed fluorescence (TADF) is the internal conversion of triplet excitons into singlet excitons via reverse intersystem crossing (RISC). It improves the efficiency of OLEDs by enabling the harvesting of nonradiative triplet excitons. Multiple resonance (MR) induced TADF chromophores exhibit an additional advantage of high color purity due to their rigid conformation. However, owing to the strict design rules there is a limited number of known MR-TADF chromophores. For applications in full-color high-resolution OLED displays, it is desirable to extend the variety of available chromophores and their color range. We computationally explore the effect of chemical modification on the properties of the MR-TADF chromophore quinolino[3,2,1-de]acridine-5,9-dione (QAD). QAD derivatives are evaluated based on several metrics: The formation energy is associated with the ease of synthesis; The spatial distribution of the frontier orbitals indicates whether a compound remains an MR-TADF chromophore or turns into a donor-acceptor TADF chromophore; The change of the singlet excitation energy compared to the parent compound corresponds to the change in color; The energy difference between the lowest singlet and triplet states corresponds to the barrier to RISC; The reorganization energy is associated with the color purity. Based on these metrics, QAD-6CN is predicted to be a promising MR-TADF chromophore with a cyan hue. This demonstrates that computer simulations may aid the design of new MR-TADF chromophores by chemical modification.