A large scale benchmark for molecular machine learning consisting of multiple public datasets, metrics, featurizations and learning algorithms.

Many applications of machine learning require a model to make accurate pre-dictions on test examples that are distributionally different from training ones, while task-specific labels are scarce during training. An effective approach to this challenge is to pre-train a model on related tasks where data is abundant, and then fine-tune it on a downstream task of interest. While pre-training has been effective in many language and vision domains, it remains an open question how to effectively use pre-training on graph datasets. In this paper, we develop a new strategy and self-supervised methods for pre-training Graph Neural Networks (GNNs). The key to the success of our strategy is to pre-train an expressive GNN at the level of individual nodes as well as entire graphs so that the GNN can learn useful local and global representations simultaneously. We systematically study pre-training on multiple graph classification datasets. We find that naive strategies, which pre-train GNNs at the level of either entire graphs or individual nodes, give limited improvement and can even lead to negative transfer on many downstream tasks. In contrast, our strategy avoids negative transfer and improves generalization significantly across downstream tasks, leading up to 9.4% absolute improvements in ROC-AUC over non-pre-trained models and achieving state-of-the-art performance for molecular property prediction and protein function prediction.

A highly efficient solid‐base organocatalyst for the gas‐phase aldol self‐condensation of n‐butanal to 2‐ethylhexenal was developed by grafting site‐isolated amines on tailored silica surfaces. The catalytic activity depends largely on the nature of amine species, the surface concentration of amine and silanol groups, and the spatial separation between the silanol and amine groups. In situ FTIR measurements demonstrated that the formation of nucleophilic enamines leads to the enhanced catalytic activity of secondary amine catalysts, whereas the formation of imines (stable up to 473 K) leads to the low activity observed for silica‐supported primary amines. Blocking the silanol groups on the silica support by silylation or cofeeding water into the reaction stream drastically decreased the reaction rates, demonstrating that weaker acidic silanol groups participate cooperatively with the amine groups to catalyze the condensation reaction. This work demonstrates that the spatial separation of the weakly acidic silanols and amines can be tuned by the controlled dehydration of the supporting silica and by varying the linker length of the amine organosilane precursor used to graft the amine to the support surface. A mechanism for aldol condensation was proposed and then analyzed by DFT calculations. DFT analysis of the reaction pathway suggested that the rate‐limiting step in aldol condensation is carboncarbon bond formation, which is consistent with the observed kinetics. The calculated apparent activation barrier agrees reasonably with that measured experimentally.

NASA’s Ice, Cloud, and land Elevation Satellite (ICESat-2), designed for surface altimetry, plays a pivotal role in providing precise ice sheet elevation measurements. While its primary focus is altimetry, ICESat-2 also offers valuable atmospheric data. Current conventional processing methods for producing atmospheric data products encounter challenges, particularly in conditions with low signal or high background noise. The thresholding technique traditionally used for atmospheric feature detection in lidar data uses a threshold value to accept signals while rejecting noise, which may result in signal loss or false detection in the presence of excessive noise. Traditional approaches for improving feature detection, such as averaging, lead to a trade-off between detection resolution and accuracy. In addition, the discrimination of cloud from aerosol in the identified features is difficult given ICESat-2’s single wavelength and lack of depolarization measurement capability. To address these challenges, we demonstrate atmospheric feature detection and cloud–aerosol discrimination using deep learning-based semantic segmentation by a convolutional neural network (CNN). The key findings from our research are the effectiveness of a deep learning model for feature detection and cloud–aerosol classification in ICESat-2 atmospheric data and the model’s surprising capability to detect complex atmospheric features at a finer resolution than is currently possible with traditional processing techniques. We identify several examples where the traditional feature detection and cloud–aerosol discrimination algorithms struggle, like in scenarios with several layers of vertically stacked clouds, or in the presence of clouds embedded within aerosol, and demonstrate the ability of the CNN model to detect such features, resolving the boundaries between adjacent layers and detecting clouds hidden within aerosol layers at a fine resolution.

We present an improved method for determining guess structures for transition state searches by incorporating internal coordinate interpolation into the freezing string method (FSM). We test our method on over 40 reactions across 3 benchmark data sets covering a diverse set of chemical reactions. Our results show that incorporation of internal coordinate interpolation improves the reliability of the FSM, enabling larger interpolation step sizes and fewer optimization steps per cycle, which together yield nearly a 50% reduction in computational cost while maintaining a 100% success rate on benchmark chemical reaction test cases, including systems where previous attempts based on linear synchronous transit interpolation have failed. We provide an open-source Python implementation of the FSM, in addition to the reactant, product, and transition state structures of all reactions studied.

Surface wettability plays an important role in heterogeneous electrocatalysis. Here we report a facile laser ablation strategy to directly modify the wettability of the silver catalyst surface and investigate its effect on oxygen reduction reaction (ORR). A broad range tuning of 2e⁻/4e⁻ ORR pathways was achieved, with hydrophilic silver surfaces (contact angle (θ_w) 31.1° ± 0.6°) showing high activity and selectivity towards 4e⁻ reduction of oxygen to water.