Tailor‐Engineered 2D Cocatalysts: Harnessing Electron–Hole Redox Center of 2D g‐C3N4 Photocatalysts toward Solar‐to‐Chemical Conversion and …
Sparked by natural photosynthesis, solar photocatalysis using metal‐free graphitic carbon
nitride (g‐C3N4) with appealing electronic structure has turned up as the most captivating …
nitride (g‐C3N4) with appealing electronic structure has turned up as the most captivating …
Computational discovery of transition-metal complexes: from high-throughput screening to machine learning
Transition-metal complexes are attractive targets for the design of catalysts and functional
materials. The behavior of the metal–organic bond, while very tunable for achieving target …
materials. The behavior of the metal–organic bond, while very tunable for achieving target …
New strategies for direct methane-to-methanol conversion from active learning exploration of 16 million catalysts
Despite decades of effort, no earth-abundant homogeneous catalysts have been discovered
that can selectively oxidize methane to methanol. We exploit active learning to …
that can selectively oxidize methane to methanol. We exploit active learning to …
Using Computational Chemistry to Reveal Nature's Blueprints for Single-Site Catalysis of C–H Activation
The challenge of activating inert C–H bonds motivates a study of catalysts that draws from
what can be accomplished by natural enzymes and translates these advantageous features …
what can be accomplished by natural enzymes and translates these advantageous features …
Performance of the r2SCAN Functional in Transition Metal Oxides
We assess the accuracy and computational efficiency of the recently developed meta-
generalized gradient approximation (metaGGA) functional, restored regularized strongly …
generalized gradient approximation (metaGGA) functional, restored regularized strongly …
Activating γ-graphyne nanoribbons as bifunctional electrocatalysts toward oxygen reduction and hydrogen evolution reactions by edge termination and nitrogen …
Carbon-based metal free materials (CMFCs) as electrocatalysts have been a hot issue and
are receiving growing attention. In this paper, we applied intensive density functional theory …
are receiving growing attention. In this paper, we applied intensive density functional theory …
Large data set-driven machine learning models for accurate prediction of the thermoelectric figure of merit
The figure of merit (zT) is a key parameter to measure the performance of thermoelectric
materials. At present, the prediction of zT values via machine leaning has emerged as a …
materials. At present, the prediction of zT values via machine leaning has emerged as a …
Machine learning to tame divergent density functional approximations: a new path to consensus materials design principles
Virtual high-throughput screening (VHTS) with density functional theory (DFT) and machine-
learning (ML)-acceleration is essential in rapid materials discovery. By necessity, efficient …
learning (ML)-acceleration is essential in rapid materials discovery. By necessity, efficient …
Accelerating materials-space exploration for thermal insulators by map** materials properties via artificial intelligence
Reliable artificial-intelligence models have the potential to accelerate the discovery of
materials with optimal properties for various applications, including superconductivity …
materials with optimal properties for various applications, including superconductivity …
Detection of multi-reference character imbalances enables a transfer learning approach for virtual high throughput screening with coupled cluster accuracy at DFT cost
Appropriately identifying and treating molecules and materials with significant multi-
reference (MR) character is crucial for achieving high data fidelity in virtual high-throughput …
reference (MR) character is crucial for achieving high data fidelity in virtual high-throughput …