Encoding quantum-chemical knowledge into machine-learning models of complex molecular properties

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.

Faithful human performance capture and free-view rendering from sparse RGB observations is a long-standing problem in Vision and Graphics. The main challenges are the lack of observations and the inherent ambiguities of the setting, e.g. occlusions and dep ...

To characterize in detail the charge density wave (CDW) transition of 1T-VSe2, its electronic structure and lattice dynamics are comprehensively studied by means of x-ray diffraction, muon spectroscopy, angle resolved photoemission (ARPES), diffuse and ine ...

A robust anomaly detection mechanism should possess the capability to effectively remediate anomalies, restoring them to a healthy state, while preserving essential healthy information. Despite the efficacy of existing generative models in learning the und ...

Automatic speech recognition (ASR) systems are well known to perform poorly on dysarthric speech. Previous works have addressed this by speaking rate modification to reduce the mismatch with typical speech. Unfortunately, these approaches rely on transcrib ...

Jointly estimating hand and object shape facilitates the grasping task in human-to-robot handovers. Relying on handcrafted prior knowledge about the geometric structure of the object fails when generalising to unseen objects, and depth sensors fail to dete ...

In the rapidly evolving landscape of machine learning research, neural networks stand out with their ever-expanding number of parameters and reliance on increasingly large datasets. The financial cost and computational resources required for the training p ...

Adversarial examples crafted in black-box scenarios are affected by unrealistic colors or spatial artifacts. To prevent these shortcomings, we propose a novel strategy that generates adversarial images with low detectability and high transferability. The p ...

Molecular junctions represent a fascinating frontier in the realm of nanotechnology and are one of the smallest optoelectronic devices possible, consisting of individual molecules or a group of molecules that serve as the active element sandwiched between ...

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 ...

This paper presents a comparison of machine learning (ML) methods used for three-dimensional localization of partial discharges (PD) in a power transformer tank. The study examines ML and deep learning (DL) methods, ranging from support vector machines (SV ...

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. ...

At room temperature, mechanical motion driven by the quantum backaction of light has been observed only in pioneering experiments in which an optical restoring force controls the oscillator stiffness1,2. For solid-state mechanical resonators in which oscil ...

Physics-informed neural networks (PINNs) have emerged as a powerful tool for solving differential equations by integrating physical laws into the learning process. This work leverages PINNs to simulate gravitational collapse, a critical phenomenon in astro ...

Through the use of the piecewise-linearity condition of the total energy, we correct the self-interaction for the study of polarons by constructing nonempirical functionals at the semilocal level of theory. We consider two functionals, the gamma DFT and mu ...

Photorealistic synthetic data and novel rendering techniques significantly advanced computer vision research. However, datasets focused on computer vision applications cannot be easily applied to robotics because they typically lack physics-related informa ...

Using scanning tunneling microscopy (STM), we experimentally and theoretically investigate isolated platinum phthalocyanine (PtPc) molecules adsorbed on an atomically thin NaCl(100) film vapor deposited on Au(111). We obtain good agreement between theory a ...

The present work proposes an extension to the approach of [Xi, C; et al. J. Chem. Theory Comput. 2022, 18, 6878] to calculate ion solvation free energies from first-principles (FP) molecular dynamics (MD) simulations of a hybrid solvation model. The approa ...

Modern neuroscience research is generating increasingly large datasets, from recording thousands of neurons over long timescales to behavioral recordings of animals spanning weeks, months, or even years. Despite a great variety in recording setups and expe ...

In the past few years, Machine Learning (ML) techniques have ushered in a paradigm shift, allowing the harnessing of ever more abundant sources of data to automate complex tasks. The technical workhorse behind these important breakthroughs arguably lies in ...

This dataset accompanies the publication "Quantum-mechanical effects in photoluminescence from thin crystalline gold films" published in Light: Science & Applications (https://doi.org/10.1038/s41377-024-01408-2). The data can be used to reproduce plots 1-4 ...

Encoding quantum-chemical knowledge into machine-learning models of complex molecular properties

Chat with Graph Search

MetaCap: Meta-learning Priors from Multi-view Imagery for Sparse-View Human Performance Capture and Rendering

Electronic structure and lattice dynamics of 1T-VSe2:Origin of the three-dimensional charge density wave

Self-Supervised Anomaly Segmentation via Diffusion Models with Dynamic Transformer UNet

Unsupervised Rhythm and Voice Conversion of Dysarthric to Healthy Speech for ASR

Stereo Hand-Object Reconstruction for Human-to-Robot Handover

Random matrix methods for high-dimensional machine learning models

GLoFool: Global Enhancements and Local Perturbations to Craft Adversarial Images

Plasmonically enhanced molecular junctions for investigation of atomic-scale fluctuations in self-assembled monolayers

Directly optimizing for synthesizability in generative molecular design using retrosynthesis models

Partial discharge localization in power transformer tanks using machine learning methods

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

Room-temperature quantum optomechanics using an ultralow noise cavity

SPINN: Advancing Cosmological Simulations of Fuzzy Dark Matter with Physics Informed Neural Networks

Nonempirical semilocal density functionals for correcting the self-interaction of polaronic states

GRADE: Generating Realistic and Dynamic Environments for robotics research with Isaac Sim

Scanning Tunneling Microscopy for Molecules: Effects of Electron Propagation into Vacuum

Solvation Free Energies from Machine Learning Molecular Dynamics

Robust machine learning for neuroscientific inference

Topics in statistical physics of high-dimensional machine learning

Dataset to accompany publication "Quantum-mechanical effects in photoluminescence from thin crystalline gold films"