Publications by Michele Ceriotti

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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 ...

Computational studies of organic systems are frequently limited to static pictures that closely align with textbook style presentations of reaction mechanisms and isomerization processes. Of course, in reality chemical systems are dynamic entities where a ...

Graph neural networks (GNN) are very popular methods in machine learning and have been applied very successfully to the prediction of the properties of molecules and materials. First-order GNNs are well known to be incomplete, i.e. there exist graphs that ...

Efficient, physically-inspired descriptors of the structure and composition of molecules and materials play a key role in the application of machine-learning techniques to atomistic simulations. The proliferation of approaches, as well as the fact that eac ...

At the interface with solids, the mobility of liquid molecules tends to be reduced compared with bulk, often resulting in increased local order due to interactions with the surface of the solid. At room temperature, liquids such as water and methanol can f ...

Atomic-scale simulations have progressed tremendously over the past decade, largely thanks to the availability of machine-learning interatomic potentials. These potentials combine the accuracy of electronic structure calculations with the ability to reach ...

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 ...

Searching for novel materials involves identifying potential candidates and selecting those that have desirable properties and facile synthesis. It is relatively easy to generate large numbers of potential candidates, for instance, by computational searche ...

Applying pressure to water reduces the average oxygen-oxygen distance, and facilitates the delocalisation of protons along the hydrogen bond. This pressure-induced delocalisation is further enhanced by the quantum nature of hydrogen nuclei, which is very s ...

Undesirable natural aging (NA) in Al-6xxx delays subsequent artificial aging (AA) but the size, composition, and evolution of clustering are challenging to measure. Here, atomistic details of early-stage clustering in Al-1% Mg-0.6%Si during NA are studied ...

Zeolites are nanoporous alumino-silicate frameworks widely used as catalysts and adsorbents. Even though millions of siliceous networks can be generated by computer-aided searches, no new hypothetical framework has yet been synthesized. The needle-in-a-hay ...

Alloys composed of several elements in roughly equimolar composition, often referred to as high-entropy alloys, have long been of interest for their thermodynamics and peculiar mechanical properties, and more recently for their potential application in cat ...

Most atomistic machine learning (ML) models rely on a locality ansatz and decompose the energy into a sum of short-ranged, atom-centered contributions. This leads to clear limitations when trying to describe problems that are dominated by long-range physic ...

Nuclear quantum effects influence the structure and dynamics of hydrogen-bonded systems, such as water, which impacts their observed properties with widely varying magnitudes. This review highlights the recent significant developments in the experiment, th ...

Traditional atomistic machine learning (ML) models serve as surrogates for quantum mechanical (QM) properties, predicting quantities such as dipole moments and polarizabilities directly from compositions and geometries of atomic configurations. With the emergence of ML approaches to predict the "ingredients" of a QM calculation, such as the ground-state charge density or the effective single-particle Hamiltonian, it has become possible to obtain multiple properties through analytical physics-based operation

Michele Ceriotti

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Chemical physics software

A Bayesian approach to NMR crystal structure determination

Beyond Static Structures: Putting Forth REMD as a Tool to Solve Problems in Computational Organic Chemistry

Incompleteness of graph neural networks for points clouds in three dimensions

The role of feature space in atomistic learning

Thermally-nucleated self-assembly of water and alcohol into stable structures at hydrophobic interfaces

i-PI 3.0: A flexible and efficient framework for advanced atomistic simulations

2020 JCP Emerging Investigator Special Collection

Ab initio thermodynamics of liquid and solid water

Generalized convex hull construction for materials discovery

Ab initio simulation of particle momentum distributions in high-pressure water

Origins of structural and electronic transitions in disordered silicon

Natural aging and vacancy trapping in Al-6xxx

Ranking the synthesizability of hypothetical zeolites with the sorting hat

Modeling high-entropy transition metal alloys with alchemical compression

Fast and flexible long-range models for atomistic machine learning

Applications of machine learning for studying local solvation environments

Nuclear Quantum Effects in Water and Aqueous Systems: Experiment, Theory, and Current Challenges

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

Exploring the Design Space of Machine Learning Models for Quantum Chemistry with a Fully Differentiable Framework