@inproceedings{14168, abstract = {Recent work has seen the development of general purpose neural architectures that can be trained to perform tasks across diverse data modalities. General purpose models typically make few assumptions about the underlying data-structure and are known to perform well in the large-data regime. At the same time, there has been growing interest in modular neural architectures that represent the data using sparsely interacting modules. These models can be more robust out-of-distribution, computationally efficient, and capable of sample-efficient adaptation to new data. However, they tend to make domain-specific assumptions about the data, and present challenges in how module behavior (i.e., parameterization) and connectivity (i.e., their layout) can be jointly learned. In this work, we introduce a general purpose, yet modular neural architecture called Neural Attentive Circuits (NACs) that jointly learns the parameterization and a sparse connectivity of neural modules without using domain knowledge. NACs are best understood as the combination of two systems that are jointly trained end-to-end: one that determines the module configuration and the other that executes it on an input. We demonstrate qualitatively that NACs learn diverse and meaningful module configurations on the NLVR2 dataset without additional supervision. Quantitatively, we show that by incorporating modularity in this way, NACs improve upon a strong non-modular baseline in terms of low-shot adaptation on CIFAR and CUBs dataset by about 10%, and OOD robustness on Tiny ImageNet-R by about 2.5%. Further, we find that NACs can achieve an 8x speedup at inference time while losing less than 3% performance. Finally, we find NACs to yield competitive results on diverse data modalities spanning point-cloud classification, symbolic processing and text-classification from ASCII bytes, thereby confirming its general purpose nature.}, author = {Rahaman, Nasim and Weiss, Martin and Locatello, Francesco and Pal, Chris and Bengio, Yoshua and Schölkopf, Bernhard and Li, Li Erran and Ballas, Nicolas}, booktitle = {36th Conference on Neural Information Processing Systems}, location = {New Orleans, United States}, title = {{Neural attentive circuits}}, volume = {35}, year = {2022}, } @inproceedings{14170, abstract = {The idea behind object-centric representation learning is that natural scenes can better be modeled as compositions of objects and their relations as opposed to distributed representations. This inductive bias can be injected into neural networks to potentially improve systematic generalization and performance of downstream tasks in scenes with multiple objects. In this paper, we train state-of-the-art unsupervised models on five common multi-object datasets and evaluate segmentation metrics and downstream object property prediction. In addition, we study generalization and robustness by investigating the settings where either a single object is out of distribution -- e.g., having an unseen color, texture, or shape -- or global properties of the scene are altered -- e.g., by occlusions, cropping, or increasing the number of objects. From our experimental study, we find object-centric representations to be useful for downstream tasks and generally robust to most distribution shifts affecting objects. However, when the distribution shift affects the input in a less structured manner, robustness in terms of segmentation and downstream task performance may vary significantly across models and distribution shifts. }, author = {Dittadi, Andrea and Papa, Samuele and Vita, Michele De and Schölkopf, Bernhard and Winther, Ole and Locatello, Francesco}, booktitle = {Proceedings of the 39th International Conference on Machine Learning}, location = {Baltimore, MD, United States}, pages = {5221--5285}, publisher = {ML Research Press}, title = {{Generalization and robustness implications in object-centric learning}}, volume = {2022}, year = {2022}, } @inproceedings{14171, abstract = {This paper demonstrates how to recover causal graphs from the score of the data distribution in non-linear additive (Gaussian) noise models. Using score matching algorithms as a building block, we show how to design a new generation of scalable causal discovery methods. To showcase our approach, we also propose a new efficient method for approximating the score's Jacobian, enabling to recover the causal graph. Empirically, we find that the new algorithm, called SCORE, is competitive with state-of-the-art causal discovery methods while being significantly faster.}, author = {Rolland, Paul and Cevher, Volkan and Kleindessner, Matthäus and Russel, Chris and Schölkopf, Bernhard and Janzing, Dominik and Locatello, Francesco}, booktitle = {Proceedings of the 39th International Conference on Machine Learning}, location = {Baltimore, MD, United States}, pages = {18741--18753}, publisher = {ML Research Press}, title = {{Score matching enables causal discovery of nonlinear additive noise models}}, volume = {162}, year = {2022}, } @inproceedings{14172, abstract = {An important component for generalization in machine learning is to uncover underlying latent factors of variation as well as the mechanism through which each factor acts in the world. In this paper, we test whether 17 unsupervised, weakly supervised, and fully supervised representation learning approaches correctly infer the generative factors of variation in simple datasets (dSprites, Shapes3D, MPI3D) from controlled environments, and on our contributed CelebGlow dataset. In contrast to prior robustness work that introduces novel factors of variation during test time, such as blur or other (un)structured noise, we here recompose, interpolate, or extrapolate only existing factors of variation from the training data set (e.g., small and medium-sized objects during training and large objects during testing). Models that learn the correct mechanism should be able to generalize to this benchmark. In total, we train and test 2000+ models and observe that all of them struggle to learn the underlying mechanism regardless of supervision signal and architectural bias. Moreover, the generalization capabilities of all tested models drop significantly as we move from artificial datasets towards more realistic real-world datasets. Despite their inability to identify the correct mechanism, the models are quite modular as their ability to infer other in-distribution factors remains fairly stable, providing only a single factoris out-of-distribution. These results point to an important yet understudied problem of learning mechanistic models of observations that can facilitate generalization.}, author = {Schott, Lukas and Kügelgen, Julius von and Träuble, Frederik and Gehler, Peter and Russell, Chris and Bethge, Matthias and Schölkopf, Bernhard and Locatello, Francesco and Brendel, Wieland}, booktitle = {10th International Conference on Learning Representations}, location = {Virtual}, title = {{Visual representation learning does not generalize strongly within the same domain}}, year = {2022}, } @inproceedings{14173, abstract = {Since out-of-distribution generalization is a generally ill-posed problem, various proxy targets (e.g., calibration, adversarial robustness, algorithmic corruptions, invariance across shifts) were studied across different research programs resulting in different recommendations. While sharing the same aspirational goal, these approaches have never been tested under the same experimental conditions on real data. In this paper, we take a unified view of previous work, highlighting message discrepancies that we address empirically, and providing recommendations on how to measure the robustness of a model and how to improve it. To this end, we collect 172 publicly available dataset pairs for training and out-of-distribution evaluation of accuracy, calibration error, adversarial attacks, environment invariance, and synthetic corruptions. We fine-tune over 31k networks, from nine different architectures in the many- and few-shot setting. Our findings confirm that in- and out-of-distribution accuracies tend to increase jointly, but show that their relation is largely dataset-dependent, and in general more nuanced and more complex than posited by previous, smaller scale studies.}, author = {Wenzel, Florian and Dittadi, Andrea and Gehler, Peter Vincent and Carl-Johann Simon-Gabriel, Carl-Johann Simon-Gabriel and Horn, Max and Zietlow, Dominik and Kernert, David and Russell, Chris and Brox, Thomas and Schiele, Bernt and Schölkopf, Bernhard and Locatello, Francesco}, booktitle = {36th Conference on Neural Information Processing Systems}, isbn = {9781713871088}, location = {New Orleans, LA, United States}, pages = {7181--7198}, publisher = {Neural Information Processing Systems Foundation}, title = {{Assaying out-of-distribution generalization in transfer learning}}, volume = {35}, year = {2022}, } @inproceedings{14174, abstract = {Building sample-efficient agents that generalize out-of-distribution (OOD) in real-world settings remains a fundamental unsolved problem on the path towards achieving higher-level cognition. One particularly promising approach is to begin with low-dimensional, pretrained representations of our world, which should facilitate efficient downstream learning and generalization. By training 240 representations and over 10,000 reinforcement learning (RL) policies on a simulated robotic setup, we evaluate to what extent different properties of pretrained VAE-based representations affect the OOD generalization of downstream agents. We observe that many agents are surprisingly robust to realistic distribution shifts, including the challenging sim-to-real case. In addition, we find that the generalization performance of a simple downstream proxy task reliably predicts the generalization performance of our RL agents under a wide range of OOD settings. Such proxy tasks can thus be used to select pretrained representations that will lead to agents that generalize.}, author = {Dittadi, Andrea and Träuble, Frederik and Wüthrich, Manuel and Widmaier, Felix and Gehler, Peter and Winther, Ole and Locatello, Francesco and Bachem, Olivier and Schölkopf, Bernhard and Bauer, Stefan}, booktitle = {10th International Conference on Learning Representations}, location = {Virtual}, title = {{The role of pretrained representations for the OOD generalization of reinforcement learning agents}}, year = {2022}, } @inproceedings{14175, abstract = {Predicting the future trajectory of a moving agent can be easy when the past trajectory continues smoothly but is challenging when complex interactions with other agents are involved. Recent deep learning approaches for trajectory prediction show promising performance and partially attribute this to successful reasoning about agent-agent interactions. However, it remains unclear which features such black-box models actually learn to use for making predictions. This paper proposes a procedure that quantifies the contributions of different cues to model performance based on a variant of Shapley values. Applying this procedure to state-of-the-art trajectory prediction methods on standard benchmark datasets shows that they are, in fact, unable to reason about interactions. Instead, the past trajectory of the target is the only feature used for predicting its future. For a task with richer social interaction patterns, on the other hand, the tested models do pick up such interactions to a certain extent, as quantified by our feature attribution method. We discuss the limits of the proposed method and its links to causality.}, author = {Makansi, Osama and Kügelgen, Julius von and Locatello, Francesco and Gehler, Peter and Janzing, Dominik and Brox, Thomas and Schölkopf, Bernhard}, booktitle = {10th International Conference on Learning Representations}, location = {Virtual}, title = {{You mostly walk alone: Analyzing feature attribution in trajectory prediction}}, year = {2022}, } @inproceedings{14215, abstract = {Geospatial Information Systems are used by researchers and Humanitarian Assistance and Disaster Response (HADR) practitioners to support a wide variety of important applications. However, collaboration between these actors is difficult due to the heterogeneous nature of geospatial data modalities (e.g., multi-spectral images of various resolutions, timeseries, weather data) and diversity of tasks (e.g., regression of human activity indicators or detecting forest fires). In this work, we present a roadmap towards the construction of a general-purpose neural architecture (GPNA) with a geospatial inductive bias, pre-trained on large amounts of unlabelled earth observation data in a self-supervised manner. We envision how such a model may facilitate cooperation between members of the community. We show preliminary results on the first step of the roadmap, where we instantiate an architecture that can process a wide variety of geospatial data modalities and demonstrate that it can achieve competitive performance with domain-specific architectures on tasks relating to the U.N.'s Sustainable Development Goals.}, author = {Rahaman, Nasim and Weiss, Martin and Träuble, Frederik and Locatello, Francesco and Lacoste, Alexandre and Bengio, Yoshua and Pal, Chris and Li, Li Erran and Schölkopf, Bernhard}, booktitle = {36th Conference on Neural Information Processing Systems}, location = {New Orleans, LA, United States}, title = {{A general purpose neural architecture for geospatial systems}}, year = {2022}, } @unpublished{14220, abstract = {Although reinforcement learning has seen remarkable progress over the last years, solving robust dexterous object-manipulation tasks in multi-object settings remains a challenge. In this paper, we focus on models that can learn manipulation tasks in fixed multi-object settings and extrapolate this skill zero-shot without any drop in performance when the number of objects changes. We consider the generic task of bringing a specific cube out of a set to a goal position. We find that previous approaches, which primarily leverage attention and graph neural network-based architectures, do not generalize their skills when the number of input objects changes while scaling as K2. We propose an alternative plug-and-play module based on relational inductive biases to overcome these limitations. Besides exceeding performances in their training environment, we show that our approach, which scales linearly in K, allows agents to extrapolate and generalize zero-shot to any new object number.}, author = {Mambelli, Davide and Träuble, Frederik and Bauer, Stefan and Schölkopf, Bernhard and Locatello, Francesco}, booktitle = {arXiv}, title = {{Compositional multi-object reinforcement learning with linear relation networks}}, doi = {10.48550/arXiv.2201.13388}, year = {2022}, } @unpublished{14236, abstract = {We show an $(1+\epsilon)$-approximation algorithm for maintaining maximum $s$-$t$ flow under $m$ edge insertions in $m^{1/2+o(1)} \epsilon^{-1/2}$ amortized update time for directed, unweighted graphs. This constitutes the first sublinear dynamic maximum flow algorithm in general sparse graphs with arbitrarily good approximation guarantee.}, author = {Goranci, Gramoz and Henzinger, Monika H}, booktitle = {arXiv}, title = {{Incremental approximate maximum flow in m1/2+o(1) update time}}, doi = {10.48550/arXiv.2211.09606}, year = {2022}, } @article{14248, abstract = {Recent work by Forsgård indicates that not every convex lattice polygon arises as the characteristic polygon of an affine dimer or, equivalently, an admissible oriented line arrangement on the torus in general position. We begin the classication of convex lattice polygons arising as characteristic polygons of affine dimers. We present several general constructions of new affine dimers from old, and an algorithm for finding affine dimers with prescribed polygon. With these tools we prove that all lattice triangles, generalised parallelograms, and polygons of genus at most two admit an affine dimer.}, author = {Holmes, Daniel}, issn = {2576-3725}, journal = {PUMP Journal of Undergraduate Research}, keywords = {dimer model, hyperplane arrangement, torus, lattice polygon}, pages = {24--51}, publisher = {California State University}, title = {{Affine dimers from characteristic polygons}}, volume = {5}, year = {2022}, } @article{14282, abstract = {Asymmetric multiprotein complexes that undergo subunit exchange play central roles in biology but present a challenge for design because the components must not only contain interfaces that enable reversible association but also be stable and well behaved in isolation. We use implicit negative design to generate β sheet–mediated heterodimers that can be assembled into a wide variety of complexes. The designs are stable, folded, and soluble in isolation and rapidly assemble upon mixing, and crystal structures are close to the computational models. We construct linearly arranged hetero-oligomers with up to six different components, branched hetero-oligomers, closed C4-symmetric two-component rings, and hetero-oligomers assembled on a cyclic homo-oligomeric central hub and demonstrate that such complexes can readily reconfigure through subunit exchange. Our approach provides a general route to designing asymmetric reconfigurable protein systems.}, author = {Sahtoe, Danny D. and Praetorius, Florian M and Courbet, Alexis and Hsia, Yang and Wicky, Basile I. M. and Edman, Natasha I. and Miller, Lauren M. and Timmermans, Bart J. R. and Decarreau, Justin and Morris, Hana M. and Kang, Alex and Bera, Asim K. and Baker, David}, issn = {1095-9203}, journal = {Science}, number = {6578}, publisher = {American Association for the Advancement of Science}, title = {{Reconfigurable asymmetric protein assemblies through implicit negative design}}, doi = {10.1126/science.abj7662}, volume = {375}, year = {2022}, } @article{14355, abstract = {Purpose: The mediator (MED) multisubunit-complex modulates the activity of the transcriptional machinery, and genetic defects in different MED subunits (17, 20, 27) have been implicated in neurologic diseases. In this study, we identified a recurrent homozygous variant in MED11 (c.325C>T; p.Arg109Ter) in 7 affected individuals from 5 unrelated families. Methods: To investigate the genetic cause of the disease, exome or genome sequencing were performed in 5 unrelated families identified via different research networks and Matchmaker Exchange. Deep clinical and brain imaging evaluations were performed by clinical pediatric neurologists and neuroradiologists. The functional effect of the candidate variant on both MED11 RNA and protein was assessed using reverse transcriptase polymerase chain reaction and western blotting using fibroblast cell lines derived from 1 affected individual and controls and through computational approaches. Knockouts in zebrafish were generated using clustered regularly interspaced short palindromic repeats/Cas9. Results: The disease was characterized by microcephaly, profound neurodevelopmental impairment, exaggerated startle response, myoclonic seizures, progressive widespread neurodegeneration, and premature death. Functional studies on patient-derived fibroblasts did not show a loss of protein function but rather disruption of the C-terminal of MED11, likely impairing binding to other MED subunits. A zebrafish knockout model recapitulates key clinical phenotypes. Conclusion: Loss of the C-terminal of MED subunit 11 may affect its binding efficiency to other MED subunits, thus implicating the MED-complex stability in brain development and neurodegeneration. (C) 2022 The Authors. Published by Elsevier Inc. on behalf of American College of Medical Genetics and Genomics.}, author = {Cali, Elisa and Lin, Sheng-Jia and Rocca, Clarissa and Sahin, Yavuz and Al Shamsi, Aisha and El Chehadeh, Salima and Chaabouni, Myriam and Mankad, Kshitij and Galanaki, Evangelia and Efthymiou, Stephanie and Sudhakar, Sniya and Athanasiou-Fragkouli, Alkyoni and Celik, Tamer and Narli, Nejat and Bianca, Sebastiano and Murphy, David and Moreira, Francisco Martins De Carvalho and Accogli, Andrea and Petree, Cassidy and Huang, Kevin and Monastiri, Kamel and Edizadeh, Masoud and Nardello, Rosaria and Ognibene, Marzia and De Marco, Patrizia and Ruggieri, Martino and Zara, Federico and Striano, Pasquale and Sahin, Yavuz and Al-Gazali, Lihadh and Warde, Marie Therese Abi and Gerard, Benedicte and Zifarelli, Giovanni and Beetz, Christian and Fortuna, Sara and Soler, Miguel and Valente, Enza Maria and Varshney, Gaurav and Maroofian, Reza and Salpietro, Vincenzo and Houlden, Henry and Grp, SYNaPS Study}, issn = {1098-3600}, journal = {Genetics in Medicine}, keywords = {Human mediator complex, MED11, MEDopathies}, number = {10}, pages = {2194--2203}, publisher = {Elsevier}, title = {{A homozygous MED11 C-terminal variant causes a lethal neurodegenerative disease}}, doi = {10.1016/j.gim.2022.07.013}, volume = {24}, year = {2022}, } @article{14356, abstract = {Aminoacyl-tRNA synthetases (ARSs) are essential enzymes for faithful assignment of amino acids to their cognate tRNA. Variants in ARS genes are frequently associated with clinically heterogeneous phenotypes in humans and follow both autosomal dominant or recessive inheritance patterns in many instances. Variants in tryptophanyl-tRNA synthetase 1 (WARS1) cause autosomal dominantly inherited distal hereditary motor neuropathy and Charcot-Marie-Tooth disease. Presently, only one family with biallelic WARS1 variants has been described. We present three affected individuals from two families with biallelic variants (p.Met1? and p.(Asp419Asn)) in WARS1, showing varying severities of developmental delay and intellectual disability. Hearing impairment and microcephaly, as well as abnormalities of the brain, skeletal system, movement/gait, and behavior were variable features. Phenotyping of knocked down wars-1 in a Caenorhabditis elegans model showed depletion is associated with defects in germ cell development. A wars1 knockout vertebrate model recapitulates the human clinical phenotypes, confirms variant pathogenicity, and uncovers evidence implicating the p.Met1? variant as potentially impacting an exon critical for normal hearing. Together, our findings provide consolidating evidence for biallelic disruption of WARS1 as causal for an autosomal recessive neurodevelopmental syndrome and present a vertebrate model that recapitulates key phenotypes observed in patients.}, author = {Lin, Sheng-Jia and Vona, Barbara and Porter, Hillary M. and Izadi, Mahmoud and Huang, Kevin and Lacassie, Yves and Rosenfeld, Jill A. and Khan, Saadullah and Petree, Cassidy and Ali, Tayyiba A. and Muhammad, Nazif and Khan, Sher A. and Muhammad, Noor and Liu, Pengfei and Haymon, Marie-Louise and Rueschendorf, Franz and Kong, Il-Keun and Schnapp, Linda and Shur, Natasha and Chorich, Lynn and Layman, Lawrence and Haaf, Thomas and Pourkarimi, Ehsan and Kim, Hyung-Goo and Varshney, Gaurav K.}, issn = {1059-7794}, journal = {Human Mutation}, keywords = {autosomal recessive, biallelic variants, C, elegans, translation initiation sites, tryptophanyl-tRNA synthetase 1 (WARS1), WHEP domain, zebrafish}, number = {10}, pages = {1472--1489}, publisher = {Wiley}, title = {{Biallelic variants in WARS1 cause a highly variable neurodevelopmental syndrome and implicate a critical exon for normal auditory function}}, doi = {10.1002/humu.24435}, volume = {43}, year = {2022}, } @article{14357, abstract = {Aminoacylation of transfer RNA (tRNA) is a key step in protein biosynthesis, carried out by highly specific aminoacyl-tRNA synthetases (ARSs). ARSs have been implicated in autosomal dominant and autosomal recessive human disorders. Autosomal dominant variants in tryptophanyl-tRNA synthetase 1 (WARS1) are known to cause distal hereditary motor neuropathy and Charcot-Marie-Tooth disease, but a recessively inherited phenotype is yet to be clearly defined. Seryl-tRNA synthetase 1 (SARS1) has rarely been implicated in an autosomal recessive developmental disorder. Here, we report five individuals with biallelic missense variants in WARS1 or SARS1, who presented with an overlapping phenotype of microcephaly, developmental delay, intellectual disability, and brain anomalies. Structural mapping showed that the SARS1 variant is located directly within the enzyme’s active site, most likely diminishing activity, while the WARS1 variant is located in the N-terminal domain. We further characterize the identified WARS1 variant by showing that it negatively impacts protein abundance and is unable to rescue the phenotype of a CRISPR/Cas9 wars1 knockout zebrafish model. In summary, we describe two overlapping autosomal recessive syndromes caused by variants in WARS1 and SARS1, present functional insights into the pathogenesis of the WARS1-related syndrome and define an emerging disease spectrum: ARS-related developmental disorders with or without microcephaly.}, author = {Boegershausen, Nina and Krawczyk, Hannah E. and Jamra, Rami A. and Lin, Sheng-Jia and Yigit, Goekhan and Huening, Irina and Polo, Anna M. and Vona, Barbara and Huang, Kevin and Schmidt, Julia and Altmueller, Janine and Luppe, Johannes and Platzer, Konrad and Doergeloh, Beate B. and Busche, Andreas and Biskup, Saskia and Mendes, I, Marisa and Smith, Desiree E. C. and Salomons, Gajja S. and Zibat, Arne and Bueltmann, Eva and Nuernberg, Peter and Spielmann, Malte and Lemke, Johannes R. and Li, Yun and Zenker, Martin and Varshney, Gaurav K. and Hillen, Hauke S. and Kratz, Christian P. and Wollnik, Bernd}, issn = {1059-7794}, journal = {Human Mutation}, keywords = {aminoacylation, aminoacyl-tRNA synthetase, ARS, CRISPR, Cas9, intellectual disability, microcephaly, SARS1, tRNA, WARS1, zebrafish}, number = {10}, pages = {1454--1471}, publisher = {Wiley}, title = {{WARS1 and SARS1: Two tRNA synthetases implicated in autosomal recessive microcephaly}}, doi = {10.1002/humu.24430}, volume = {43}, year = {2022}, } @article{14381, abstract = {Expander graphs (sparse but highly connected graphs) have, since their inception, been the source of deep links between Mathematics and Computer Science as well as applications to other areas. In recent years, a fascinating theory of high-dimensional expanders has begun to emerge, which is still in a formative stage but has nonetheless already lead to a number of striking results. Unlike for graphs, in higher dimensions there is a rich array of non-equivalent notions of expansion (coboundary expansion, cosystolic expansion, topological expansion, spectral expansion, etc.), with differents strengths and applications. In this talk, we will survey this landscape of high-dimensional expansion, with a focus on two main results. First, we will present Gromov’s Topological Overlap Theorem, which asserts that coboundary expansion (a quantitative version of vanishing mod 2 cohomology) implies topological expansion (roughly, the property that for every map from a simplicial complex to a manifold of the same dimension, the images of a positive fraction of the simplices have a point in common). Second, we will outline a construction of bounded degree 2-dimensional topological expanders, due to Kaufman, Kazhdan, and Lubotzky.}, author = {Wagner, Uli}, issn = {2102-622X}, journal = {Bulletin de la Societe Mathematique de France}, pages = {281--294}, publisher = {Societe Mathematique de France}, title = {{High-dimensional expanders (after Gromov, Kaufman, Kazhdan, Lubotzky, and others)}}, doi = {10.24033/ast.1188}, volume = {438}, year = {2022}, } @article{14437, abstract = {Future LEDs could be based on lead halide perovskites. A breakthrough in preparing device-compatible solids composed of nanoscale perovskite crystals overcomes a long-standing hurdle in making blue perovskite LEDs.}, author = {Utzat, Hendrik and Ibáñez, Maria}, issn = {1476-4687}, journal = {Nature}, keywords = {Multidisciplinary}, number = {7941}, pages = {638--639}, publisher = {Springer Nature}, title = {{Molecular engineering enables bright blue LEDs}}, doi = {10.1038/d41586-022-04447-0}, volume = {612}, year = {2022}, } @article{17868, abstract = {Reversed conductance decay describes increasing conductance of a molecular chain series with increasing chain length. Realizing reversed conductance decay is an important step toward making long and highly conducting molecular wires. Recent work has shown that one-dimensional topological insulators (1D TIs) can exhibit reversed conductance decay due to their nontrivial edge states. The Su–Schrieffer–Heeger (SSH) model for 1D TIs relates to the electronic structure of these isolated molecules but not their electron transport properties as single-molecule junctions. Herein, we use a tight-binding approach to demonstrate that polyacetylene and other diradicaloid 1D TIs show a reversed conductance decay at the short chain limit. We explain these conductance trends by analyzing the impact of the edge states in these 1D systems on the single-molecule junction transmission. Additionally, we discuss how the self-energy from the electrode-molecule coupling and the on-site energy of the edge sites can be tuned to create longer wires with reversed conductance decays.}, author = {Li, Liang and Gunasekaran, Suman and Wei, Yujing and Nuckolls, Colin and Venkataraman, Latha}, issn = {1948-7185}, journal = {The Journal of Physical Chemistry Letters}, number = {41}, pages = {9703--9710}, publisher = {American Chemical Society}, title = {{Reversed conductance decay of 1D topological insulators by tight-binding analysis}}, doi = {10.1021/acs.jpclett.2c02812}, volume = {13}, year = {2022}, } @article{17869, abstract = {The formation of carbon–carbon bonds with transition metal reagents serves as a cornerstone of organic synthesis. Here, we show that the reactivity of an otherwise kinetically inert transition metal complex can be induced by an external electric field to affect a coupling reaction. These results highlight the importance of electric field effects in reaction chemistry and offers a new strategy to modulate organometallic reactivity.}, author = {Orchanian, Nicholas M. and Guizzo, Sophia and Steigerwald, Michael L. and Nuckolls, Colin and Venkataraman, Latha}, issn = {1364-548X}, journal = {Chemical Communications}, number = {90}, pages = {12556--12559}, publisher = {Royal Society of Chemistry}, title = {{Electric-field-induced coupling of aryl iodides with a nickel(0) complex}}, doi = {10.1039/d2cc03671a}, volume = {58}, year = {2022}, } @article{17870, abstract = {The electric fields created at solid–liquid interfaces are important in heterogeneous catalysis. Here we describe the Ullmann coupling of aryl iodides on rough gold surfaces, which we monitor in situ using the scanning tunneling microscope-based break junction (STM-BJ) and ex situ using mass spectrometry and fluorescence spectroscopy. We find that this Ullmann coupling reaction occurs only on rough gold surfaces in polar solvents, the latter of which implicates interfacial electric fields. These experimental observations are supported by density functional theory calculations that elucidate the roles of surface roughness and local electric fields on the reaction. More broadly, this touchstone study offers a facile method to access and probe in real time an increasingly prominent yet incompletely understood mode of catalysis.}, author = {Stone, Ilana B. and Starr, Rachel L. and Hoffmann, Norah and Wang, Xiao and Evans, Austin M. and Nuckolls, Colin and Lambert, Tristan H. and Steigerwald, Michael L. and Berkelbach, Timothy C. and Roy, Xavier and Venkataraman, Latha}, issn = {2041-6539}, journal = {Chemical Science}, number = {36}, pages = {10798--10805}, publisher = {Royal Society of Chemistry}, title = {{Interfacial electric fields catalyze Ullmann coupling reactions on gold surfaces}}, doi = {10.1039/d2sc03780g}, volume = {13}, year = {2022}, }