14 papers · ranked by Valyu relevance
Authors not listed
Quantum mechanics/molecular mechanics (QM/MM) methods have long been used to analyze enzymatic reaction mechanisms in silico. While treating the active site with QM and the remainder with MM reduces the computational cost, the inherently high computational cost of the QM calculation is still a major limitation for…
Authors not listed
Our study focused on the implementation and testing of machine learning interatomic potentials (MLIPs) into the AMBER software suite. This implementation enables us to perform a novel type of molecular dynamics simulation utilizing the hybrid machine learning/molecular mechanics (ML/MM) potentials. To underpin the…
Authors not listed
Chemical reactions in solution are central to biological function, synthetic chemistry, and materials design. Accurate modeling of these systems is essential for obtaining mechanistic insights, but remains computationally demanding. Hybrid machine-learned/molecular mechanics (ML/MM) simulations offer a promising…
Jógvan Magnus Haugaard Olsen, Viacheslav Bolnykh, Simone Meloni, Emiliano Ippoliti + 3 more
We present a flexible and efficient framework for multiscale modeling in computational chemistry (MiMiC). It is based on a multiple-program multiple-data (MPMD) model with loosely coupled programs. Fast data exchange between programs is achieved through the use of MPI intercommunicators. This allows exploiting the…
Authors not listed
Quantum mechanics/molecular mechanics (QM/MM) simulations are crucial for understanding enzymatic reactions, but their accuracy depends heavily on the quantum-mechanical method used. Semiempirical methods offer computational efficiency but often struggle with accuracy in complex systems. This work presents a novel…
Authors not listed
Hybrid machine-learning/molecular-mechanics (ML/MM) methods extend the classical QM/MM paradigm by replacing the quantum desription with neural network interatomic potentials trained to reproduce accurately quantum-mechanical (QM) results. By describing only the chemically active region with ML and the surrounding…
Sonata Kvedaravičiūtė, Andrej Antalík, Olivier Adjoua, Thomas Plé + 4 more
In this work, we present the development of a fully-polarizable KS-DFT/AMOEBA embedding scheme for delocalized basis sets such as plane-waves and real-space grids. The augmented problem of electron spill-out inherent to a polarizable QM/MM implementation with plane-wave basis sets is addressed and the periodicity for…
Authors not listed
Electrostatic embedding Quantum mechanics / molecular mechanics (QM/MM) methods in periodic boundary conditions can successfully describe the condensed phase reactivity of a fragment treated at the QM level with an atomistic description of an electrostatic environment treated at the MM level. The computational cost of…
Nathanael J. King, Ian LeBlanc, Alex Brown
Conformational ensemble generation and the search for the global minimum conformation are important problems in computational chemistry. In this work, a variant on the Conformer-Rotamer Ensemble Sampling Tool (CREST) algorithm designed for determining structural ensembles and energetics of non-covalent clusters of…
Sukanya Sasmal, Léa El Khoury, David Mobley
The Drug Design Data Resource (D3R) Grand Challenges present an opportunity to assess, in the context of a blind predictive challenge, the accuracy and the limits of tools and methodologies designed to help guide pharmaceutical drug discovery projects. Here, we report the results of our participation in the D3R Grand…
Authors not listed
Periodic boundary condition-adapted formulations of quantum mechanics/molecular mechanics (QM/MM) methods enable the extraction of accurate free energies, provided that efficient phase-space sampling is achieved. In this work, we develop a thermodynamic integration scheme based on an electrostatic embedding QM/MM…
Authors not listed
We present a theoretical and computational framework for virtual mass spectrometry based on Molecular Maxwell Demons (MMDs) operating as information catalysts. Building on the biological Maxwell demon framework, we demonstrate that mass spectrometry data contain categorical state information that is fundamentally…
Andrew Simmonett, Bernard Brooks, Thomas Darden
Evaluation of noncovalent electrostatic interactions is the dominant bottleneck in classical molecular dynamics simulations, and evaluation of Coulombic matrix elements similarly limits quantum mechanical self consistent field calculations. These difficulties are a result of the Coulomb operator’s slow decay, which…
Authors not listed
Machine olfaction—the artificial replication of the sense of smell—faces significant challenges due to the absence of large, standardized training datasets. Unlike vision, language, and audio models, which benefit from extensive corpora such as ImageNet, GLUE, and AudioSet, olfaction lacks scaled equivalents and…