Prediction rigidities for data-driven chemistry

S. Chong; F. Bigi; F. Grasselli; P. R. Loche; M. L. Kellner et al. 

Thermal conductivity of Li 3 PS 4 solid electrolytes with ab initio accuracy

D. Tisi; F. Grasselli; L. Gigli; M. Ceriotti 

Unearthing the foundational role of anharmonicity in heat transport in glasses

A. Fiorentino; E. Drigo; S. Baroni; P. Pegolo 

Seebeck Coefficient of Ionic Conductors from Bayesian Regression Analysis

E. Drigo; S. Baroni; P. Pegolo 

Excited State-Specific CASSCF Theory for the Torsion of Ethylene

S. Saade; H. G. A. Burton 

Surface segregation in high-entropy alloys from alchemical machine learning

A. Mazitov; M. A. Springer; N. Lopanitsyna; G. Fraux; S. De et al. 

Wandering principal optical axes in van der Waals triclinic materials

G. A. Ermolaev; K. V. Voronin; A. N. Toksumakov; D. V. Grudinin; I. M. Fradkin et al. 

Thermal transport of glasses via machine learning driven simulations

P. Pegolo; F. Grasselli 

Integrating symmetry and physical constraints into atomic-scale machine learning

Efficient and insightful descriptors for representing molecular and material space

Natural aging and vacancy trapping in Al-6xxx

A. C. P. Jain; M. Ceriotti; W. A. Curtin 

Accelerated chemical science with AI

S. Back; A. Aspuru-Guzik; M. Ceriotti; G. Gryn’ova; B. Grzybowski et al. 

Revealing the Formation Dynamics of Janus Polymer Particles: Insights from Experiments and Molecular Dynamics

M. Nedyalkova; G. Russo; P. R. Loche; M. Lattuada 

Divide-and-conquer potentials enable scalable and accurate predictions of forces and energies in atomistic systems

C. Zeni; A. Anelli; A. Glielmo; S. de Gironcoli; K. Rossi 

Robustness of Local Predictions in Atomistic Machine Learning Models

S. Chong; F. Grasselli; C. Ben Mahmoud; J. D. Morrow; V. L. Deringer et al. 

Accuracy Assessment of Atomistic Neural Network Potentials: The Impact of Cutoff Radius and Message Passing

J. Xia; Y. Zhang; B. Jiang 

Physics-Inspired Equivariant Descriptors of Nonbonded Interactions

K. K. Huguenin-Dumittan; P. R. Loche; N. Haoran; M. Ceriotti 

Universal machine learning for the response of atomistic systems to external fields

Y. Zhang; B. Jiang 

Self-interaction and transport of solvated electrons in molten salts

P. Pegolo; S. Baroni; F. Grasselli 

van der Waals Materials for Overcoming Fundamental Limitations in Photonic Integrated Circuitry

A. A. Vyshnevyy; G. A. Ermolaev; D. V. Grudinin; K. V. Voronin; I. Kharichkin et al. 

Fast evaluation of spherical harmonics with sphericart

F. Bigi; G. Fraux; N. J. Browning; M. Ceriotti 

Effect of a temperature gradient on the screening properties of ionic fluids

A. Grisafi; F. Grasselli 

Modeling high-entropy transition metal alloys with alchemical compression

N. Lopanitsyna; G. Fraux; M. A. Springer; S. De; M. Ceriotti 

A data-driven interpretation of the stability of organic molecular crystals

R. K. Cersonsky; M. Pakhnova; E. A. Engel; M. Ceriotti 

Multiscale Modeling of Aqueous Electric Double Layers

M. Becker; P. R. Loche; M. Rezaei; A. Wolde-Kidan; Y. Uematsu et al. 

Machine-learning the electronic density of states: electronic properties without quantum mechanics

Modelling of metal alloys in realistic conditions with machine learning

Electrokinetic, electrochemical, and electrostatic surface potentials of the pristine water liquid-vapor interface

M. R. Becker; P. Loche; R. R. Netz 

A smooth basis for atomistic machine learning

F. Bigi; K. K. Huguenin-Dumittan; M. Ceriotti; D. E. Manolopoulos 

Beyond potentials: Integrated machine learning models for materials

M. Ceriotti 

Incompleteness of graph neural networks for points clouds in three dimensions

S. N. Pozdnyakov; M. Ceriotti 

Comment on “Manifolds of quasi-constant SOAP and ACSF fingerprints and the resulting failure to machine learn four-body interactions” [J. Chem. Phys. 156, 034302 (2022)]

S. N. N. Pozdnyakov; M. J. J. Willatt; A. P. P. Bartok; C. Ortner; G. Csanyi et al. 

Thermodynamics and dielectric response of BaTiO3 by data-driven modeling

L. Gigli; M. Veit; M. Kotiuga; G. Pizzi; N. Marzari et al. 

Predicting hot-electron free energies from ground-state data

C. Ben Mahmoud; F. Grasselli; M. Ceriotti 

A Machine Learning Model of Chemical Shifts for Chemically and Structurally Diverse Molecular Solids

M. Cordova; E. A. Engel; A. Stefaniuk; F. Paruzzo; A. Hofstetter et al. 

Effects of surface rigidity and metallicity on dielectric properties and ion interactions at aqueous hydrophobic interfaces

P. Loche; L. Scalfi; M. A. Amu; O. Schullian; D. J. Bonthuis et al. 

Roadmap on Machine learning in electronic structure

H. J. Kulik; T. Hammerschmidt; J. Schmidt; S. Botti; M. A. L. Marques et al. 

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

J. Nigam; S. Pozdnyakov; G. Fraux; M. Ceriotti 

Molecular dynamics simulations of the evaporation of hydrated ions from aqueous solution

P. Loche; D. J. Bonthuis; R. R. Netz 

Investigating finite-size effects in molecular dynamics simulations of ion diffusion, heat transport, and thermal motion in superionic materials

F. Grasselli 

A complete description of thermodynamic stabilities of molecular crystals

V. Kapil; E. A. Engel 

Temperature- and vacancy-concentration-dependence of heat transport in Li3ClO from multi-method numerical simulations

P. Pegolo; S. Baroni; F. Grasselli 

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

J. Nigam; M. J. Willatt; M. Ceriotti 

A route to hierarchical assembly of colloidal diamond

Y. Zhou; R. K. Cersonsky; S. C. Glotzer 

Ranking the synthesizability of hypothetical zeolites with the sorting hat

B. A. Helfrecht; G. Pireddu; R. Semino; S. Auerbach; M. Ceriotti 

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

R. Fabregat; A. Fabrizio; E. A. Engel; B. Meyer; V. Juraskova et al. 

Topology, Oxidation States, and Charge Transport in Ionic Conductors

P. Pegolo; S. Baroni; F. Grasselli 

Characterization and prediction of peptide structures on inorganic surfaces

2020 JCP Emerging Investigator Special Collection

M. Ceriotti; L. Jensen; D. E. Manolopoulos; T. J. Martinez; A. Michaelides et al. 

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

B. Cheng; G. Mazzola; C. J. Pickard; M. Ceriotti 

Learning Electron Densities in the Condensed Phase

A. M. Lewis; A. Grisafi; M. Ceriotti; M. Rossi 

Bayesian probabilistic assignment of chemical shifts in organic solids

M. Cordova; M. Balodis; B. S. de Almeida; M. Ceriotti; L. Emsley 

Optimal radial basis for density-based atomic representations

A. Goscinski; F. Musil; S. Pozdnyakov; J. Nigam; M. Ceriotti 

Improving sample and feature selection with principal covariates regression

R. K. Cersonsky; B. A. Helfrecht; E. A. Engel; S. Kliavinek; M. Ceriotti 

Introduction: Machine Learning at the Atomic Scale

M. Ceriotti; C. Clementi; O. A. von Lilienfeld 

Importance of Nuclear Quantum Effects for NMR Crystallography

E. A. Engel; V. Kapil; M. Ceriotti 

Invariance principles in the theory and computation of transport coefficients

F. Grasselli; S. Baroni 

Quantum vibronic effects on the electronic properties of solid and molecular carbon

A. Kundu; M. Govoni; H. Yang; M. Ceriotti; F. Gygi et al. 

Chemical physics software

C. D. Sherrill; D. E. Manolopoulos; T. J. Martinez; M. Ceriotti; A. Michaelides 

Modeling the Ga/As binary system across temperatures and compositions from first principles

G. Imbalzano; M. Ceriotti 

Global Free-Energy Landscapes as a Smoothly Joined Collection of Local Maps

F. Giberti; G. A. Tribello; M. Ceriotti 

The role of feature space in atomistic learning

A. Goscinski; G. Fraux; G. Imbalzano; M. Ceriotti 

Machine learning for metallurgy III: A neural network potential for Al-Mg-Si

A. C. P. Jain; D. Marchand; A. Glensk; M. Ceriotti; W. A. Curtin 

Machine learning meets chemical physics

M. Ceriotti; C. Clementi; O. Anatole von Lilienfeld 

Origins of structural and electronic transitions in disordered silicon

V. L. Deringer; N. Bernstein; G. Csányi; C. Ben Mahmoud; M. Ceriotti et al. 

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

N. Lopanitsyna; C. Ben Mahmoud; M. Ceriotti 

Multi-scale approach for the prediction of atomic scale properties

A. Grisafi; J. Nigam; M. Ceriotti 

Local invertibility and sensitivity of atomic structure-feature mappings

S. N. Pozdnyakov; L. Zhang; C. Ortner; G. Csányi; M. Ceriotti 

Uncertainty estimation for molecular dynamics and sampling

G. Imbalzano; Y. Zhuang; V. Kapil; K. Rossi; E. A. Engel et al. 

Simulating the ghost: quantum dynamics of the solvated electron

J. Lan; V. Kapil; P. Gasparotto; M. Ceriotti; M. Iannuzzi et al. 

Efficient implementation of atom-density representations

F. Musil; M. Veit; A. Goscinski; G. Fraux; M. J. Willatt et al. 

Gaussian Process Regression for Materials and Molecules

V. L. Deringer; A. P. Bartok; N. Bernstein; D. M. Wilkins; M. Ceriotti et al. 

Physics-Inspired Structural Representations for Molecules and Materials

F. Musil; A. Grisafi; A. P. Bartok; C. Ortner; G. Csanyi et al. 

Structure-Property Relationships in Complex Materials by Combining Supervised and Unsupervised Machine Learning

A general and efficient framework for atomistic machine learning

Transferable machine-learning models of complex materials: the case of GaAs

Physics-enhanced machine learning with symmetry-adapted and long-range representations

Learning the electronic density of states in condensed matter

C. Ben Mahmoud; A. Anelli; G. Csanyi; M. Ceriotti 

Oxidation States, Thouless’ Pumps, and Nontrivial Ionic Transport in Nonstoichiometric Electrolytes

P. Pegolo; F. Grasselli; S. Baroni 

Incompleteness of Atomic Structure Representations

S. N. Pozdnyakov; M. J. Willatt; A. P. Bartok; C. Ortner; G. Csanyi et al. 

Gas-sieving zeolitic membranes fabricated by condensation of precursor nanosheets

M. Dakhchoune; L. F. Villalobos; R. Semino; L. Liu; M. Rezaei et al. 

DUBS: A Framework for Developing Directory of Useful Benchmarking Sets for Virtual Screening

J. Fine; M. Muhoberac; G. Fraux; G. Chopra 

Simulating Solvation and Acidity in Complex Mixtures with First-Principles Accuracy: The Case of CH3SO3H and H2O2 in Phenol

K. Rossi; V. Juraskova; R. Wischert; L. Garel; C. Corminboeuf et al. 

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

M. Zamani; G. Imbalzano; N. Tappy; D. T. L. Alexander; S. Marti-Sanchez et al. 

Heat and charge transport in H2O at ice-giant conditions from ab initio molecular dynamics simulations

F. Grasselli; L. Stixrude; S. Baroni 

Predicting molecular dipole moments by combining atomic partial charges and atomic dipoles

M. Veit; D. M. Wilkins; Y. Yang; R. A. DiStasio; M. Ceriotti 

Quantum kinetic energy and isotope fractionation in aqueous ionic solutions

L. Wang; M. Ceriotti; T. E. Markland 

Inexpensive modeling of quantum dynamics using path integral generalized Langevin equation thermostats

V. Kapil; D. M. Wilkins; J. Lan; M. Ceriotti 

Structural Screening and Design of Platinum Nanosamples for Oxygen Reduction

K. Rossi; G. G. Asara; F. Baletto 

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

M. Asgari; R. Semino; P. A. Schouwink; I. Kochetygov; J. Tarver et al. 

Accurate Description of Nuclear Quantum Effects with High-Order Perturbed Path Integrals (HOPPI)

I. Poltaysky; V. Kapil; M. Ceriotti; K. S. Kim; A. Tkatchenko 

Classical nucleation theory predicts the shape of the nucleus in homogeneous solidification

B. Cheng; M. Ceriotti; G. A. Tribello 

Identifying and Tracking Defects in Dynamic Supramolecular Polymers

P. Gasparotto; D. Bochicchio; M. Ceriotti; G. M. Pavan 

Machine Learning-Guided Approach for Studying Solvation Environments

Y. Basdogan; M. C. Groenenboom; E. Henderson; S. De; S. B. Rempe et al. 

Iterative Unbiasing of Quasi-Equilibrium Sampling

F. Giberti; B. Cheng; G. A. Tribello; M. Ceriotti 

Recursive evaluation and iterative contraction of N-body equivariant features

J. Nigam; S. Pozdnyakov; M. Ceriotti 

Chemiscope: interactive structure-property explorer for materials and molecules

G. Fraux; R. Cersonsky; M. Ceriotti 

Machine Learning Force Fields and Coarse-Grained Variables in Molecular Dynamics: Application to Materials and Biological Systems

P. Gkeka; G. Stoltz; A. B. Farimani; Z. Belkacemi; M. Ceriotti et al. 

Atomistic modeling of the solid-liquid interface of metals and alloys

Nuclear Quantum Effects: Fast and Accurate