289 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- 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…
Efficient Implementation of the Random Phase Approximation with Domain-based Local Pair Natural Orbitals
Yu Hsuan Liang, Xing Zhang, Garnet Kin-Lic Chan +2
We present an efficient implementation of the random phase approximation (RPA) for molecular systems within the domain-based local pair natural orbital (DLPNO) framework. With opti…
Integer linear programming for unsupervised training set selection in molecular machine learning
Matthieu Haeberle, Puck van Gerwen, Ruben Laplaza +4
Integer linear programming (ILP) is an elegant approach to solve linear optimization problems, naturally described using integer decision variables. Within the context of physics-i…
Dynamics of McMillan mappings III. Symmetric map with mixed nonlinearity
Tim Zolkin, Sergei Nagaitsev, Ivan Morozov +1
This article extends the study of the dynamical properties of the symmetric McMillan map, emphasizing its utility in understanding and modeling complex nonlinear systems. Although…
Surface activation by electron scavenger metal nanorod adsorption on TiH2, TiC, TiN, and Ti2O3
Yoyo Hinuma, Shinya Mine, Takashi Toyao +2
Metal/oxide support perimeter sites are known to provide unique properties because the nearby metal changes the local environment on the support surface. In particular, the electro…
Bridging the Gap: 3D Real-Space Characterization of Colloidal Assemblies via FIB-SEM Tomography
Jessi E. S. van der Hoeven, Ernest B. van der Wee, D. A. Matthijs de Winter +8
Insight in the structure of nanoparticle assemblies up to a single particle level is key to understand the collective properties of these assemblies, which critically depend on the…
Gel-Based Morphological Design of Zirconium Metal-organic Frameworks
Bart Bueken, Niels Van Velthoven, Tom Willhammar +7
The ability of metal-organic frameworks (MOFs) to gelate under specific synthetic conditions opens up new opportunities in the preparation and shaping of hierarchically porous MOF…