Balancing computational chemistry's potential with its environmental impact

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More attention has been paid to integrating existing knowledge with data to understand the complex mechanical behaviour of geomaterials, but it incurs scepticism and criticism on its generalizability and robustness. Moreover, a common mistake in current da ...

The first two chapters develop ML-based models that generate conditional SDF estimates efficiently using conditioning information sets, resulting in minimal pricing errors and superior Sharpe ratios compared to traditional conditional asset pricing models. ...

Monitoring of cardiac output (CO) is a mainstay of hemodynamic management in the acutely or critically ill patient. Invasive determination of CO using thermodilution, albeit regarded as the gold standard, is cumbersome and bears risks inherent to catheteri ...

Magnons are the quanta of spin waves and transport angular momenta through magnetically ordered materials. They can be used to distribute and control on-chip GHz signals without charge flow, thereby avoiding Joule heating. Beyond multiplexed signal process ...

BackgroundBronchiolitis Obliterans Syndrome (BOS), a fibrotic airway disease that may develop after lung transplantation, conventionally relies on pulmonary function tests (PFTs) for diagnosis due to limitations of CT imaging. Deep neural networks (DNNs) h ...

Object detection, a fundamental task in computer vision, is crucial for various intelligent edge computing applications. However, object detection algorithms are usually heavy in computation, hindering their deployments on resource-constrained edge devices ...

Scientific Machine Learning (SciML) is a recently emerged research field which combines physics-based and data-driven models for the numerical approximation of differential problems. Physics-based models rely on the physical understanding of the problem at ...

Synthesizability in generative molecular design remains a pressing challenge. Existing methods to assess synthesizability include heuristics-based metrics or retrosynthesis models which predict a synthetic pathway. By contrast, an explicit approach anchors ...

Recently developed van der Waals magnets offer a promising platform for advancing spintronics. The weak interlayer antiferromagnetic exchange coupling in van der Waals antiferromagnets allows for unique spin dynamics and control over magnons. In this study ...

This study integrates deep learning and geospatial analysis to enhance soil loss estimation in the Moulouya Watershed, a region prone to erosion due to diverse topography and climatic conditions. Traditional models like the Universal Soil Loss Equation (US ...

Speed and efficiency of codes for atomistic simulations can be improved through refactoring and tailoring for GPU architectures. This activity, however, comes with associated, often overlooked, costs, namely a reduced readability and flexibility upon optim ...

G protein-coupled receptors (GPCRs) are the largest human membrane protein family that transduce extracellular signals into cellular responses. They are major pharmacological targets, with approximately 26% of marketed drugs targeting GPCRs, primarily at t ...

This paper introduces a novel architecture for Quantum Graph Neural Networks, which is significantly different from previous approaches found in the literature. The proposed approach produces similar outcomes with respect to previous models but with fewer ...

A search for heavy neutral gauge bosons (Z ') decaying into a pair of tau leptons is performed in proton-proton collisions at root s = 13 TeV at the CERN LHC. The data were collected with the CMS detector and correspond to an integrated luminosity of 138 f ...

As the demand for high channel counts and high-resolution recordings of neural activity continues to grow, the increased power and data rate generated impose hard constraints on the telemetry capabilities of wireless implantable neural interfaces. To addre ...

Slope deformation poses significant risks to infrastructure, ecosystems, and human safety, making early and accurate predictions essential for mitigating slope failures and landslides. In this study, we propose a novel approach that integrates a fractional ...

MiMiC is a framework for modeling large-scale chemical processes that require treatment at multiple resolutions. It does not aim to implement single-handedly all methods required to treat individual subsystems, but instead, it relegates this task to specia ...

Integer linear programming (ILP) is an elegant approach to solve linear optimization problems, naturally described using integer decision variables. Within the context of physics-inspired machine learning (ML) applied to chemistry, we demonstrate the relev ...

Based on 6.1 fb(-1) of ethorne- annihilation data collected at center-of-mass energies from 4.600 to 4.843 GeV with the BESIII detector at the BEPCII collider, a partial wave analysis of Lambda(+)(c) -> Lambda pi(+)eta is performed, and branching fractions ...

Cluster expansions are commonly employed as surrogate models to link the electronic structure of an alloy to its finite-temperature properties. Using cluster expansions to model materials with several alloying elements is challenging due to a rapid increas ...

Balancing computational chemistry's potential with its environmental impact

Chattez avec Graph Search

t-PiNet: A thermodynamics-informed hierarchical learning for discovering constitutive relations of geomaterials

Machine Learning Applications to Conditional Asset Pricing: Attention, CNNs, and Neural Tangent Kernels

Machine learning-enabled estimation of cardiac output from peripheral waveforms is independent of blood pressure measurement location in an in silico population

Perspective on nonvolatile magnon-signal storage and in-memory computation for low-power consuming magnonics

Harnessing deep learning to detect bronchiolitis obliterans syndrome from chest CT

Edge-Cloud Collaborated Object Detection via Bandwidth Adaptive Difficult-Case Discriminator

Combining physics-based and data-driven models: advancing the frontiers of research with scientific machine learning

Directly optimizing for synthesizability in generative molecular design using retrosynthesis models

Coherent magnon transport in a van der Waals antiferromagnet

Application of Deep Learning and Geospatial Analysis in Soil Loss Risk in the Moulouya Watershed, Morocco

A tale of two codes: CUDA vs OpenACC for mass-zero constrained dynamics

Functional dynamics of G protein-coupled receptors reveal new routes for drug discovery

A Study on Quantum Graph Neural Networks Applied to Molecular Physics

Search for Heavy Neutral Resonances Decaying to Tau Lepton Pairs in Proton-proton Collisions at √s=13 Tev

A Reconfigurable High-Dynamic Range ∆Σ Front-End with Event-Based Decimation for Bandwidth-Efficient Implantable Neural Interfaces

Slope Deformation Prediction Combining Particle Swarm Optimization-Based Fractional-Order Grey Model and K-Means Clustering

Making Puzzle Pieces Fit or Reshaping MiMiC for Multiscale Simulations with CP2K and More

Integer Linear Programming for Unsupervised Training Set Selection in Molecular Machine Learning

Observation of Λc+ → Λa0 (980)+ and Evidence for Σ(1380)+ in Λc+ → Λπ+η

Constructing multicomponent cluster expansions with machine-learning and chemical embedding