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Michele Ceriotti

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Related publications (160)

Page 3 of 8

Learning the electronic density of states in condensed matter

Michele Ceriotti, Andrea Anelli, Chiheb Ben Mahmoud

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 ...

AMER PHYSICAL SOC2020

Electronic Excited States from Physically Constrained Machine Learning

Michele Ceriotti, Jigyasa Nigam, Divya Suman

Data-driven techniques are increasingly used to replace electronic-structure calculations of matter. In this context, a relevant question is whether machine learning (ML) should be applied directly to predict the desired properties or combined explicitly w ...

2024

Local invertibility and sensitivity of atomic structure-feature mappings

Michele Ceriotti, Sergey Pozdnyakov

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 ...

2021

Beyond potentials: Integrated machine learning models for materials

Michele Ceriotti

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 ...

SPRINGER HEIDELBERG2022

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

Anna Fontcuberta i Morral, Cécile Hébert, Michele Ceriotti, Duncan Thomas Lindsay Alexander, Gözde Tütüncüoglu, Nicolas Tappy, Mahdi Zamani, Martin George Friedl, Luca Francaviglia, Giulio Imbalzano, Lea Ghisalberti

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 ...

Wiley2020

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

Michele Ceriotti, Anne-Clémence Corminboeuf, David Mark Wilkins, Bingqing Cheng, Venkat Kapil, Mariana Rossi Carvalho, Riccardo Petraglia, Benjamin Aaron Helfrecht, Robert Horst Meissner

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 ...

ELSEVIER SCIENCE BV2019

Fast evaluation of spherical harmonics with sphericart

Michele Ceriotti, Guillaume André Jean Fraux, Filippo Bigi

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 ...

AIP Publishing2023

Nuclear quantum effects enter the mainstream

Michele Ceriotti

Atomistic simulations of chemical, biological and materials systems have become increasingly precise and predictive owing to the development of accurate and efficient techniques that describe the quantum mechanical behaviour of electrons. Nevertheless, the ...

2018

Uncertainty estimation for molecular dynamics and sampling

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

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

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

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

Barely porous organic cages for hydrogen isotope separation

Michele Ceriotti, Venkat Kapil, Sanggyu Chong

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 ...

American Association for the Advancement of Science2019

PLUMED Tutorials: A collaborative, community-driven learning ecosystem

Michele Ceriotti, Guillaume André Jean Fraux

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 ...

AIP Publishing2025

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

Michele Ceriotti, Jigyasa Nigam, Guillaume André Jean Fraux, Sergey Pozdnyakov

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 ...

AIP Publishing2022

Semiconducting Nanowire-Based Optoelectronic Fibers

Michele Ceriotti, Fabien Sorin, Alexis Gérald Page, Wei Yan, Yunpeng Qu, Dang Tùng Nguyen, Mariana Rossi Carvalho

he recent ability to integrate semiconductor-based optoelectronic functionalities within thin fibers is opening intriguing opportunities for flexible electronics and advanced textiles. The scalable integration of high-quality semiconducting devices within ...

Wiley2017

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

Michele Ceriotti

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 ...

Amer Chemical Soc2017

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

Michele Ceriotti, Federico Grasselli, Kevin Kazuki Huguenin-Dumittan, Sanggyu Chong, Wei Bin How

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 ...

2025

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

Michele Ceriotti, Sandip De

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 ...

2018

Unsupervised machine learning in atomistic simulations, between predictions and understanding

Michele Ceriotti

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 ...

2019

Accelerated chemical science with AI

Michele Ceriotti, Philippe Schwaller

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 ...

Royal Soc Chemistry2023

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

Michele Ceriotti, Bingqing Cheng

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 ...

Amer Chemical Soc2016