Pushing the limits of unconstrained machine-learned interatomic potentials

F. Bigi; P. Pegolo; A. Mazitov; J. Schmidt; M. Ceriotti 

Reproducible orchestration of best practices for reaction path optimization with the nudged elastic band

R. Goswami 

Comparing the Latent Features of Universal Machine‐Learning Interatomic Potentials

S. Chorna; D. Tisi; C. Malosso; W. B. How; M. Ceriotti et al. 

Interfacial Atomic and Electronic Structures of LSM/YSZ Thin Films as Models for SOC Air Electrodes

H. Ali; P. König; X. Q. Tran; A. Kaus; H. Türk et al. 

Simulating the Photochemical Birth of the Hydrated Electron in Liquid Water

G. D. Mirón; C. Malosso; S. Di Pino; C. K. Egan; D. Dasgupta et al. 

Two-dimensional RMSD projections for reaction path visualization and validation

R. Goswami 

Adaptive Pruning for Increased Robustness and Reduced Computational Overhead in Gaussian Process Accelerated Saddle Point Searches

R. Goswami; H. Jónsson 

Cover Feature: Adaptive Pruning for Increased Robustness and Reduced Computational Overhead in Gaussian Process Accelerated Saddle Point Searches (ChemPhysChem 4/2026)

R. Goswami; H. Jónsson 

Resolving the body-order paradox of machine learning interatomic potentials

S. Chong; T. Jiang; M. Domina; F. Bigi; F. Grasselli et al. 

metatensor and metatomic: Foundational libraries for interoperable atomistic machine learning

F. Bigi; J. W. Abbott; P. R. Loche; A. Mazitov; D. Tisi et al. 

A benchmark of expert-level academic questions to assess AI capabilities

L. Phan; A. Gatti; N. Li; A. Khoja; R. Kim et al. 

Learning Long-Range Representations with Equivariant Messages

E. Rumiantsev; M. F. Langer; T. E. Sodjargal; M. Ceriotti; P. Loche 

A universal machine learning model for the electronic density of states

W. B. How; P. Febrer; S. Chong; A. Mazitov; F. Bigi et al. 

From Atoms to XYZ: How the atomistic machine learning community shares data

M. Langer 

Molecular Surface Engineering of Sulfide Electrolytes with Enhanced Humidity Tolerance for Robust Lithium Metal All‐Solid‐State Batteries

L. Fadillah; L. Braks; J. Oh; M. Liu; H. C. Türk et al. 

Massive Atomic Diversity: a compact universal dataset for atomistic machine learning

A. Mazitov; S. Chorna; G. Fraux; M. Bercx; G. Pizzi et al. 

Electrospun poly(3-hydroxybutyrate) fibers containing pheophorbide derivatives: Structural, photophysical, and photodynamic properties for anticancer applications

P. M. Tyubaeva; I. A. Varyan; A. B. Mazitov; A. V. Krivandin; A. V. Bolshakova et al. 

PET-MAD as a lightweight universal interatomic potential for advanced materials modeling

A. Mazitov; F. Bigi; M. L. Kellner; P. Pegolo; D. Tisi et al. 

A foundation model for atomistic materials chemistry

I. Batatia; P. Benner; Y. Chiang; A. M. Elena; D. P. Kovács et al. 

An Adapted Similarity Kernel and Generalized Convex Hull for Molecular Crystal Structure Prediction

J. Martin; M. Ceriotti; G. M. Day 

Representing spherical tensors with scalar-based machine-learning models

M. Domina; F. Bigi; P. Pegolo; M. Ceriotti 

Dynamical heterogeneity in supercooled water and its spectroscopic fingerprints

C. Malosso; E. D. Donkor; S. Baroni; A. Hassanali 

Reconstructions and Dynamics of β -Lithium Thiophosphate Surfaces

H. Türk; D. Tisi; M. Ceriotti 

A Deep Learning Model for Chemical Shieldings in Molecular Organic Solids Including Anisotropy

M. Kellner; J. B. Holmes; R. Rodriguez-Madrid; F. Viscosi; Y. Zhang et al. 

General Formalism for Machine-learning Models Based on Multipolar Spherical Harmonics

M. Domina; S. Sanvito 

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

D. Suman; J. Nigam; S. Saade; P. Pegolo; H. Türk et al. 

Lagnet: Better Electron Density Prediction for Lcao-based Data and Drug-like Substances

K. Ushenin; K. Khrabrov; A. Tsypin; A. Ber; E. Rumiantsev et al. 

Effects of colored disorder on the heat conductivity of SiGe alloys from first principles

A. Fiorentino; P. Pegolo; S. Baroni; D. Donadio 

Dielectric Properties of Aqueous Electrolytes at the Nanoscale

M. R. Becker; R. R. Netz; P. Loche; D. J. Bonthuis; D. Mouhanna et al. 

Fast and flexible long-range models for atomistic machine learning

P. R. Loche; K. K. Huguenin-Dumittan; M. Honarmand; Q. Xu; E. Rumiantsev et al. 

PLUMED Tutorials: A collaborative, community-driven learning ecosystem

G. A. Tribello; M. Bonomi; G. Bussi; C. Camilloni; B. I. Armstrong et al. 

Transport coefficients from equilibrium molecular dynamics

P. Pegolo; E. Drigo; F. Grasselli; S. Baroni 

Water cavitation results from the kinetic competition of bulk, surface, and surface-defect nucleation events

P. Loche; M. Kanduč; E. Schneck; R. R. Netz 

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

W. B. How; S. Chong; F. Grasselli; K. K. Huguenin-Dumittan; M. Ceriotti 

Is there a future for ⁴³Ca nuclear magnetic resonance in cement science?

Z. Casar; D. Tisi; S. J. Page; H. C. Greenwell; F. Zunino 

Boon and Bane of Local Solid State Chemistry on the Performance of LSM-Based Solid Oxide Electrolysis Cells

H. C. Türk; X. Q. Tran; P. König; A. Hammud; V. Vibhu et al. 

The dark side of the forces: assessing non-conservative force models for atomistic machine learning

F. Bigi; M. Langer; M. Ceriotti 

Uncertainty in the Era of Machine Learning for Atomistic Modeling

F. Grasselli; S. Chong; V. Kapil; S. Bonfanti; K. Rossi 

Mathematical Analysis and Optimization of Features for Atomistic Machine Learning

Advancing understanding and practical performance of machine learning interatomic potentials

Physically-constrained machine learning models of effective electronic Hamiltonians

An Adapted Similarity Kernel and Generalised Convex Hull for Molecular Crystal Structure Prediction

J. Martin; M. Ceriotti; G. M. Day 

Probing the effects of broken symmetries in machine learning

M. F. Langer; S. N. Pozdnyakov; M. Ceriotti 

Exploring van der Waals materials with high anisotropy: geometrical and optical approaches

A. S. Slavich; G. A. Ermolaev; M. K. Tatmyshevskiy; A. N. Toksumakov; O. G. Matveeva et al. 

A prediction rigidity formalism for low-cost uncertainties in trained neural networks

F. Bigi; S. Chong; M. Ceriotti; F. Grasselli 

Doping position estimation for FeRh-based alloys

E. Rumiantsev; K. Khrabrov; A. Tsypin; N. D. Peresypkin; R. R. Gimaev et al. 

Uncertainty quantification by direct propagation of shallow ensembles

M. Kellner; M. Ceriotti 

Prediction rigidities for data-driven chemistry

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

Expanding density-correlation machine learning representations for anisotropic coarse-grained particles

A. Lin; K. K. Huguenin-Dumittan; Y. C. Cho; J. Nigam; R. K. Cersonsky 

Force field for halide and alkali ions in water based on single-ion and ion-pair thermodynamic properties for a wide range of concentrations

M. Duenas-Herrera; D. J. Bonthuis; P. Loche; R. R. Netz; L. Scalfi 

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

Y. Litman; V. Kapil; Y. M. Feldman; D. Tisi; T. Begušić et al. 

Observation of Transient Prenucleation Species of Calcium Carbonate by DNP-Enhanced NMR

M. Balodis; B. F. Chmelka; Y. Rao; M. L. Kellner; J. Meibom et al. 

Wigner kernels: Body-ordered equivariant machine learning without a basis

F. Bigi; S. N. Pozdnyakov; M. Ceriotti 

Modeling the ferroelectric phase transition in barium titanate with DFT accuracy and converged sampling

L. Gigli; A. Goscinski; M. Ceriotti; G. A. Tribello 

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. 

Electronic Excited States from Physically Constrained Machine Learning

E. Cignoni; D. Suman; J. Nigam; L. Cupellini; B. Mennucci 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 

Completeness of Atomic Structure Representations

J. Nigam; S. N. Pozdnyakov; K. K. Huguenin-Dumittan; M. Ceriotti 

Mechanism of Charge Transport in Lithium Thiophosphate

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

∇²DFT: A Universal Quantum Chemistry Dataset of Drug-Like Molecules and a Benchmark for Neural Network Potentials

K. Khrabrov; A. Ber; A. Tsypin; K. Ushenin; E. Rumiantsev et al. 

Del²dft: a Universal Quantum Chemistry Dataset of Drug-like Molecules and a Benchmark for Neural Network Potentials

K. Khrabrov; A. Ber; A. Tsypin; K. Ushenin; E. Rumiantsev et al. 

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. 

Modelling of metal alloys in realistic conditions with machine learning

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

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 

Topology, Oxidation States, and Charge Transport in Ionic Conductors

P. Pegolo; S. Baroni; F. Grasselli