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Zeo-1: A computational data set of zeolite structures
Fast, empirical potentials are gaining increased popularity in the computational fields of materials science, physics and chemistry. With it, there is a rising demand for... -
Data-driven studies of magnetic two-dimensional materials
We use a data-driven approach to study the magnetic and thermodynamic properties of van der Waals (vdW) layered materials. We investigate monolayers of the form A2B2X6, based on... -
Machine learning molecular dynamics simulation of CO-driven formation of Cu c...
The behavior of adsorbate-induced surface transformation can be clearly understood given the mechanical aspects of such phenomenon are well described at the atomic level. In... -
Understanding the diversity of the metal-organic framework ecosystem
By combining metal nodes and organic linkers one can make millions of different metal-organic frameworks (MOFs). At present over 90,000 MOFs have been synthesized and there are... -
Sensitivity benchmarks of structural representations for atomic-scale machine...
This dataset contains three sets of CH4 geometries that are distorted along special directions, to reveal the sensitivity to atomic displacements of structural descriptors used... -
QMrxn20: Thousands of reactants and transition states for competing E2 and SN...
For competing E2 and SN2 reactions, we report 4'400 validated transition state geometries and 143'200 reactant complex geometries including conformers obtained at MP2/6-311G(d)... -
A deep learning model for chemical shieldings in molecular organic solids inc...
Nuclear Magnetic Resonance (NMR) chemical shifts are powerful probes of local atomic and electronic structure that can be used to resolve the structures of powdered or amorphous... -
Finite-temperature materials modeling from the quantum nuclei to the hot elec...
Atomistic simulations provide insights into structure-property relations on an atomic size and length scale that are complementary to the macroscopic observables that can be... -
Sampling enhancement by metadynamics driven by machine learning and de novo p...
Folding of villin miniprotein was studied by parallel tempering metadynamics driven by machine learning. To obtain a training set for machine learning, we generated a large... -
Sampling enhancement by metadynamics driven by machine learning and de novo p...
Folding of villin miniprotein was studied by parallel tempering metadynamics driven by machine learning. To obtain a training set for machine learning, we generated a large... -
Sampling enhancement by metadynamics driven by machine learning and de novo p...
Folding of villin miniprotein was studied by parallel tempering metadynamics driven by machine learning. To obtain a training set for machine learning, we generated a large... -
Teaching oxidation states to neural networks
The accurate description of redox reactions remains a challenge for first-principles calculations, but it has been shown that extended Hubbard functionals (DFT+U+V) can provide... -
Machine learning for metallurgy V: A neural-network potential for zirconium d...
The mechanical performance—including deformation, fracture, and radiation damage—of zirconium is determined at the atomic scale. With Zr and its alloys extensively used in the... -
On the robust extrapolation of high-dimensional machine learning potentials
We show that, contrary to popular assumptions, predictions from machine learning potentials built upon high-dimensional atom-density representations almost exclusively occur in... -
Mechanism of charge transport in lithium thiophosphate
Lithium ortho-thiophosphate (Li₃PS₄) has emerged as a promising candidate for solid-state-electrolyte batteries, thanks to its highly conductive phases, cheap components, and... -
Mechanism of charge transport in lithium thiophosphate
Lithium ortho-thiophosphate (Li₃PS₄) has emerged as a promising candidate for solid-state-electrolyte batteries, thanks to its highly conductive phases, cheap components, and... -
SPAᴴM: the spectrum of approximated hamiltonian matrices representations
Physics-inspired molecular representations are the cornerstone of similarity-based learning applied to solve chemical problems. Despite their conceptual and mathematical... -
Machine learning on multiple topological materials datasets
A dataset of 35,608 materials with their topological properties is constructed by combining the density functional theory (DFT) results of Materiae and the Topological Materials... -
Machine learning on multiple topological materials datasets
A dataset of 35,608 materials with their topological properties is constructed by combining the density functional theory (DFT) results of Materiae and the Topological Materials... -
Deep learning the slow modes for rare events sampling
The development of enhanced sampling methods has greatly extended the scope of atomistic simulations, allowing long-time phenomena to be studied with accessible computational...
