Publications by Michele Ceriotti

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The synthesis of molecular-sieving zeolitic membranes by the assembly of building blocks, avoiding the hydrothermal treatment, is highly desired to improve reproducibility and scalability. Here we report exfoliation of the sodalite precursor RUB-15 into cr ...

The input of almost every machine learning algorithm targeting the properties of matter at the atomic scale involves a transformation of the list of Cartesian atomic coordinates into a more symmetric representation. Many of the most popular representations ...

Evaluating the (dis)similarity of crystalline, disordered and molecular compounds is a critical step in the development of algorithms to navigate automatically the configuration space of complex materials. For instance, a structural similarity metric is cr ...

The hydrogen-bond network of water is characterized by the presence of coordination defects relative to the ideal tetrahedral network of ice, whose fluctuations determine the static and time-dependent properties of the liquid. Because of topological constr ...

Atomistic simulations provide insights into structure-property relations on an atomic size and length scale, that are complementary to the macroscopic observables that can be obtained from experiments. Quantitative predictions, however, are usually hindere ...

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 ...

Path integral methods provide a rigorous and systematically convergent framework to include the quantum mechanical nature of atomic nuclei in the evaluation of the equilibrium properties of molecules, liquids, or solids at finite temperature. Such nuclear ...

Symmetry considerations are at the core of the major frameworks used to provide an effective mathematical representation of atomic configurations that is then used in machine-learning models to predict the properties associated with each structure. In most ...

Determining the stability ofmolecules and condensed phases is the cornerstone of atomisticmodeling, underpinning our understanding of chemical andmaterials properties and transformations. We show that amachine-learningmodel, based on a local description of ...

Lithium ortho-thiophosphate (Li3PS4) has emerged as a promising candidate for solid-state electrolyte batteries, thanks to its highly conductive phases, cheap components, and large electrochemical stability range. Nonetheless, the microscopic mechanisms of ...

Electrolytes interact with water in many ways: changing dipole orientation, inducing charge transfer, and distorting the hydrogen-bond network in the bulk and at interfaces. Numerous experiments and computations have detected short-range perturbations that ...

Over recent years, the use of statistical learning techniques applied to chemical problems has gained substantial momentum. This is particularly apparent in the realm of physical chemistry, where the balance between empiricism and physics-based theory has ...

Molecular crystals often exist in multiple competing polymorphs, showing significantly different physicochemical properties. Computational crystal structure prediction is key to interpret and guide the search for the most stable or useful form, a real chal ...

Many-body descriptors are widely used to represent atomic environments in the construction of machine-learned interatomic potentials and more broadly for fitting, classification, and embedding tasks on atomic structures. There is a widespread belief in the ...

High-throughput computational materials design promises to greatly accelerate the process of discovering new materials and compounds, and of optimizing their properties. The large databases of structures and properties that result from computational search ...

We introduce a local machine-learning method for predicting the electron densities of periodic systems. The framework is based on a numerical, atom-centered auxiliary basis, which enables an accurate expansion of the all-electron density in a form suitable ...

The widespread application of machine learning (ML) to the chemical sciences is making it very important to understand how the ML models learn to correlate chemical structures with their properties, and what can be done to improve the training efficiency w ...

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 ...

Interfaces between different materials and phases play a crucial role in many physical and chemical phenomena. When performing simulations of matter at the atomic scale, however, it is often not trivial to characterize these interfaces, particularly when t ...

Michele Ceriotti

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Gas-sieving zeolitic membranes fabricated by condensation of precursor nanosheets

Optimal radial basis for density-based atomic representations

Comparing molecules and solids across structural and alchemical space

Probing Defects and Correlations in the Hydrogen-Bond Network of ab Initio Water

Finite-temperature materials modeling from the quantum nuclei to the hot electron regime

Electron density learning of non-covalent systems

Accelerated path integral methods for atomistic simulations at ultra-low temperatures

Equivariant representations for molecular Hamiltonians and N-center atomic-scale properties

Machine learning unifies the modeling of materials and molecules

Mechanism of Charge Transport in Lithium Thiophosphate

Electrolytes induce long-range orientational order and free energy changes in the H-bond network of bulk water

Machine learning meets chemical physics

Anharmonic and Quantum Fluctuations in Molecular Crystals: A First-Principles Study of the Stability of Paracetamol

Incompleteness of Atomic Structure Representations

Mapping and classifying molecules from a high-throughput structural database

Learning Electron Densities in the Condensed Phase

Prediction rigidities for data-driven chemistry

Reply to: On the liquid-liquid phase transition of dense hydrogen

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

Extracting the interfacial free energy and anisotropy from a smooth fluctuating dividing surface