research
∙
04/12/2023
Accurate transition state generation with an object-aware equivariant elementary reaction diffusion model
Transition state (TS) search is key in chemistry for elucidating reactio...
share
research
∙
10/25/2022
A Database of Ultrastable MOFs Reassembled from Stable Fragments with Machine Learning Models
High-throughput screening of large hypothetical databases of metal-organ...
research
∙
09/18/2022
Low-cost machine learning approach to the prediction of transition metal phosphor excited state properties
Photoactive iridium complexes are of broad interest due to their applica...
research
∙
08/10/2022
Rapid Exploration of a 32.5M Compound Chemical Space with Active Learning to Discover Density Functional Approximation Insensitive and Synthetically Accessible Transitional Met
Two outstanding challenges for machine learning (ML) accelerated chemica...
research
∙
07/21/2022
A Transferable Recommender Approach for Selecting the Best Density Functional Approximations in Chemical Discovery
Approximate density functional theory (DFT) has become indispensable owi...
research
∙
05/06/2022
Putting Density Functional Theory to the Test in Machine-Learning-Accelerated Materials Discovery
Accelerated discovery with machine learning (ML) has begun to provide th...
research
∙
05/05/2022
Exploiting Ligand Additivity for Transferable Machine Learning of Multireference Character Across Known Transition Metal Complex Ligands
Accurate virtual high-throughput screening (VHTS) of transition metal co...
research
∙
03/02/2022
Machine learning models predict calculation outcomes with the transferability necessary for computational catalysis
Virtual high throughput screening (VHTS) and machine learning (ML) have ...
research
∙
01/11/2022
Two Wrongs Can Make a Right: A Transfer Learning Approach for Chemical Discovery with Chemical Accuracy
Appropriately identifying and treating molecules and materials with sign...
research
∙
11/02/2021
Audacity of huge: overcoming challenges of data scarcity and data quality for machine learning in computational materials discovery
Machine learning (ML)-accelerated discovery requires large amounts of hi...
research
∙
07/29/2021
Deciphering Cryptic Behavior in Bimetallic Transition Metal Complexes with Machine Learning
The rational tailoring of transition metal complexes is necessary to add...
research
∙
06/24/2021
Using Machine Learning and Data Mining to Leverage Community Knowledge for the Engineering of Stable Metal-Organic Frameworks
Although the tailored metal active sites and porous architectures of MOF...
research
∙
06/24/2021
Machine learning to tame divergent density functional approximations: a new path to consensus materials design principles
Computational virtual high-throughput screening (VHTS) with density func...
research
∙
06/20/2021