A polymer library enables the rapid identification of a highly scalable and efficient donor material for organic solar cells.

The dramatic improvement of the PCE (power conversion efficiency) of organic photovoltaic devices in the past few years has been driven by the development of new polymer donor materials and non-fullerene acceptors (NFAs). In the design of such materials synthetic scalability is often not considered, and hence complicated synthetic protocols are typical for high-performing materials. Here we report an approach to readily introduce a variety of solubilizing groups into a benzo[c][1,2,5]thiadiazole acceptor comonomer. This allowed for the ready preparation of a library of eleven donor polymers of varying side chains and comonomers, which facilitated a rapid screening of properties and photovoltaic device performance. Donor FO6-T emerged as the optimal material, exhibiting good solubility in chlorinated and non-chlorinated solvents and achieving 15.4% PCE with L8BO as the acceptor (15.2% with Y6) and good device stability. FO6-T was readily prepared on the gram scale, and synthetic complexity (SC) analysis highlighted FO6-T as an attractive donor polymer for potential large scale applications.

Biorenewable Solvents for High-Performance Organic Solar Cells.

With the advent of nonfullerene acceptors (NFAs), organic photovoltaic (OPV) devices are now achieving high enough power conversion efficiencies (PCEs) for commercialization. However, these high performances rely on active layers processed from petroleum-based and toxic solvents, which are undesirable for mass manufacturing. Here, we demonstrate the use of biorenewable 2-methyltetrahydrofuran (2MeTHF) and cyclopentyl methyl ether (CPME) solvents to process donor: NFA-based OPVs with no additional additives in the active layer. Furthermore, to reduce the overall carbon footprint of the manufacturing cycle of the OPVs, we use polymeric donors that require a few synthetic steps for their synthesis, namely, PTQ10 and FO6-T, which are blended with the Y-series NFA Y12. High performance was achieved using 2MeTHF as the processing solvent, reaching PCEs of 14.5% and 11.4% for PTQ10:Y12 and FO6-T:Y12 blends, respectively. This work demonstrates the potential of using biorenewable solvents without additives for the processing of OPV active layers, opening the door to large-scale and green manufacturing of organic solar cells.

Enhanced sub-1 eV detection in organic photodetectors through tuning polymer energetics and microstructure.

One of the key challenges facing organic photodiodes (OPDs) is increasing the detection into the infrared region. Organic semiconductor polymers provide a platform for tuning the bandgap and optoelectronic response to go beyond the traditional 1000-nanometer benchmark. In this work, we present a near-infrared (NIR) polymer with absorption up to 1500 nanometers. The polymer-based OPD delivers a high specific detectivity D* of 1.03 × 1010 Jones (-2 volts) at 1200 nanometers and a dark current Jd of just 2.3 × 10-6 ampere per square centimeter at -2 volts. We demonstrate a strong improvement of all OPD metrics in the NIR region compared to previously reported NIR OPD due to the enhanced crystallinity and optimized energy alignment, which leads to reduced charge recombination. The high D* value in the 1100-to-1300-nanometer region is particularly promising for biosensing applications. We demonstrate the OPD as a pulse oximeter under NIR illumination, delivering heart rate and blood oxygen saturation readings in real time without signal amplification.

#COVIDisAirborne: AI-enabled multiscale computational microscopy of delta SARS-CoV-2 in a respiratory aerosol.

We seek to completely revise current models of airborne transmission of respiratory viruses by providing never-before-seen atomic-level views of the SARS-CoV-2 virus within a respiratory aerosol. Our work dramatically extends the capabilities of multiscale computational microscopy to address the significant gaps that exist in current experimental methods, which are limited in their ability to interrogate aerosols at the atomic/molecular level and thus obscure our understanding of airborne transmission. We demonstrate how our integrated data-driven platform provides a new way of exploring the composition, structure, and dynamics of aerosols and aerosolized viruses, while driving simulation method development along several important axes. We present a series of initial scientific discoveries for the SARS-CoV-2 Delta variant, noting that the full scientific impact of this work has yet to be realized.

Low Dark Current Organic Photodetectors Utilizing Highly Cyanated Non-fullerene Acceptors.

Organic materials combining high electron affinity with strong absorption in the visible spectrum are of interest for photodetector applications. In this study, we report two such molecular semiconductors, based upon an acceptor-donor-acceptor (A-D-A) approach. Coupling of an acceptor end group, 2,1,3-benzothiadiazole-4,5,6-tricarbonitrile (TCNBT), with a donor cyclopentadithiophene core affords materials with a band gap of 1.5 eV and low-lying LUMO levels around -4.2 eV. Both materials were readily synthesized by a one-pot nucleophilic displacement of a fluorinated precursor by cyanide. The two acceptors only differ in the nature of the solubilizing alkyl chain, which is either branched 2-ethyl hexyl (EH-TCNBT) or linear octyl (O-TCNBT). Both acceptors were blended with polymer donor PTQ10 as an active layer in OPDs. Significant device differences were observed depending on the alkyl chain, with the branched acceptor giving the optimum performance. Both acceptors exhibited very low dark current densities, with values up to 10-5 mA cm-2 at -2 V, highlighting the potential of the highly cyanated cores (TCNBT) as acceptor materials.

Informing geometric deep learning with electronic interactions to accelerate quantum chemistry.

Predicting electronic energies, densities, and related chemical properties can facilitate the discovery of novel catalysts, medicines, and battery materials. However, existing machine learning techniques are challenged by the scarcity of training data when exploring unknown chemical spaces. We overcome this barrier by systematically incorporating knowledge of molecular electronic structure into deep learning. By developing a physics-inspired equivariant neural network, we introduce a method to learn molecular representations based on the electronic interactions among atomic orbitals. Our method, OrbNet-Equi, leverages efficient tight-binding simulations and learned mappings to recover high-fidelity physical quantities. OrbNet-Equi accurately models a wide spectrum of target properties while being several orders of magnitude faster than density functional theory. Despite only using training samples collected from readily available small-molecule libraries, OrbNet-Equi outperforms traditional semiempirical and machine learning-based methods on comprehensive downstream benchmarks that encompass diverse main-group chemical processes. Our method also describes interactions in challenging charge-transfer complexes and open-shell systems. We anticipate that the strategy presented here will help to expand opportunities for studies in chemistry and materials science, where the acquisition of experimental or reference training data is costly.

Infrared Organic Photodetectors Employing Ultralow Bandgap Polymer and Non-Fullerene Acceptors for Biometric Monitoring.

Recent efforts in the field of organic photodetectors (OPD) have been focused on extending broadband detection into the near-infrared (NIR) region. Here, two blends of an ultralow bandgap push-pull polymer TQ-T combined with state-of-the-art non-fullerene acceptors, IEICO-4F and Y6, are compared to obtain OPDs for sensing in the NIR beyond 1100 nm, which is the cut off for benchmark Si photodiodes. It is observed that the TQ-T:IEICO-4F device has a superior IR responsivity (0.03 AW-1 at 1200 nm and -2 V bias) and can detect infrared light up to 1800 nm, while the TQ-T:Y6 blend shows a lower responsivity of 0.01 AW-1 . Device physics analyses are tied with spectroscopic and morphological studies to link the superior performance of TQ-T:IEICO-4F OPD to its faster charge separation as well as more favorable donor-acceptor domains mixing. In the polymer blend with Y6, the formation of large agglomerates that exceed the exciton diffusion length, which leads to high charge recombination, is observed. An application of these devices as biometric sensors for real-time heart rate monitoring via photoplethysmography, utilizing infrared light, is demonstrated.

OrbNet Denali: A machine learning potential for biological and organic chemistry with semi-empirical cost and DFT accuracy.

We present OrbNet Denali, a machine learning model for an electronic structure that is designed as a drop-in replacement for ground-state density functional theory (DFT) energy calculations. The model is a message-passing graph neural network that uses symmetry-adapted atomic orbital features from a low-cost quantum calculation to predict the energy of a molecule. OrbNet Denali is trained on a vast dataset of 2.3 × 106 DFT calculations on molecules and geometries. This dataset covers the most common elements in biochemistry and organic chemistry (H, Li, B, C, N, O, F, Na, Mg, Si, P, S, Cl, K, Ca, Br, and I) and charged molecules. OrbNet Denali is demonstrated on several well-established benchmark datasets, and we find that it provides accuracy that is on par with modern DFT methods while offering a speedup of up to three orders of magnitude. For the GMTKN55 benchmark set, OrbNet Denali achieves WTMAD-1 and WTMAD-2 scores of 7.19 and 9.84, on par with modern DFT functionals. For several GMTKN55 subsets, which contain chemical problems that are not present in the training set, OrbNet Denali produces a mean absolute error comparable to those of DFT methods. For the Hutchison conformer benchmark set, OrbNet Denali has a median correlation coefficient of R2 = 0.90 compared to the reference DLPNO-CCSD(T) calculation and R2 = 0.97 compared to the method used to generate the training data (ωB97X-D3/def2-TZVP), exceeding the performance of any other method with a similar cost. Similarly, the model reaches chemical accuracy for non-covalent interactions in the S66x10 dataset. For torsional profiles, OrbNet Denali reproduces the torsion profiles of ωB97X-D3/def2-TZVP with an average mean absolute error of 0.12 kcal/mol for the potential energy surfaces of the diverse fragments in the TorsionNet500 dataset.

#COVIDisAirborne: AI-Enabled Multiscale Computational Microscopy of Delta SARS-CoV-2 in a Respiratory Aerosol.

We seek to completely revise current models of airborne transmission of respiratory viruses by providing never-before-seen atomic-level views of the SARS-CoV-2 virus within a respiratory aerosol. Our work dramatically extends the capabilities of multiscale computational microscopy to address the significant gaps that exist in current experimental methods, which are limited in their ability to interrogate aerosols at the atomic/molecular level and thus ob-scure our understanding of airborne transmission. We demonstrate how our integrated data-driven platform provides a new way of exploring the composition, structure, and dynamics of aerosols and aerosolized viruses, while driving simulation method development along several important axes. We present a series of initial scientific discoveries for the SARS-CoV-2 Delta variant, noting that the full scientific impact of this work has yet to be realized. ACM REFERENCE FORMAT: Abigail Dommer 1† , Lorenzo Casalino 1† , Fiona Kearns 1† , Mia Rosenfeld 1 , Nicholas Wauer 1 , Surl-Hee Ahn 1 , John Russo, 2 Sofia Oliveira 3 , Clare Morris 1 , AnthonyBogetti 4 , AndaTrifan 5,6 , Alexander Brace 5,7 , TerraSztain 1,8 , Austin Clyde 5,7 , Heng Ma 5 , Chakra Chennubhotla 4 , Hyungro Lee 9 , Matteo Turilli 9 , Syma Khalid 10 , Teresa Tamayo-Mendoza 11 , Matthew Welborn 11 , Anders Christensen 11 , Daniel G. A. Smith 11 , Zhuoran Qiao 12 , Sai Krishna Sirumalla 11 , Michael O'Connor 11 , Frederick Manby 11 , Anima Anandkumar 12,13 , David Hardy 6 , James Phillips 6 , Abraham Stern 13 , Josh Romero 13 , David Clark 13 , Mitchell Dorrell 14 , Tom Maiden 14 , Lei Huang 15 , John McCalpin 15 , Christo- pherWoods 3 , Alan Gray 13 , MattWilliams 3 , Bryan Barker 16 , HarindaRajapaksha 16 , Richard Pitts 16 , Tom Gibbs 13 , John Stone 6 , Daniel Zuckerman 2 *, Adrian Mulholland 3 *, Thomas MillerIII 11,12 *, ShantenuJha 9 *, Arvind Ramanathan 5 *, Lillian Chong 4 *, Rommie Amaro 1 *. 2021. #COVIDisAirborne: AI-Enabled Multiscale Computational Microscopy ofDeltaSARS-CoV-2 in a Respiratory Aerosol. In Supercomputing '21: International Conference for High Perfor-mance Computing, Networking, Storage, and Analysis . ACM, New York, NY, USA, 14 pages. https://doi.org/finalDOI.

OrbNet: Deep learning for quantum chemistry using symmetry-adapted atomic-orbital features.

We introduce a machine learning method in which energy solutions from the Schrödinger equation are predicted using symmetry adapted atomic orbital features and a graph neural-network architecture. OrbNet is shown to outperform existing methods in terms of learning efficiency and transferability for the prediction of density functional theory results while employing low-cost features that are obtained from semi-empirical electronic structure calculations. For applications to datasets of drug-like molecules, including QM7b-T, QM9, GDB-13-T, DrugBank, and the conformer benchmark dataset of Folmsbee and Hutchison [Int. J. Quantum Chem. (published online) (2020)], OrbNet predicts energies within chemical accuracy of density functional theory at a computational cost that is 1000-fold or more reduced.

Ice Nucleation of Confined Monolayer Water Conforms to Classical Nucleation Theory.

We confirmed that monolayer water confined by parallel graphene sheets spontaneously crystallizes from a structurally and dynamically heterogeneous liquid phase under moderate supercooling via direct molecular dynamics simulation. Square-lattice-like geometric order is observed at the early stage of nucleation and is preserved during the entire nucleus growth process. The diffusion coefficient and free energy profile in the cluster space extracted from a Bayesian trajectory analysis agree well with the classical nucleation theory (CNT) prediction and yield thermodynamic quantities exhibiting linear temperature dependence. The effectiveness of maximum cluster size as the descriptor of ice nucleation dynamics in the CNT framework can be attributed to the dynamical time scale decoupling and strong structural pattern dependence of density fluctuation in the liquid phase.

Structure of water confined between two parallel graphene plates.

We study, in this paper, the physical properties of water confined between two parallel graphene plates with different slit widths to understand the effects of confinement on the water structure and how bulk properties are reached as the water layer thickens. It was found that the microscopic structures of the interfacial liquid layer close to graphene vary with the slit width. Water tends to locate at the center of the six-membered ring of graphene planes to form triangular patterns, as found by others. The narrower the slit width is, the more pronounced this pattern is, except for the slit width of 9.5 Å, for which a well-defined two-layer structure of water forms. On the other hand, squared structures can be clearly seen in single snapshots at small (6.5 Å and 7.5 Å) but not large slit widths. Even at small slit widths, the square-like geometry is observed only when an average is taken for a short trajectory, and averaging over a long time yields a triangular pattern dictated by the graphene geometry. We estimate the length of time needed to observe two patterns, respectively. We also used the two-phase thermodynamic model to study the variation of entropy of confined water and found that at 8.5 Å, the entropy of confined water is larger than that of bulk water. The rotational entropy of confined water is higher than that of bulk water for all slit widths due to the reduction of the hydrogen bond in the confined space.