112 datasets found

Recently, we introduced a class of molecular representations for kernel-based regression methods — the spectrum of approximated Hamiltonian matrices (SPAᴴM) — that takes...

In recent years, there has been a surge of interest in predicting computed activation barriers, to enable the acceleration of the automated exploration of reaction networks....

One essential ingredient in many machine learning (ML) based methods for atomistic modeling of materials and molecules is the use of locality. While allowing better system-size...

Rotational symmetry plays a central role in physics, providing an elegant framework to describe how the properties of 3D objects – from atoms to the macroscopic scale –...

We present a workflow that traces the path from the bulk structure of a crystalline material to assessing its performance in carbon capture from coal's postcombustion flue...

We present a workflow that traces the path from the bulk structure of a crystalline material to assessing its performance in carbon capture from coal's postcombustion flue...

We present a workflow that traces the path from the bulk structure of a crystalline material to assessing its performance in carbon capture from coal's postcombustion flue...

We present a workflow that traces the path from the bulk structure of a crystalline material to assessing its performance in carbon capture from coal's postcombustion flue...

We present a workflow that traces the path from the bulk structure of a crystalline material to assessing its performance in carbon capture from coal's postcombustion flue...

We present a workflow that traces the path from the bulk structure of a crystalline material to assessing its performance in carbon capture from coal's postcombustion flue...

Data analyses based on linear methods constitute the simplest, most robust, and transparent approaches to the automatic processing of large amounts of data for building...

Data analyses based on linear methods constitute the simplest, most robust, and transparent approaches to the automatic processing of large amounts of data for building...

Reticular chemistry provides materials designers with a practically infinite playground on different length scales. However, the space of all plausible materials for a given...

The chemical versatility of carbon imparts manifold properties to organic compounds, where magnetism remains one of the most desirable but elusive. Polycyclic aromatic...

Knowledge of the oxidation state of a metal centre in a material is essential to understand its properties. Chemists have developed theories to predict the oxidation state based...

Knowledge of the oxidation state of a metal centre in a material is essential to understand its properties. Chemists have developed several theories to predict the oxidation...

The design rules for materials are clear for applications with a single objective. For most applications, however, there are often multiple, sometimes competing objectives where...

Recently, we introduced a class of molecular representations for kernel-based regression methods — the spectrum of approximated Hamiltonian matrices (SPAᴴM) — that takes...

By combining metal nodes and organic linkers, an infinite number of metal organic frameworks (MOFs) can be designed in silico. When making new databases of such hypothetical...

Quantitative evaluation of the thermodynamic properties of materials—most notably their stability, as measured by the free energy—must take into account the role of thermal and...