6 citations · 10 across the 13 of their papers we have counts for
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Electronic Density versus Geometry for Machine-Learned Molecular Absorption Spectra
Siddharth Dhanpal, Peter Elliott, Paolo Emilio Trevisanutto +2
Molecular optical absorption spectroscopy provides a direct probe of electronic structure and is widely used for molecular identification, interpretation of photophysical behaviour…
A strategic roadmap for an atomistic machine-learning ecosystem
Jörg Behler, Michele Ceriotti, Cecilia Clementi +47
Data-driven machine learning (ML) techniques have become an essential tool in many domains of science. Their application to atomistic simulations of matter is particularly widespre…
uMOF: A Universal Database, Benchmark, and Machine Learning Interatomic Potentials for Metal-Organic Frameworks
Théo Jaffrelot Inizan, Prathami Divakar Kamath, Alin Marin Elena +1
Foundation machine learning interatomic potentials (MLIPs) deliver near-ab-initio accuracy at a fraction of the computational cost, yet their promise for Metal-organic Frameworks (…
A budget-dependent crossover between coverage- and response-based training-set selection for machine-learned interatomic potentials
Jia Bi, Alin-Marin Elena
Selecting compact training sets for machine-learned interatomic potentials requires deciding whether to preserve structural diversity or target configurations on which models disag…
Scientific applications of quantum computing: challenges and opportunities
Bruno Camino, C. Richard A. Catlow, John Buckeridge +13
The predictive simulation of molecules and materials has had a broad and significant impact. It nevertheless remains constrained by the cost of accurately treating electronic corre…
Data-driven Design of Metal-Organic Frameworks with Tunable Negative Thermal Expansion
Prathami Divakar Kamath, Francesco Tavani, Alin Marin Elena +6
Materials with negative thermal expansion (NTE) are essential for applications requiring precise control of thermal expansion. Owing to their exceptional chemical tunability, flexi…
Bond, orbital and spin order in d4/d6/d7 perovskite oxides: successes and limitations of foundation interatomic potentials
Swagata Acharya, Dimitar Pashov, Mark van Schilfgaarde +1
Foundation machine-learning interatomic potentials (MLIPs) are rapidly replacing density-functional theory (DFT) for modeling structure and nuclear dynamics, making their fidelity…
Scalar-pathway fidelity improves physical accuracy in short-range equivariant interatomic potentials
Jia Bi, Alin Marin Elena, Samuel Pinilla
Accurate interatomic potentials enable molecular dynamics of materials, molecules, and interfaces beyond density-functional-theory length and time scales. Equivariant neural networ…
Fine-tuning MLIP foundation models: strategies for accuracy and transferability
Tamás Lajos Tompa, Eszter Varga-Umbrich, Ilyes Batatia +3
Adapting machine-learned interatomic potential (MLIP) foundation models to specialised tasks through fine-tuning is an increasingly important practice, yet systematic guidance on w…
High-Pressure Inelastic Neutron Spectroscopy: A true test of Machine-Learned Interatomic Potential energy landscapes
Jeff Armstrong, Adam Jackson, Alin Elena
Machine-learned interatomic potentials (MLIPs) promise to provide near density-functional theory accuracy at a fraction of the computational cost, offering a transformative route t…
MACE-POLAR-1: A Polarisable Electrostatic Foundation Model for Molecular Chemistry
Ilyes Batatia, William J. Baldwin, Domantas Kuryla +10
Accurate modelling of electrostatic interactions and charge transfer is fundamental to computational chemistry, yet most machine learning interatomic potentials (MLIPs) rely on loc…