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Michele Ceriotti

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Related publications (162)

A Bayesian approach to NMR crystal structure determination

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

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

Correction to “Second-Harmonic Scattering as a Probe of Structural Correlations in Liquids”

Sylvie Roke, Michele Ceriotti, Gabriele Tocci, Chung-Wen Liang, David Mark Wilkins

2019

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

Atom-density representations for machine learning

Félix Benedito Clément Musil, Michele Ceriotti, Michael John Willatt

This has led to a proliferation of alternative ways to convert an atomic structure into an input for a machine-learning model. We introduce an abstract definition of chemical environments that is based on a smoothed atomic density, using a bra-ket notation ...

2019

Electron density learning of non-covalent systems

Anne-Clémence Corminboeuf, Alberto Fabrizio, Michele Ceriotti, Andrea Grisafi, Benjamin André René Meyer

Chemists continuously harvest the power of non-covalent interactions to control phenomena in both the micro- and macroscopic worlds. From the quantum chemical perspective, the strategies essentially rely upon an in-depth understanding of the physical origi ...

2019

Assessment of Approximate Methods for Anharmonic Free Energies

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

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

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

Determination and evaluation of the nonadditivity in wetting of molecularly heterogeneous surfaces

Francesco Stellacci, Quy Ong Khac, Sylvie Roke, Zhi Luo, Michele Ceriotti, Filip Kovacik, David Mark Wilkins, Halil Ibrahim Okur, Anna Murello

The interface between water and folded proteins is very complex. Proteins have "patchy" solvent-accessible areas composed of domains of varying hydrophobicity. The textbook understanding is that these domains contribute additively to interfacial properties ...

National Academy of Sciences2019

Barely porous organic cages for hydrogen isotope separation

Michele Ceriotti, Venkat Kapil, Sanggyu Chong, Ming Liu

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

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

Anne-Clémence Corminboeuf, Riccardo Petraglia, Sébastien Pierre Bienvenue, Michele Ceriotti, Bingqing Cheng, David Mark Wilkins, Przemyslaw Juda, Benjamin Aaron Helfrecht, Venkat Kapil, Mariana Rossi Carvalho, Robert Horst Meissner, Alice Cuzzocrea, Gareth Aneurin Tribello, Wei Fang

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

Incorporating long-range physics in atomic-scale machine learning

Michele Ceriotti, Andrea Grisafi

The most successful and popular machine learning models of atomic-scale properties derive their transferability from a locality ansatz. The properties of a large molecule or a bulk material are written as a sum over contributions that depend on the configu ...

2019

Accurate molecular polarizabilities with coupled cluster theory and machine learning

Michele Ceriotti, David Mark Wilkins, Andrea Grisafi, Yang Yang

The molecular dipole polarizability describes the tendency of a molecule to change its dipole moment in response to an applied electric field. This quantity governs key intra- and intermolecular interactions, such as induction and dispersion; plays a vital ...

2019

A new kind of atlas of zeolite building blocks

Michele Ceriotti, Benjamin Aaron Helfrecht, Rocio Semino Barbaresi, Giovanni Pireddu

We have analyzed structural motifs in the Deem database of hypothetical zeolites to investigate whether the structural diversity found in this database can be well-represented by classical descriptors, such as distances, angles, and ring sizes, or whether ...

2019

Using Gaussian process regression to simulate the vibrational Raman spectra of molecular crystals

Michele Ceriotti, Mariana Rossi Carvalho, Andrea Grisafi

Vibrational properties of molecular crystals are constantly used as structural fingerprints, in order to identify both the chemical nature and the structural arrangement of molecules. The simulation of these properties is typically very costly, especially ...

2019

Modeling the Structural and Thermal Properties of Loaded Metal–Organic Frameworks. An Interplay of Quantum and Anharmonic Fluctuations

Michele Ceriotti, Venkat Kapil

Metal–organic frameworks show both fundamental interest and great promise for applications in adsorption-based technologies, such as the separation and storage of gases. The flexibility and complexity of the molecular scaffold pose a considerable challenge ...

2019

Energy Relaxation and Thermal Diffusion in Infrared Pump-Probe Spectroscopy of Hydrogen-Bonded Liquids

Michele Ceriotti

Infrared pump-probe spectroscopy provides detailed information about the dynamics of hydrogen-bonded liquids. Due to dissipation of the absorbed pump pulse energy, thermal equilibration dynamics also contributes to the observed signal. Disentangling this c ...

AMER CHEMICAL SOC2019

Machine Learning at the Atomic Scale

Félix Benedito Clément Musil, Michele Ceriotti

Statistical learning algorithms are finding more and more applications in science and technology. Atomic-scale modeling is no exception, with machine learning becoming commonplace as a tool to predict energy, forces and properties of molecules and condense ...

SWISS CHEMICAL SOC2019

Learning (from/about) the ground-state electron density

Anne-Clémence Corminboeuf, Alberto Fabrizio, Michele Ceriotti, David Mark Wilkins, Andrea Grisafi, Benjamin André René Meyer

AMER CHEMICAL SOC2019

Fast and Accurate Uncertainty Estimation in Chemical Machine Learning

Félix Benedito Clément Musil, Michele Ceriotti, Mikhail Langovoy, Michael John Willatt

We present a scheme to obtain an inexpensive and reliable estimate of the uncertainty associated with the predictions of a machine-learning model of atomic and molecular properties. The scheme is based on resampling, with multiple models being generated ba ...

2019

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