MOOC
Path Integral Methods in Atomistic Modelling
Related publications (180)
2023 Roadmap on molecular modelling of electrochemical energy materials
Alfredo Pasquarello, Chunmin Zhang, Assil Bouzid
New materials for electrochemical energy storage and conversion are the key to the electrification and sustainable development of our modern societies. Molecular modelling based on the principles of quantum mechanics and statistical mechanics as well as em ...
Bristol2023Sampling-Based AQP in Modern Analytical Engines
Anastasia Ailamaki, Viktor Sanca
As the data volume grows, reducing the query execution times remains an elusive goal. While approximate query processing (AQP) techniques present a principled method to trade off accuracy for faster queries in analytics, the sample creation is often consid ...
ACM2022Meta-Learners for Estimation of Causal Effects: Finite Sample Cross-Fit Performance
Estimation of causal effects using machine learning methods has become an active research field in econometrics. In this paper, we study the finite sample performance of meta-learners for estimation of heterogeneous treatment effects under the usage of sam ...
2022On the Double Descent of Random Features Models Trained with SGD
We study generalization properties of random features (RF) regression in high dimensions optimized by stochastic gradient descent (SGD) in under-/overparameterized regime. In this work, we derive precise non-asymptotic error bounds of RF regression under b ...
2022Global Free-Energy Landscapes as a Smoothly Joined Collection of Local Maps
Michele Ceriotti, Gareth Aneurin Tribello, Federico Giberti
Enhanced sampling techniques have become an essential tool in computational chemistry and physics, where they are applied to sample activated processes that occur on a time scale that is inaccessible to conventional simulations. Despite their popularity, i ...
AMER CHEMICAL SOC2021Generalized parallel tempering on Bayesian inverse problems
Fabio Nobile, Juan Pablo Madrigal Cianci
In the current work we present two generalizations of the Parallel Tempering algorithm in the context of discrete-timeMarkov chainMonteCarlo methods for Bayesian inverse problems. These generalizations use state-dependent swapping rates, inspired by the so ...
SPRINGER2021A general and efficient framework for atomistic machine learning
Over the last two decades, many technological and scientific discoveries, ranging from the development of materials for energy conversion and storage through the design of new drugs, have been accelerated by the use of preliminary in silico experiments, to ...
EPFL2021Is there an analog of Nesterov acceleration for gradient-based MCMC?
Nicolas Henri Bernard Flammarion, Xiang Cheng
We formulate gradient-based Markov chain Monte Carlo (MCMC) sampling as optimization on the space of probability measures, with Kullback-Leibler (KL) divergence as the objective functional. We show that an under-damped form of the Langevin algorithm perfor ...
INT STATISTICAL INST2021Printed Iontophoretic‐Integrated Wearable Microfluidic Sweat‐Sensing Patch for On‐Demand Point‐Of‐Care Sweat Analysis
Danick Briand, Silvia Demuru, Brince Paul Kunnel
In recent years, wearable epidermal sweat sensors have received extensive attention owing to their great potential to provide personalized information on the health status of individuals at the molecular level. For on‐demand medical analysis of sweat in se ...
2021Uncertain Sampling with Certain Priors
Sampling has always been at the heart of signal processing providing a bridge between the analogue world and discrete representations of it, as our ability to process data in continuous space is quite limited. Furthermore, sampling plays a key part in unde ...
EPFL2021