6 citations · 10 across the 13 of their papers we have counts for
15 papers
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…
Automatic generation of input files with optimised k-point meshes for Quantum Espresso self-consistent field single point total energy calculations
Elena Patyukova, Junwen Yin, Susmita Basak +3
Performing density functional theory (DFT) calculations requires a careful choice of computational parameters to ensure convergence and obtain meaningful results. This represents a…
Cross Learning between Electronic Structure Theories for Unifying Molecular, Surface, and Inorganic Crystal Foundation Force Fields
Ilyes Batatia, Chen Lin, Joseph Hart +5
Creating a single unified interatomic potential capable of attaining ab initio accuracy across all chemistry remains a long-standing challenge in computational chemistry and materi…
Machine Learned Potential for High-Throughput Phonon Calculations of Metal-Organic Frameworks
Alin Marin Elena, Prathami Divakar Kamath, Théo Jaffrelot Inizan +3
Metal-organic frameworks (MOFs) are highly porous and versatile materials studied extensively for applications such as carbon capture and water harvesting. However, computing phono…
Modelling Silica using MACE-MP-0 Machine Learnt Interatomic Potentials
Jamal Abdul Nasir, Jingcheng Guan, Woongkyu Jee +4
Silica polymorphs and zeolites are fundamental to a wide range of industrial applications owing to their diverse structural characteristics, thermodynamic and mechanical stability…