Computational molecular science
We develop computational methods, artificial intelligence, and open software to understand and accelerate the discovery of colloidal quantum dots, bridging atomistic simulations with data-driven materials design.
Prof. Ivan Infante, Ikerbasque Research Professor
BCMaterials, Basque Center for Materials, Applications and Nanostructures
InfanteLab develops computational nanochemistry, artificial intelligence, and digital platforms for realistic colloidal quantum dots and semiconductor nanocrystals.
Research
We combine atomistic modelling, electronic structure theory, machine learning, and open software to understand and design colloidal quantum dots.
Can machine-learning force fields reach experimental QD sizes and timescales?
How do ligands, oxidation, and under-coordinated sites control losses?
How do composition, strain, and interfaces shape confinement?
How do we make realistic QD simulation reproducible and open?
Reusable science
Open platforms and computational tools developed by the group for model construction, force-field training, excited-state analysis, and large-scale simulations.
Cloud platform for constructing, simulating, visualising, and designing colloidal quantum dots with first-principles simulations and artificial intelligence. It is the public-facing environment for connecting model construction, simulation workflows, and AI-assisted materials discovery.
Unified, modular, engine-agnostic framework for training machine-learning force fields for quantum dots. It supports multiple ML engines, including SchNet, PaiNN, SO3net, FieldSchNet, NequIP, Allegro, and MACE, with workflows for preprocessing, training, inference, benchmarking, and HPC execution.
Python package for building and passivating atomistic quantum-dot models from CIF files. It supports Wulff and spherical cuts, core-shell particles, facet-specific surface energies, coordination-aware ligand passivation, charge-balance logic, and experimental Janus heterostructure workflows.
Post-processing package for electronic structure calculations of finite quantum dots and excited-state analysis.
Automated Forcefield Optimization Extension for analyzing potential energy surfaces and constructing force-field parameters. The toolkit includes multi-XYZ trajectory handling, RDF/ADF, RMSD/RMSF, shell-structure descriptors for nanocrystals, and Monte Carlo force-field parameter optimization.
Lightweight tool for attaching chemical ligands to nanocrystal and quantum-dot surfaces directly from SMILES strings. It uses RDKit-based functional-group detection to anchor ligands on dummy sites, with configurable multi-ligand, multi-pass passivation ratios and spatial distributions.
Team
Current group members working on machine-learning force fields, atomistic quantum dot simulations, electronic structure, and digital platforms.
Principal Investigator
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Former members
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Guests and collaborators
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Selected outputs
A curated selection from Ivan Infante's Scopus publication export dated 9 July 2026. The complete record is available on the publications page.
Llusar, Jordi, El Adel, Abdessamad, De Trizio, Luca, Manna, Liberato, Hens, Zeger, Infante, Ivan
ACS Energy Letters 11(5), 3945-3952
DOIHens, Zeger, Llusar, Jordi, Infante, Ivan
ACS Nano 19(8), 8227-8237
DOIZhu, Dongxu, Llusar, Jordi, Asaithambi, Aswin, Liu, Zheming, Bes, René, Prieur, Damien, Karakkal, Hiba H., Prato, Mirko, Brovelli, Sergio, Saleh, Gabriele, Panda, Satyaprakash, Infante, Ivan, et al.
ACS Nano 19(39), 34807-34818
DOIHoutepen, A.J., Sargent, E.H., Infante, I., Owen, J.S., Green, P.B., Schaller, R.D., Bals, S., Zeiske, Stefan, Stöferle, T., Hens, Z.
Nature Reviews Methods Primers 5(1)
DOILlusar, Jordi, du Fossé, Indy, Hens, Zeger, Houtepen, Arjan, Infante, Ivan
ACS Nano 18(2), 1563-1572
DOIDe Trizio, Luca, Infante, Ivan, Manna, Liberato
Accounts of Chemical Research 56(13), 1815-1825
DOIDümbgen, Kim Corinna, Pascazio, Roberta, van Beek, Bas, Hens, Zeger, Infante, Ivan
Journal of Physical Chemistry A 127(15), 3427-3436
DOICosseddu, Salvatore, Pascazio, Roberta, Giansante, Carlo, Manna, Liberato, Infante, Ivan
Nanoscale 15(16), 7410-7419
DOIUpdates
Our new group website brings together current research programmes, open scientific software, team profiles, and the publication record in one place.
The QuantumDotSpace cloud platform for AI-accelerated quantum dot discovery is now publicly available, enabling collaborative model construction and simulation workflows.
The publication section now uses the Scopus BibTeX export and highlights selected quantum dot papers.
Contact
We welcome collaborations across theory, simulation, synthesis, spectroscopy, device physics, and data-driven materials discovery.