Michele Ceriotti

Artificial Intelligence for Advanced Functional Materials: Progress and Emerging Frontiers

Cristiano Malica, Kostya S. Novoselov, Seong-Min Kim, Yousung Jung et autres

Artificial intelligence (AI) is transforming the way materials are designed, understood, and manufactured. This Perspective examines how recent advances in data‐driven modeling, high‐performance simulation, and autonomous experimentation are converging to accelerate the discovery of functional materials for next‐generation technologies—from energy storage and …

de, sg, Soudan, kr, fi, ch, us, es, gb, be, nl, fr, Maroc, it, se, dk (code pays fourni par la source)

Replication Data for: How to Train a Shallow Ensemble

Moritz R. Schäfer, Matthias Kellner, Johannes Kästner, Michele Ceriotti

This repository accompanies our paper on "How to Train a Shallow Ensemble". It includes workflows and input files needed to reproduce the results. Most of the experiments are contained in the directory "0_convergence_nll_training". There is a separate directory for the experiments performed …

de, ch (code pays fourni par la source)

How unconstrained machine-learning models learn physical symmetries

Michelangelo Domina, Joseph W. Abbott, Paolo Pegolo, Filippo Bigi et autres

The requirement of generating predictions that exactly fulfill the fundamental symmetry of the corresponding physical quantities has profoundly shaped the development of machine-learning models for physical simulations.In many cases, models are built using constrained mathematical forms that ensure that symmetries are enforced …

ch (code pays fourni par la source)

How unconstrained machine-learning models learn physical symmetries

Michelangelo Domina, Joseph W. Abbott, Paolo Pegolo, Filippo Bigi et autres

The requirement of generating predictions that exactly fulfill the fundamental symmetry of the corresponding physical quantities has profoundly shaped the development of machine-learning models for physical simulations.In many cases, models are built using constrained mathematical forms that ensure that symmetries are enforced …

ch (code pays fourni par la source)

Editorial: Publishing Physical Sciences in the Era of AI

Michele Ceriotti, Mario Krenn, Ann B. Lee, Nicola Marzari et autres

We present the vision, scope, and editorial philosophy of , a selective, fully open access journal at the intersection of artificial intelligence, machine learning, and the physical sciences. We reflect on the transformational role of artificial intelligence as an emerging paradigm of …

es, de, us, gb, ca, si (code pays fourni par la source)

How Unconstrained Machine-Learning Models Learn Physical Symmetries

Michelangelo Domina, Joseph W. Abbott, Paolo Pegolo, Filippo Bigi et autres

The requirement of generating predictions that exactly fulfill the fundamental symmetry of the corresponding physical quantities has profoundly shaped the development of machine-learning (ML) models for physical simulations. In many cases, models are built using constrained mathematical forms that ensure that symmetries …

ch (code pays fourni par la source)

Molecular Interfacial Regulators Enable Stable Sulfide Electrolytes for High‐Performance All‐Solid‐State Batteries

Laras Fadillah, Hanna Türk, Weicheng Hua, Leonie Braks et autres

ABSTRACT Interfacial instability remains a critical limitation for sulfide‐based solid electrolytes in all‐solid‐state batteries, where both reductive decomposition at lithium metal and oxidative degradation at high voltage hinder long‐term performance. Here, pyridine and fluorinated pyridine derivatives are introduced as molecular surface regulators …

ch, kr (code pays fourni par la source)

Tracking the lithiation state of LixSi from machine-learned XPS binding energies

Michael A. Hernandez Bertran, Davide Tisi, Federico Grasselli, Michele Ceriotti et autres

X-ray Photoelectron Spectroscopy (XPS) is a powerful technique to probe chemical states and interfacial processes in battery materials, but a quantitative interpretation is often hindered by the complex, heterogeneous microstructures that form during operation and dominate electrochemical cycling. Silicon based anodes represent …

it, ch (code pays fourni par la source)

Tracking the lithiation state of LixSi from machine-learned XPS binding energies

Michael A. Hernandez Bertran, Davide Tisi, Federico Grasselli, Michele Ceriotti et autres

X-ray Photoelectron Spectroscopy (XPS) is a powerful technique to probe chemical states and interfacial processes in battery materials, but a quantitative interpretation is often hindered by the complex, heterogeneous microstructures that form during operation and dominate electrochemical cycling. Silicon based anodes represent …

it, ch (code pays fourni par la source)

Roadmap on Advancements of the FHI-aims Software Package

Joseph W. Abbott, Carlos Mera Acosta, Alaa Akkoush, Alberto Ambrosetti et autres

Abstract Electronic-structure theory is the foundation of the description of materials including multiscale modeling of their properties and functions. Obviously, without sufficient accuracy at the base, reliable predictions are unlikely at any level that follows. The software package FHI-aims has proven to …

ch, co, de, it, se, us, gb, ca, es, jp, nl, fi, at, cn, dk, fr, il, ee, kr (code pays fourni par la source)

Simultaneous learning of static and dynamic charges

Philipp Stärk, Henrik Stooß, Marcel F. Langer, Egor Rumiantsev et autres

Long-range interactions and electric response are essential for accurate modeling of condensed-phase systems, but capturing them efficiently remains a challenge for atomistic machine learning. Traditionally, these two phenomena can be represented by static charges that underlie Coulomb interactions between atoms, and dynamic …

Learning the Action for Long-Time-Step Simulations of Molecular Dynamics

Filippo Bigi, Johannes Spies, Michele Ceriotti

ch (code pays fourni par la source)