Chemiscope 1.0: interactive exploration of atomistic data from analysis to dissemination

S. Chorna; J. Lála; Q. Xu; R. K. Cersonsky; G. Fraux et al. 

Enhanced climbing image nudged elastic band method with Hessian eigenmode alignment

R. Goswami; M. Gunde; H. Jónsson 

A Tutorial Review of Bayesian Optimization with Gaussian Processes to Accelerate Stationary Point Searches

R. Goswami 

How to Train a Shallow Ensemble

M. Schäfer; M. Kellner; J. Kästner; M. Ceriotti 

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. 

Representing spherical tensors with scalar-based machine-learning models

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

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

J. Martin; M. Ceriotti; G. M. Day 

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 

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. 

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

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

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 

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

Physically-constrained machine learning models of effective electronic Hamiltonians

Advancing understanding and practical performance of machine learning interatomic potentials

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

J. Martin; M. Ceriotti; G. M. Day 

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

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

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. 

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 

Seebeck Coefficient of Ionic Conductors from Bayesian Regression Analysis

E. Drigo; S. Baroni; P. Pegolo 

Unearthing the foundational role of anharmonicity in heat transport in glasses

A. Fiorentino; 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. 

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. 

Topology, Oxidation States, and Charge Transport in Ionic Conductors

P. Pegolo; S. Baroni; F. Grasselli