15 citations · 38 across the 10 of their papers we have counts for
11 papers
Journal Research Data Policies in Materials Science
Lukas Hörmann, Hemanadhan Myneni, Rwayda Kh. S. Al-Hamd +17
Open and reproducible research in materials science relies on the availability of data, code, and common metadata standards. Journal research data policies (RDPs) remain a primary…
Tracking the Lithiation State of LiSi from Machine-Learned XPS Binding Energies
Michael Alejandro Hernandez Bertran, Davide Tisi, Federico Grasselli +3
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 h…
Long-range electrostatics in atomistic machine learning: a physical perspective
Federico Grasselli, Kevin Rossi, Stefano de Gironcoli +1
The inclusion of long-range electrostatics in atomistic machine learning (ML) is receiving increasing attention for achieving quantum-mechanical accuracy in predicting a wide range…
Resolving the Body-Order Paradox of Machine Learning Interatomic Potentials
Sanggyu Chong, Tong Jiang, Michelangelo Domina +4
In many cases, the predictions of machine learning interatomic potentials (MLIPs) can be interpreted as a sum of body-ordered contributions, which is explicit when the model is dir…
Machine learning model for efficient nonthermal tuning of the charge density wave in monolayer NbSe
Luka Benić, Federico Grasselli, Chiheb Ben Mahmoud +2
Understanding and controlling the charge density wave (CDW) phase diagram of transition metal dichalcogenides is a long-studied problem in condensed matter physics. However, due to…
Uncertainty in the era of machine learning for atomistic modeling
Federico Grasselli, Sanggyu Chong, Venkat Kapil +2
The widespread adoption of machine learning surrogate models has significantly improved the scale and complexity of systems and processes that can be explored accurately and effici…
Prediction rigidities for data-driven chemistry
Sanggyu Chong, Filippo Bigi, Federico Grasselli +3
The widespread application of machine learning (ML) to the chemical sciences is making it very important to understand how the ML models learn to correlate chemical structures with…
Adaptive energy reference for machine-learning models of the electronic density of states
Wei Bin How, Sanggyu Chong, Federico Grasselli +2
The electronic density of states (DOS) provides information regarding the distribution of electronic energy levels in a material, and can be used to approximate its optical and ele…
A prediction rigidity formalism for low-cost uncertainties in trained neural networks
Filippo Bigi, Sanggyu Chong, Michele Ceriotti +1
Regression methods are fundamental for scientific and technological applications. However, fitted models can be highly unreliable outside of their training domain, and hence the qu…
Thermal conductivity of LiPS solid electrolytes with ab initio accuracy
Davide Tisi, Federico Grasselli, Lorenzo Gigli +1
The vast amount of computational studies on electrical conduction in solid-state electrolytes is not mirrored by comparable efforts addressing thermal conduction, which has been sc…
Mechanism of charge transport in lithium thiophosphate
Lorenzo Gigli, Davide Tisi, Federico Grasselli +1
Lithium ortho-thiophosphate (LiPS) has emerged as a promising candidate for solid-state-electrolyte batteries, thanks to its highly conductive phases, cheap components, and…