Quantum Chemistry Meets Machine Learning

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Demographic fairness or equity is a crucial aspect of machine learning models, particularly in critical domains such as healthcare, where errors can have severe consequences. A fair model avoids making distinctions between groups with different sensitive o ...

We introduce a new approach to defect engineering in Zr-based metal-organic frameworks (Zr-MOFs), aiming to reduce Zr site valency while preserving high node connectivity. Using a rapid heat treatment (RHT) in humid air, oxygen vacancies (O-vacancies) were ...

Cyclopropanes have emerged as powerful C3 building blocks in organic synthesis. Cyclopropanes serve as precursors to generate reactive intermediates in various total syntheses of natural products and bioactive molecules. Several strategies have been develo ...

The sum-of-squares (SOS) approximation method is a technique used in optimization problems to derive lower bounds on the optimal value of an objective function. By representing the objective function as a sum of squares in a feature space, the SOS method t ...

By 'social learning,' in this article we refer to mechanisms for opinion formation and decision making over graphs and the study of how agents' decisions evolve dynamically through interactions with neighbors and the environment. The study of social learni ...

Over the past decade, quantum photonics platforms aiming at harnessing the fundamental properties of single particles, such as quantum superposition and quantum entanglement, have flourished. In this context, single-photon emitters capable of operating at ...

Sensing and imaging of light in the shortwave infrared (SWIR) range is increasingly used in various fields, including bio-imaging, remote sensing, and semiconductor process control. SWIR-sensitive organic photodetectors (OPDs) are promising because organic ...

Our understanding of the generalization capabilities of neural networks (NNs) is still incomplete. Prevailing explanations are based on implicit biases of gradient descent (GD) but they cannot account for the capabilities of models from gradient-free metho ...

The increasing complexity of transformer models in artificial intelligence expands their computational costs, memory usage, and energy consumption. Hardware acceleration tackles the ensuing challenges by designing processors and accelerators tailored for t ...

Pt(II)-based molecular catalysts stand as a prototypical system in hydrogen evolution reactions (HER) owing to their consistently elevated activity levels. Their integration into heterogeneous systems thus provides an ideal platform to develop catalytic ma ...

Graph neural networks (GNNs) have demonstrated promising performance across various chemistry-related tasks. However, conventional graphs only model the pairwise connectivity in molecules, failing to adequately represent higher order connections, such as m ...

Quantum optics studies how photons interact with other forms of matter, the understanding of which was crucial for the development of quantum mechanics as a whole. Starting from the photoelectric effect, the quantum property of light has led to the develop ...

Accurate human trajectory prediction is crucial for applications such as autonomous vehicles, robotics, and surveillance systems. Yet, existing models often fail to fully leverage the non-verbal social cues human subconsciously communicate when navigating ...

Plasmonic photochemistry has a large potential to replace energy-intensive chemical processes with low-temperature, low-pressure light-driven chemical reactions. Plasmonic nanostructures have emerged as promising photocatalysts with exceptional and tunable ...

Hot exciton materials have the potential to improve the quantum efficiency of organic light-emitting diodes (OLEDs) by promoting high reversed intersystem crossing (hRISC) between a high-lying triplet (T-n, n >= 2) and a radiative singlet (S-m). In recent ...

We study the random binary symmetric perceptron problem, focusing on the behavior of rare high-margin solutions. While most solutions are isolated, we demonstrate that these rare solutions are part of clusters of extensive entropy, heuristically correspond ...

Organic upconversion devices (OUCs) consist of an organic infrared photodetector and an organic visible light-emitting diode (OLED), connected in series. OUCs convert photons from the infrared to the visible and are of use in applications such as process c ...

The use of a bulk heterojunction of organic semiconductors to drive photoelectrochemical water splitting is an emerging trend; however, the optimum energy levels of the donor and acceptor have not been established for photoanode operation with respect to e ...

Data redundancy has been one of the most important problems in data-intensive applications such as data mining and machine learning. Removing data redundancy brings many benefits in efficient data updating, effective data storage, and error-free query proc ...

Quantum Chemistry Meets Machine Learning

Chattez avec Graph Search

Optimizing fairness and utility in healthcare machine learning models

Anisotropic node distortions in amorphous MOFs: Low-valent Zr sites as catalytic hotspots

Photo-mediated generation of radical synthons from cyclopropanes

Theory and applications of the sum-of-squares technique

Socially Intelligent Networks: A framework for decision making over graphs

Statistical mechanics of deep learning

Investigation of room-temperature single-photon emitters in GaN-based materials and GaN/AlN quantum dots

Shortwave Infrared-Sensitive, Narrowband Organic Upconversion Devices with a Gain

Neural Redshift: Random Networks are not Random Functions

Accelerator-driven Data Arrangement to Minimize Transformers Run-time on Multi-core Architectures

Photogeneration of Hydrogen: Insights from a Pt(II)-Complex Incorporated into a Covalent Organic Framework

Molecular hypergraph neural networks

Room-Temperature Quantum Optomechanics and Free-Electron Quantum Optics

Social-Transmotion: Promptable Human Trajectory Prediction

Plasmonic Nanostructures for Photocatalysis: Material, Photonic and Electronic Effects

Donor-Acceptor-Donor "Hot Exciton" Triads for High Reverse Intersystem Crossing in OLEDs

On the atypical solutions of the symmetric binary perceptron

On the Response Speed of Narrowband Organic Optical Upconversion Devices

Bulk Heterojunction Organic Semiconductor Photoanodes: Tuning Energy Levels to Optimize Electron Injection

Validating functional redundancy with mixed generative adversarial networks