289 citations
- Flatiron Health (United States)US3 papers
- Flatiron Institute3 papers
- Columbia UniversityUS2 papers
- KU LeuvenBE2 papers
- Boreskov Institute of CatalysisRU1 paper
- Brookhaven CollegeUS1 paper
- California Institute of TechnologyUS1 paper
- Cancer Genomics CentreNL1 paper
- Chalmers University of TechnologySE1 paper
- Chinese Academy of SciencesCN1 paper
- Collaborative Innovation Center of Chemistry for Energy MaterialsCN1 paper
- ConvergenceUS1 paper
12 papers
Markov State Models for Tracking Reaction Dynamics on Catalytic Nanoparticles
Caitlin A. McCandler, Chatipat Lorpaiboon, Timothy C. Berkelbach +1
Markov state models (MSMs) are a powerful tool to analyze and coarse-grain complex dynamical data into interpretable kinetic processes. This capability is particularly important in…
Skeletal editing by tip-induced chemistry
Shantanu Mishra, Valentina Malave, Rasmus Svensson +4
Skeletal editing of cyclic molecules has garnered considerable attention in the context of drug discovery and green chemistry, with notable examples in solution-phase synthesis. He…
Automated Machine Learning Pipeline: Large Language Models-Assisted Automated Dataset Generation for Training Machine-Learned Interatomic Potentials
Adam Lahouari, Jutta Rogal, Mark E. Tuckerman
Machine learning interatomic potentials (MLIPs) have become powerful tools to extend molecular simulations beyond the limits of quantum methods, offering near-quantum accuracy at m…
Amorphous to Crystalline Transformation: How Cluster Aggregation Drives the Multistep Nucleation of ZIF-8
Sambhu Radhakrishnan, Flip de Jong, Estelle Becquevort +11
Nucleation, the pivotal first step of crystallization, governs essential characteristics of crystallization products, including size distribution, morphology, and polymorphism. Whi…
Quantum Chemistry Driven Molecular Inverse Design with Data-free Reinforcement Learning
Francesco Calcagno, Luca Serfilippi, Giorgio Franceschelli +3
The inverse design of molecules has challenged chemists for decades. In the past years, machine learning and artificial intelligence have emerged as new tools to generate molecules…
Decoding the Competing Effects of Dynamic Solvation Structures on Nuclear Magnetic Resonance Chemical Shifts of Battery Electrolytes via Machine Learning
Qi You, Yan Sun, Feng Wang +2
Understanding the solvation structure of electrolytes is critical for optimizing the electrochemical performance of rechargeable batteries, as it directly influences properties suc…