Publications related to Machine learning meets volcano plots: Computational discovery of cross-coupling catalysts

Palladium-based supramolecular assemblies: from complex structures to water-soluble anion-receptors

The combination of palladium salts and bipyridyl ligands can lead to the formation of a large variety of coordination complexes, with different shapes and sizes, displaying a very versatile host-guest chemistry. Increasing their structural complexity remai ...

EPFL2024

Lewis acid catalyzed [4+2] annulation of bicyclobutanes with dienol ethers for the synthesis of bicyclo[4.1.1]octanes

Jérôme Waser, Stefano Nicolai

Bicyclic carbocycles containing a high fraction of Csp3 have become highly attractive synthetic targets because of the multiple applications they have found in medicinal chemistry. The formal cycloaddition of bicyclobutanes (BCBs) with two- or three-atom p ...

Royal Soc Chemistry2024

Surface segregation in high-entropy alloys from alchemical machine learning

Michele Ceriotti, Sandip De, Nataliya Lopanitsyna, Guillaume André Jean Fraux, Arslan Mazitov

High-entropy alloys (HEAs), containing several metallic elements in near-equimolar proportions, have long been of interest for their unique mechanical properties. More recently, they have emerged as a promising platform for the development of novel heterog ...

Iop Publishing Ltd2024

Random matrix methods for high-dimensional machine learning models

Antoine Philippe Michel Bodin

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 ...

EPFL2024

Few-shot Learning for Efficient and Effective Machine Learning Model Adaptation

Arnout Jan J Devos

Machine learning (ML) enables artificial intelligent (AI) agents to learn autonomously from data obtained from their environment to perform tasks. Modern ML systems have proven to be extremely effective, reaching or even exceeding human intelligence. Altho ...

EPFL2024

Novel Alkynylation Methods through the Combination of Hypervalent Iodine Reagents and Alkynyl-trifluoroborate Salts

Julien Aymeric Borrel

The alkyne motif is a versatile functional group often encountered in organic chemistry. It can be involved in various transformations such as the alkyne-azide cycloaddition and has found widespread application in medicinal chemistry, chemical biology and ...

EPFL2024

Partial discharge localization in power transformer tanks using machine learning methods

Farhad Rachidi-Haeri, Marcos Rubinstein, Hamidreza Karami

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 ...

Nature Publishing Group2024

Topics in statistical physics of high-dimensional machine learning

Hugo Chao Cui

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 ...

EPFL2024

Robust machine learning for neuroscientific inference

Steffen Schneider

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 ...

EPFL2024

Reduced Training Data for Laser Ultrasound Signal Interpretation by Neural Networks

Romain Christophe Rémy Fleury, Janez Rus

The performance of machine learning algorithms is conditioned by the availability of training datasets, which is especially true for the field of nondestructive evaluation. Here we propose one reconfigurable specimen instead of numerous reference specimens ...

2024

Heterogeneous catalysis via light-heat dual activation: A path to the breakthrough in C1 chemistry

Bingqiao Xie

A hybrid photothermal catalytic system, which combines both the photochemical (light) and thermal (heat) activation pathways over a bifunctional catalyst, has demonstrated remarkable levels of reaction activity and selectivity when compared with individual ...

Cell Press2024

Residual-based attention in physics-informed neural networks

Nikolaos Stergiopoulos, Sokratis Anagnostopoulos

Driven by the need for more efficient and seamless integration of physical models and data, physics -informed neural networks (PINNs) have seen a surge of interest in recent years. However, ensuring the reliability of their convergence and accuracy remains ...

Elsevier Science Sa2024

Optimization Algorithms for Decentralized, Distributed and Collaborative Machine Learning

Anastasiia Koloskova

Distributed learning is the key for enabling training of modern large-scale machine learning models, through parallelising the learning process. Collaborative learning is essential for learning from privacy-sensitive data that is distributed across various ...

EPFL2024

Machine Learning for Modeling Stock Returns

Teng Andrea Xu

Throughout history, the pace of knowledge and information sharing has evolved into an unthinkable speed and media. At the end of the XVII century, in Europe, the ideas that would shape the "Age of Enlightenment" were slowly being developed in coffeehouses, ...

EPFL2024

Arylative Ring Expansion of 3-Vinylazetidin-3-Ols and 3-Vinyloxetan-3-Ols to Dihydrofurans by Dual Palladium and Acid Catalysis

Qian Wang, Jieping Zhu, Takuji Fujii

In contrast to the well-studied 1-vinylcyclobu- tanols, the reactivity of 3-vinylazetidin-3-ols 1 and 3- vinyloxetan-3-ols 2 under transition metal catalysis remains largely unexplored. We report herein their unique reactivity under dual palladium and acid ...

Wiley-VCH Verlag GmbH2024

Predicting the long-term collective behaviour of fish pairs with deep learning

Francesco Mondada, Alexandre Massoud Alahi, Vaios Papaspyros

Modern computing has enhanced our understanding of how social interactions shape collective behaviour in animal societies. Although analytical models dominate in studying collective behaviour, this study introduces a deep learning model to assess social in ...

2024

Performing and Detecting Backdoor Attacks on Face Recognition Algorithms

Alexander Carl Unnervik

The field of biometrics, and especially face recognition, has seen a wide-spread adoption the last few years, from access control on personal devices such as phones and laptops, to automated border controls such as in airports. The stakes are increasingly ...

EPFL2024

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

Ksenia Briling

Statistical (machine-learning, ML) models are more and more often used in computational chemistry as a substitute to more expensive ab initio and parametrizable methods. While the ML algorithms are capable of learning physical laws implicitly from data, ad ...

EPFL2024

Machine learning-aided generative molecular design

Philippe Schwaller, Jeff Guo

Machine learning has provided a means to accelerate early-stage drug discovery by combining molecule generation and filtering steps in a single architecture that leverages the experience and design preferences of medicinal chemists. However, designing mach ...

Nature Portfolio2024

On the Generalization of Stochastic Gradient Descent with Momentum

Volkan Cevher, Kimon Antonakopoulos

While momentum-based accelerated variants of stochastic gradient descent (SGD) are widely used when training machine learning models, there is little theoretical understanding on the generalization error of such methods. In this work, we first show that th ...

Microtome Publishing2024