23 citations · 30 across the 13 of their papers we have counts for
16 papers
Pressure-transferable universal machine-learning potentials: the WBM-MatPES benchmark and Tuga-FF
Pierre-Paul De Breuck, Thalis H. B. da Silva, Antoine Loew +2
Universal machine-learning interatomic potentials hold the promise to replace costly ab initio calculations in materials science workflows. However, the accuracy and transferabilit…
High-throughput computational discovery of CuI-based ternary -type transparent conductors
Michael Seifert, Miguel A. L. Marques, Silvana Botti
P-type transparent conducting materials (TCMs) remain scarce, limiting progress in transparent electronics and tandem photovoltaics. Here, we perform a high-throughput computationa…
Intrinsic Point Defects and Frenkel Pair Formation in Photovoltaic Absorber ZnP: Regulating -type Conductivity through Growth and Annealing Conditions
Nico Kawashima, Silvana Botti
This study investigates the ground-state energetics and thermodynamics of intrinsic point defects in zinc phosphide ZnP using \emph{ab initio} density functional theory com…
Cathodoluminescence Analysis of Defects and Grain Boundaries in Zn3P2 Thin Films Grown on Graphene by MOVPE and MBE
Thomas Hagger, Mohammadreza Hassanzadeh, Aidas Urbonavicius +12
Zn3P2 is a promising earth-abundant absorber for thin-film photovoltaics, yet its development is hindered by the lack of lattice-matched substrates, its incompatible thermal expans…
Functional and Density-Driven Errors in Density Functional Theory: Quantum Monte Carlo Benchmarks for Solids
Ayoub Aouina, Nicolas Tancogne-Dejean, Silvana Botti
We introduce a systematic analysis of density functional approximation errors in solids by separating functional-driven from density-driven contributions using quantum Monte Carlo…
Optical selection rules in hexagonal Ge polytypes and their lifting by symmetry perturbation
Martin Keller, Haichen Wang, Friedhelm Bechstedt +2
Hexagonal germanium polytypes have emerged as promising direct-gap semiconductors for silicon-integrated optoelectronics, yet their optical properties remain largely unexplored bey…
High-throughput study of electrical conductivity in ordered metals
Thalis H. B. da Silva, Hai-Chen Wang, Tiago F. T. Cerqueira +3
We present a computational framework that integrates machine learning with high-throughput ab initio calculations to screen over 2.8 million compounds for metallic transport. We id…
AI-Driven Expansion and Application of the Alexandria Database
Théo Cavignac, Jonathan Schmidt, Pierre-Paul De Breuck +9
We present a novel multi-stage workflow for computational materials discovery that achieves a 99% success rate in identifying compounds within 100 meV/atom of thermodynamic stabili…
Generative AI for Crystal Structures: A Review
Pierre-Paul De Breuck, Hai-Chen Wang, Gian-Marco Rignanese +2
As in many other fields, the rapid rise of generative artificial intelligence is reshaping materials discovery by offering new ways to propose crystal structures and, in some cases…
Universal Machine Learning Potentials under Pressure
Antoine Loew, Jonathan Schmidt, Silvana Botti +1
Universal machine learning interatomic potentials (uMLIPs) represent arguably the most successful application of machine learning to materials science, demonstrating remarkable per…
Universal Machine Learning Potential for Systems with Reduced Dimensionality
Giulio Benedini, Antoine Loew, Matti Hellstrom +2
We present a benchmark designed to evaluate the predictive capabilities of universal machine learning interatomic potentials across systems of varying dimensionality. Specifically,…
MACE4IRmol: An uncertainty-aware foundation model for molecular infrared spectroscopy
Nitik Bhatia, Ondrej Krejci, Silvana Botti +2
Machine-learned interatomic potentials (MLIPs) have shown significant promise in predicting infrared spectra with high fidelity. However, the absence of general-purpose MLIPs that…
Enhanced superconductivity in X4H15compounds via hole-doping at ambient pressure
Kun Gao, Wenwen Cui, Tiago F. T. Cerqueira +3
This study presents a computational investigation of X4H15 compounds (where X represents a metal) as potential superconductors at ambient conditions or under pressure. Through syst…
The Maximum of Conventional Superconductors at Ambient Pressure
Kun Gao, Tiago F. T. Cerqueira, Antonio Sanna +6
The theoretical maximum critical temperature () for conventional superconductors at ambient pressure remains a fundamental question in condensed matter physics. Through analys…
Universal Machine Learning Interatomic Potentials are Ready for Phonons
Antoine Loew, Dewen Sun, Hai-Chen Wang +2
There has been an ongoing race for the past several years to develop the best universal machinelearning interatomic potential. This progress has led to increasingly accurate models…
A non-orthogonal representation of the chemical space
Tiago F. T. Cerqueira, Haichen Wang, Silvana Botti +1
We present a novel approach to generate a fingerprint for crystalline materials that balances efficiency for machine processing and human interpretability, allowing its application…