Publications by Michele Ceriotti

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In recent years, we have been witnessing a paradigm shift in computational materials science. In fact, traditional methods, mostly developed in the second half of the XXth century, are being complemented, extended, and sometimes even completely replaced by ...

The electronic density of states (DOS) quantifies the distribution of the energy levels that can be occupied by electrons in a quasiparticle picture and is central to modern electronic structure theory. It also underpins the computation and interpretation ...

Background: The increasingly common applications of machine-learning schemes to atomic-scale simulations have triggered efforts to better understand the mathematical properties of the mapping between the Cartesian coordinates of the atoms and the variety o ...

Over the past decade, interatomic potentials based on machine learning (ML) techniques have become an indispensable tool in the atomic-scale modeling of materials. Trained on energies and forces obtained from electronic-structure calculations, they inherit ...

The nature of the liquid-solid interface determines the characteristics of a variety of physical phenomena, including catalysis, electrochemistry, lubrication, and crystal growth. Most of the established models for crystal growth are based on macroscopic t ...

Progress in the atomic-scale modeling of matter over the past decade has been tremendous. This progress has been brought about by improvements in methods for evaluating interatomic forces that work by either solving the electronic structure problem explici ...

Spherical harmonics provide a smooth, orthogonal, and symmetry-adapted basis to expand functions on a sphere, and they are used routinely in physical and theoretical chemistry as well as in different fields of science and technology, from geology and atmos ...

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 ...

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 ...

The separation of hydrogen isotopes for applications such as nuclear fusion is a major challenge. Current technologies are energy intensive and inefficient. Nanoporous materials have the potential to separate hydrogen isotopes by kinetic quantum sieving, b ...

Data-driven schemes that associate molecular and crystal structures with their microscopic properties share the need for a concise, effective description of the arrangement of their atomic constituents. Many types of models rely on descriptions of atom-cen ...

In computational physics, chemistry, and biology, the implementation of new techniques in shared and open-source software lowers barriers to entry and promotes rapid scientific progress. However, effectively training new software users presents several cha ...

We introduce a nonequilibrium molecular dynamics simulation approach, based on the generalized Langevin equation, to study vibrational energy relaxation in pump probe spectroscopy. A colored noise thermostat is used to selectively excite a set of vibration ...

The electronic density of states (DOS) provides information regarding the distribution of electronic energy levels in a material, and can be used to approximate its optical and electronic properties and therefore guide computational materials design. Given ...

Predictive computational methods have the potential to significantly accelerate the discovery of new materials with targeted properties by guiding the choice of candidate materials for synthesis. Recently, a planar pyrrole-based azaphenacene molecule (pyri ...

Automated analyses of the outcome of a simulation have been an important part of atomistic modeling since the early days, addressing the need of linking the behavior of individual atoms and the collective properties that are usually the final quantity of i ...

In light of the pressing need for practical materials and molecular solutions to renewable energy and health problems, to name just two examples, one wonders how to accelerate research and development in the chemical sciences, so as to address the time it ...

One of the most prominent consequences of the quantum nature of light atomic nuclei is that their kinetic energy does not follow a Maxwell-Boltzmann distribution. Deep inelastic neutron scattering (DINS) experiments can measure this effect. Thus, the nucle ...

In this work, a detailed study is conducted to understand how ligand substitution influences the CO, and N-2 adsorption properties of two highly crystalline sodalite metal-organic frameworks (MOFs) known as Cu-BTT (BTT-3 = 1,3,5-benzenetristetrazolate) and ...

Fractionation of isotopes among distinct molecules or phases is a quantum effect which is often exploited to obtain insights on reaction mechanisms, biochemical, geochemical, and atmospheric phenomena. Accurate evaluation of isotope ratios in atomistic sim ...

Michele Ceriotti

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Roadmap on Machine learning in electronic structure

Learning the electronic density of states in condensed matter

Local invertibility and sensitivity of atomic structure-feature mappings

Beyond potentials: Integrated machine learning models for materials

3D Ordering at the Liquid-Solid Polar Interface of Nanowires

i-PI 2.0: A universal force engine for advanced molecular simulations

Fast evaluation of spherical harmonics with sphericart

Uncertainty estimation for molecular dynamics and sampling

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

Barely porous organic cages for hydrogen isotope separation

Unified theory of atom-centered representations and message-passing machine-learning schemes

PLUMED Tutorials: A collaborative, community-driven learning ecosystem

Simulating Energy Relaxation in Pump-Probe Vibrational Spectroscopy of Hydrogen-Bonded Liquids

Adaptive energy reference for machine-learning models of the electronic density of states

Large-Scale Computational Screening of Molecular Organic Semiconductors Using Crystal Structure Prediction

Unsupervised machine learning in atomistic simulations, between predictions and understanding

Accelerated chemical science with AI

Nuclear Quantum Effects in Water at the Triple Point: Using Theory as a Link Between Experiments

Understanding How Ligand Functionalization Influences CO2 and N-2 Adsorption in a Sodalite Metal-Organic Framework

Direct path integral estimators for isotope fractionation ratios