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View article: A foundation model for atomistic materials chemistry
A foundation model for atomistic materials chemistry Open
Atomistic simulations of matter, especially those that leverage first-principles (ab initio) electronic structure theory, provide a microscopic view of the world, underpinning much of our understanding of chemistry and materials science. O…
Machine learning-accelerated evolutionary Monte Carlo for rapid phase exploration of compositionally complex materials in reactive environments Open
Compositionally complex materials, e.g. high-entropy alloys/oxides, have long been of research interest for their unique mechanical and functional properties thanks to the synergistic effects between the large number of components. More re…
View article: Seed-Mediated Colloidal Synthesis of Multimetallic and High-Entropy Alloy Nanocrystal Libraries with Enhanced Catalytic Performance
Seed-Mediated Colloidal Synthesis of Multimetallic and High-Entropy Alloy Nanocrystal Libraries with Enhanced Catalytic Performance Open
Engineering colloidally stable multimetallic nanocrystals has many benefits in a wide range of applications and allows manipulating physical, chemical, and electronic structure properties of materials at the nanoscale. Synthesis routes are…
MLIPX: machine-learned interatomic potential eXploration Open
The rapid advancement in machine-learned interatomic potentials (MLIPs) and the proliferation of universal MLIPs ( u MLIPs) have significantly broadened their application scope. Community benchmarks and leaderboard rankings are frequently …
MLIPX: Machine Learned Interatomic Potential eXploration Open
The rapid advancement in machine-learned interatomic potentials (MLIPs) and the proliferation of uni- versal MLIPs (uMLIPs) have significantly broadened their application scope. Community benchmarks and leaderboard rankings are frequently …
View article: Massive Atomic Diversity: a compact universal dataset for atomistic machine learning
Massive Atomic Diversity: a compact universal dataset for atomistic machine learning Open
The development of machine-learning models for atomic-scale simulations has benefited tremendously from the large databases of materials and molecular properties computed in the past two decades using electronic-structure calculations. Mor…
Copper Gallium Aluminum Mixed Metal Oxides as new Alternative Catalyst Candidates for Efficient Conversion of Carbon Dioxide to Methanol and Dimethyl Ether Open
Converting CO2 with renewable hydrogen requires high-value products to be economically viable due to its inherent energy intensity and associated renewable energy costs. Direct hydrogenation of CO2 via exothermic reactions is appealing giv…
Simple Heuristics for Advanced Sampling of Reactive Species on Surfaces Open
Understanding interactions between reactive species and surfaces is a central task in materials science and heterogeneous catalysis. Decades of research have produced strategies to automatically detects surface active sites on computationa…
Exploring the Intricacies of Glycerol Hydrodeoxygenation to Propanediol on Cu surface: A Comprehensive Investigation with the Aid of Machine Learning Forcefield Open
The utilization of biomass to feedstock chemicals often relies on transforming hydroxyl-containing molecules. One such example is glycerol which can undergo a selective hydrodeoxygenation reaction to produce propanediol, a valuable chemica…
From Poison to Promotor: Spatially Isolated Metal Sites in Supported Rhodium Sulfides as Hydroformylation Catalysts Open
The hydroformylation of alkenes is a cornerstone transformation for the chemical industry, central for both functionalizing and extending the carbon backbone of an alkene. In this study, silica‐supported crystalline rhodium sulfide nanopar…
From poison to promotor –unique Rh4 structural motifs in supported rhodium sulphides as hydroformylation catalysts Open
The hydroformylation of alkenes is a cornerstone transformation for the chemical industry, central for both functionalizing and extending the carbon backbone of an alkene. In our study, we explored silica-supported crystalline rhodium sulf…
From poison to promoter: investigation of supported rhodium sulphides as heterogeneous hydroformylation catalysts Open
Herein we report the use of supported nanoparticles of crystalline rhodium sulphides as active heterogeneous catalysts for the hydroformylation of alkenes showing an excellent selectivity towards the aldehyde products. It was found that su…
View article: Surface segregation in high-entropy alloys from alchemical machine learning
Surface segregation in high-entropy alloys from alchemical machine learning Open
High-entropy alloys (HEAs), containing several metallic elements in near-equimolar proportions, have long been of interest for their unique mechanical properties. More recently, they have emerged as a promising platform for the development…
View article: A foundation model for atomistic materials chemistry
A foundation model for atomistic materials chemistry Open
Atomistic simulations of matter, especially those that leverage first-principles (ab initio) electronic structure theory, provide a microscopic view of the world, underpinning much of our understanding of chemistry and materials science. O…
View article: Surface segregation in high-entropy alloys from alchemical machine learning
Surface segregation in high-entropy alloys from alchemical machine learning Open
High-entropy alloys (HEAs), containing several metallic elements in near-equimolar proportions, have long been of interest for their unique mechanical properties. More recently, they have emerged as a promising platform for the development…
Accurate energy barriers for catalytic reaction pathways: an automatic training protocol for machine learning force fields Open
We introduce a training protocol for developing machine learning force fields (MLFFs), capable of accurately determining energy barriers in catalytic reaction pathways. The protocol is validated on the extensively explored hydrogenation of…
Data‐driven Design of Enhanced In‐based Catalyst for CO<sub>2</sub> to Methanol Reaction Open
The environmental impact of unsustainable CO 2 emissions calls for immediate action. One of the main methods for large‐scale reduction of CO 2 emissions is conversion of carbon dioxide to valuable feedstocks like energy carriers or chemica…
View article: Modeling high-entropy transition metal alloys with alchemical compression
Modeling high-entropy transition metal alloys with alchemical compression Open
Alloys composed of several elements in roughly equimolar composition, often referred to as high-entropy alloys, have long been of interest for their thermodynamics and peculiar mechanical properties, and more recently for their potential a…
Data deposit accompanying Accurate Energy Barriers for Catalytic Reaction Pathways: An Automatic Training Protocol for Machine Learning Force Fields Open
Dataset accompanying the paper: "Accurate Energy Barriers for Catalytic Reaction Pathways: An Automatic Training Protocol for Machine Learning Force Fields". Contains the training sets curated during active learning as well as .xyz files u…
Data deposit accompanying Accurate Energy Barriers for Catalytic Reaction Pathways: An Automatic Training Protocol for Machine Learning Force Fields Open
Dataset accompanying the paper: "Accurate Energy Barriers for Catalytic Reaction Pathways: An Automatic Training Protocol for Machine Learning Force Fields". Contains the training sets curated during active learning as well as .xyz files u…
A data-driven high-throughput workflow applied to promoted In-oxide catalysts for CO<sub>2</sub> hydrogenation to methanol Open
To facilitate accelerated catalyst design, a combined computation and experimental workflow based on machine learning algorithms is proposed, which detects key performance-related descriptors in a CO 2 to methanol reaction, for In 2 O 3 -b…
Accurate energy barriers for catalytic reaction pathways: an automatic training protocol for machine learning force fields Open
In this study, we introduce a training protocol for developing machine learning force fields (MLFFs), capable of accurately determining energy barriers in catalytic reaction pathways. The protocol is validated on the extensively explored h…
View article: Modeling high-entropy transition-metal alloys with alchemical compression
Modeling high-entropy transition-metal alloys with alchemical compression Open
Alloys composed of several elements in roughly equimolar composition, often referred to as high-entropy alloys, have long been of interest for their thermodynamics and peculiar mechanical properties, and more recently for their potential a…
Quantification and Tuning of Surface Oxygen Vacancies for the Hydrogenation of CO<sub>2</sub> on Indium Oxide Catalysts Open
The direct hydrogenation of CO 2 to methanol is an attractive approach to employ the greenhouse gas as a chemical feedstock. However, the commercial copper catalyst, used for methanol synthesis from CO‐rich syngas, suffers from deactivatio…
Learning Design Rules for Selective Oxidation Catalysts from High-Throughput Experimentation and Artificial Intelligence Open
The design of heterogeneous catalysts is challenged by the complexity of materials and processes that govern reactivity and by the fact that the number of good catalysts is very small in comparison to the number of possible materials. Here…
Learning design rules for selective oxidation catalysts from high-throughput experimentation and artificial intelligence Open
The design of heterogeneous catalysts is challenged by the complexity of materials and processes that govern reactivity and by the fact that the number of good catalysts is very small compared to the number of possible materials. Here, we …
Learning design rules for selective oxidation catalysts from high-throughput experimentation and artificial intelligence Open
The design of heterogeneous catalysts is challenged by the complexity of materials and processes that govern reactivity and by the fact that the number of good catalysts is very small compared to the number of possible materials. Here, we …
View article: An assessment of the structural resolution of various fingerprints commonly used in machine learning
An assessment of the structural resolution of various fingerprints commonly used in machine learning Open
Atomic environment fingerprints are widely used in computational materials science, from machine learning potentials to the quantification of similarities between atomic configurations. Many approaches to the construction of such fingerpri…