Publications de Edgar Albert Engel

A data-driven interpretation of the stability of organic molecular crystals

Michele Ceriotti, Edgar Albert Engel, Maria Pakhnova

Due to the subtle balance of intermolecular interactions that govern structure-property relations, predicting the stability of crystal structures formed from molecular building blocks is a highly non-trivial scientific problem. A particularly active and fr ...

ROYAL SOC CHEMISTRY2023

A complete description of thermodynamic stabilities of molecular crystals

Venkat Kapil, Edgar Albert Engel

Predictions of relative stabilities of (competing) molecular crystals are of great technological relevance, most notably for the pharmaceutical industry. However, they present a long-standing challenge for modeling, as often minuscule free energy differenc ...

National Academy of Sciences2022

Local Kernel Regression and Neural Network Approaches to the Conformational Landscapes of Oligopeptides

Michele Ceriotti, Anne-Clémence Corminboeuf, Alberto Fabrizio, Benjamin André René Meyer, Edgar Albert Engel, Raimon Fabregat I De Aguilar-Amat, Veronika Juraskova

The application of machine learning to theoretical chemistry has made it possible to combine the accuracy of quantum chemical energetics with the thorough sampling of finite-temperature fluctuations. To reach this goal, a diverse set of methods has been pr ...

2022

Importance of Nuclear Quantum Effects for NMR Crystallography

Michele Ceriotti, Venkat Kapil, Edgar Albert Engel

The resolving power of solid-state nuclear magnetic resonance (NMR) crystallography depends heavily on the accuracy of computational predictions of NMR chemical shieldings of candidate structures, which are usually taken to be local minima in the potential ...

AMER CHEMICAL SOC2021

Improving sample and feature selection with principal covariates regression

Michele Ceriotti, Edgar Albert Engel, Benjamin Aaron Helfrecht, Sergei Kliavinek

Selecting the most relevant features and samples out of a large set of candidates is a task that occurs very often in the context of automated data analysis, where it improves the computational performance and often the transferability of a model. Here we ...

2021

Uncertainty estimation for molecular dynamics and sampling

Michele Ceriotti, Venkat Kapil, Yongbin Zhuang, Kevin Rossi, Edgar Albert Engel, Federico Grasselli, Giulio Imbalzano

Machine-learning models have emerged as a very effective strategy to sidestep time-consuming electronic-structure calculations, enabling accurate simulations of greater size, time scale, and complexity. Given the interpolative nature of these models, the r ...

2021

A Bayesian approach to NMR crystal structure determination

David Lyndon Emsley, Michele Ceriotti, Albert Hofstetter, Edgar Albert Engel, Federico Maria Paruzzo, Andrea Anelli

Nuclear Magnetic Resonance (NMR) spectroscopy is particularly well suited to determine the structure of molecules and materials in powdered form. Structure determination usually proceeds by finding the best match between experimentally observed NMR chemica ...

2019

Physics-based machine learning for materials and molecules

Michele Ceriotti, Michael John Willatt, Edgar Albert Engel

AMER CHEMICAL SOC2019

Ab initio thermodynamics of liquid and solid water

Michele Ceriotti, Bingqing Cheng, Edgar Albert Engel

Thermodynamic properties of liquid water as well as hexagonal (Ih) and cubic (Ic) ice are predicted based on density functional theory at the hybrid-functional level, rigorously taking into account quantum nuclear motion, anharmonic fluctuations, and proto ...

National Academy of Sciences2019

Assessment of Approximate Methods for Anharmonic Free Energies

Michele Ceriotti, Venkat Kapil, Edgar Albert Engel, Mariana Rossi Carvalho

Quantitative evaluation of the thermodynamic properties of materials—most notably their stability, as measured by the free energy—must take into account the role of thermal and zero-point energy fluctuations. While these effects can easily be estimated wit ...

2019