Publication
Toward High-level Machine Learning Potential for Water Based on Quantum Fragmentation and Neural Networks
Related publications (35)
Encoding quantum-chemical knowledge into machine-learning models of complex molecular properties
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 ...
EPFL2024Structure and dynamics of liquid water from ab initio simulations: adding Minnesota density functionals to Jacob's ladder
Ursula Röthlisberger, Justin Villard, Martin Peter Bircher
The accurate representation of the structural and dynamical properties of water is essential for simulating the unique behavior of this ubiquitous solvent. Here we assess the current status of describing liquid water using ab initio molecular dynamics, wit ...
Cambridge2024Advancing Computational Chemistry with Stochastic and Artificial Intelligence Approaches
Computational chemistry aims to simulate reactions and molecular properties at the atomic scale, advancing the design of novel compounds and materials with economic, environmental, and societal implications. However, the field relies on approximate quantum ...
EPFL2023Temporal Prediction of Landslide-GeneratedWaves Using a Theoretical–Statistical Combined Method.
Christophe Ancey, Zhenzhu Meng, Yating Hu
For the prediction of landslide-generated waves, previous studies have developed numerous empirical equations to express the maximums of wave characteristics as functions of slide parameters upon impact. In this study, we built the temporal relationship be ...
2023Nonlinear inviscid damping and shear-buoyancy instability in the two-dimensional Boussinesq equations
We investigate the long-time properties of the two-dimensional inviscid Boussinesq equations near a stably stratified Couette flow, for an initial Gevrey perturbation of size & epsilon;. Under the classical Miles-Howard stability condition on the Richardso ...
WILEY2023Deterministic and Statistical Approaches to Quantum Chemistry
The field of quantum chemistry has recently undergone a series of paradigm shifts, including a boom in machine learning applications that target the electronic structure problem. Along with these technological innovations, the community continues to identi ...
EPFL2020Experimental study of impulse waves generated by viscoplastic fluid
Landslide-generated waves, also called impulse waves, occur as a result of the intrusion of landslides (such as rock falls, debris flows, and avalanches) into bodies of water (such as lakes, reservoirs, and seas). The objective of this thesis was to study ...
EPFL2020Gravity Wave Excitation during the Coastal Transition of an Extreme Katabatic Flow in Antarctica
Alexis Berne, Etienne Gabriel Henri Vignon
The offshore extent of Antarctic katabatic winds exerts a strong control on the production of sea ice and the formation of polynyas. In this study, we make use of a combination of ground-based remotely sensed and meteorological measurements at Dumont d'Urv ...
AMER METEOROLOGICAL SOC2020Simulating Solvation and Acidity in Complex Mixtures with First-Principles Accuracy: The Case of CH3SO3H and H2O2 in Phenol
Michele Ceriotti, Kevin Rossi, Veronika Juraskova
We present a generally applicable computational framework for the efficient and accurate characterization of molecular structural patterns and acid properties in an explicit solvent using H2O2 and CH3SO3H in phenol as an example. To address the challenges ...
AMER CHEMICAL SOC2020Ab 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 ...
NATL ACAD SCIENCES2019