Article
Chemistry, Multidisciplinary
Vittorio Del Tatto, Paolo Raiteri, Mattia Bernetti, Giovanni Bussi
Summary: In this study, the equations of motion for a fully anisotropic barostat are developed and implemented, and validated through testing on different materials. The algorithm, with a single parameter controlling the relaxation time of the volume, exhibits exponential decay of correlation functions and can be applied effectively to a wide range of systems.
APPLIED SCIENCES-BASEL
(2022)
Article
Chemistry, Physical
A. Arjun, Peter G. Bolhuis
Summary: This study investigates the homogenous nucleation rate of CO2 hydrates at low temperatures using transition interface sampling simulations, revealing the differences in hydrate formation processes at different temperatures and shedding light on the kinetics of nucleation processes.
JOURNAL OF CHEMICAL PHYSICS
(2021)
Article
Physics, Multidisciplinary
Alfredo Ricci Vasquez, Carmelo Mordini, Chloe Verniere, Martin Stadler, Maciej Malinowski, Chi Zhang, Daniel Kienzler, Karan K. Mehta, Jonathan P. Home
Summary: Using a single calcium ion trapped in a surface-electrode trap, the interaction of electric quadrupole transitions with a passively phase-stable optical standing wave field sourced by photonics integrated within the trap is studied. The optical fields are characterized through spatial mapping of the Rabi frequencies of both carrier and motional sideband transitions as well as ac Stark shifts. The measurements demonstrate the ability to engineer favorable combinations of sideband and carrier Rabi frequency as well as ac Stark shifts for specific tasks in quantum state control and metrology.
PHYSICAL REVIEW LETTERS
(2023)
Article
Biochemistry & Molecular Biology
Kader Sahin
Summary: In this study, novel indole-based hits against HIV-1 PR were identified by screening molecules containing indole keywords, and using a combination of molecular docking and molecular dynamics simulations.
JOURNAL OF BIOMOLECULAR STRUCTURE & DYNAMICS
(2021)
Article
Engineering, Chemical
Mingli Mu, Gangqiang Yu, Xinfeng Zhang, Ruinian Xu, Ning Wang, Biaohua Chen, Chengna Dai
Summary: This study proposes and systematically investigates the efficient capture of dichloromethane (DCM) with ionic liquids (ILs). Suitable carboxylic acid-based anions and quaternary phosphonium cations were screened, and the designed ILs of tetrabutylphosphonium hexanoate ([P4444][C5COO]) and tetrabutylammonium hexanoate ([N4444][C5COO]) were synthesized. The results show that [P4444][C5COO] exhibits the best absorption performance and the lowest Henry's law constant for DCM. The microscopic mechanism was explored through various experiments, revealing the dominance of the anion in the absorption process. The proposed IL shows promising potential as an alternative absorbent with high absorption capacity, suitable physicochemical properties, and excellent recyclability.
CHEMICAL ENGINEERING SCIENCE
(2024)
Article
Multidisciplinary Sciences
N. Gao, Z. W. Yao, G. H. Lu, H. Q. Deng, F. Gao
Summary: The study found a new diffusion mechanism for <100> interstitial dislocation loops in BCC iron using self-adaptive accelerated molecular dynamics, which represents a significant step towards understanding the mechanical behavior and microstructure evolution of the material.
NATURE COMMUNICATIONS
(2021)
Article
Chemistry, Physical
Aleksander E. P. Durumeric, Gregory A. Voth
Summary: Bottom-up CG molecular dynamics models, parameterized using complex effective Hamiltonians, are often optimized to approximate high dimensional data from atomistic simulations. However, human validation of these models may not differentiate between the CG model and the atomistic simulations. We propose using classification to estimate high dimensional errors and utilizing explainable machine learning to convey this information to scientists. This approach is demonstrated using Shapley additive explanations and two CG protein models, and may be valuable for assessing the accuracy of allosteric effects in CG models at the atomistic level.
JOURNAL OF CHEMICAL PHYSICS
(2023)
Article
Biochemistry & Molecular Biology
Wensheng Wu, As'ad Alizadeh, Maboud Hekmatifar
Summary: Nowadays, advances in science and technology in biological macromolecules have enabled early detection and treatment of cancer cells. In this study, molecular dynamics simulation was used to analyze atomic interactions between 3DN5 and 5OTF structures, and the effect of initial temperature on atomic behavior was investigated. The stability of simulated structures was examined by changes in temperature and kinetic energy. Biomechanical interaction was studied using the radius of gyration, interaction energy, and interaction force. The results suggest that these studies can contribute to early detection and treatment of cancer cells.
INTERNATIONAL JOURNAL OF BIOLOGICAL MACROMOLECULES
(2023)
Article
Chemistry, Physical
Artem D. Glova, Victor M. Nazarychev, Sergey V. Larin, Andrey A. Gurtovenko, Sergey V. Lyulin
Summary: Atomistic computer simulations suggest that asphaltenes with enlarged aromatic cores can improve the performance of heat storage devices based on organic phase change materials. Increasing the size of the asphaltene cores promotes the formation of extended structures in paraffin and enhances the thermal conductivity of liquid paraffin.
PHYSICAL CHEMISTRY CHEMICAL PHYSICS
(2023)
Article
Cell Biology
Varun Chauhan, Tripti Rungta, Manmeet Rawat, Kapil Goyal, Yash Gupta, Mini P. Singh
Summary: This study aimed to explore the SARS-CoV-2 genome, design a multi-epitope vaccine, and conduct immune simulation analysis. The vaccine may be effective and cover over 97% of the global population, with high affinity and stability in interaction with immune receptors.
JOURNAL OF CELLULAR PHYSIOLOGY
(2021)
Article
Chemistry, Physical
Edoardo Cignoni, Lorenzo Cupellini, Benedetta Mennucci
Summary: We propose a machine learning strategy to calculate the excitonic properties of light harvesting complexes. By combining molecular dynamics simulations with ML prediction of the excitonic Hamiltonian, the proposed model can account for geometric fluctuations and electrostatic interactions. The model is trained on chlorophylls but can extrapolate beyond the training set, and its accuracy is demonstrated through simulations of absorption spectra in different environments.
JOURNAL OF CHEMICAL THEORY AND COMPUTATION
(2023)
Article
Computer Science, Hardware & Architecture
Xiao-Yang Liu, Zeliang Zhang, Zhiyuan Wang, Han Lu, Xiaodong Wang, Anwar Walid
Summary: This paper presents hardware-oriented optimization strategies for tensor learning primitives on GPU tensor cores, resulting in significant speedups for tasks such as tensor decomposition and neural network compression. The proposed optimizations achieve up to 32.25x speedup compared to existing libraries like TensorLab and TensorLy, demonstrating the effectiveness of GPU-based tensor learning.
IEEE TRANSACTIONS ON COMPUTERS
(2023)
Article
Chemistry, Physical
Takamasa Saito, Eita Shoji, Masaki Kubo, Takao Tsukada, Gota Kikugawa, Donatas Surblys
Summary: This study evaluated the affinity between a surface-modified inorganic solid and an organic solvent by calculating the work of adhesion at the interface. Results showed that the surface coverage of the modifier affected the work of adhesion, with solvent molecules penetrating the modification layer at high coverage, increasing the adhesion.
JOURNAL OF CHEMICAL PHYSICS
(2021)
Article
Chemistry, Physical
Majid Moosavi, Mehrangiz Torkzadeh, Amir Nikpour
Summary: MXenes combined with ionic liquid electrolytes have shown great promise in energy storage systems. The nanoscopic structure of biodegradable choline-based ionic liquids (CBILs) and their water mixtures near the MXene surface was studied through quantum mechanics calculations and molecular dynamics simulations. The results revealed that the interaction between the anion and cation in CBILs affects their behavior on the MXene surface, and the presence of water molecules alters the dynamics and structural correlations of the system. Understanding the nano-scale behavior of these unique ionic liquids on MXenes has potential applications in energy storage systems.
JOURNAL OF MOLECULAR LIQUIDS
(2023)
Article
Chemistry, Physical
D. Aristoff, J. Copperman, G. Simpson, R. J. Webber, D. M. Zuckerman
Summary: The weighted ensemble (WE) method, an enhanced sampling approach, has gained popularity in computational biochemistry due to improved hardware, software, and algorithmic advancements. Recent results have led to greater computational efficiency, particularly through variance reduction approaches for managing trajectories in systems of any dimensionality.
JOURNAL OF CHEMICAL PHYSICS
(2023)
Article
Chemistry, Physical
Susan M. Mniszewski, Romain Perriot, Emanuel H. Rubensson, Christian F. A. Negre, Marc J. Cawkwell, Anders M. N. Niklasson
JOURNAL OF CHEMICAL THEORY AND COMPUTATION
(2019)
Article
Chemistry, Physical
Joshua Finkelstein, Justin S. Smith, Susan M. Mniszewski, Kipton Barros, Christian F. A. Negre, Emanuel H. Rubensson, Anders M. N. Niklasson
Summary: A second-order recursive Fermi-operator expansion scheme using mixed precision floating point operations on tensor core units is presented, achieving over 100 teraFLOPS of performance for half-precision floating point operations on Nvidia's A100. The scheme is formulated as a generalized, differentiable deep neural network structure, accelerating convergence by optimizing weight and bias values, and optimizing coefficients of the expansion to accurately represent fractional occupation numbers of electronic states at finite temperatures using a machine learning approach.
JOURNAL OF CHEMICAL THEORY AND COMPUTATION
(2021)
Article
Computer Science, Interdisciplinary Applications
Anton G. Artemov, Emanuel H. Rubensson
Summary: The proposed method utilizes acceleration technique and submatrix product screening to reduce computational cost, with a selected threshold value for error control.
JOURNAL OF COMPUTATIONAL PHYSICS
(2021)
Article
Chemistry, Physical
Joshua Finkelstein, Emanuel H. Rubensson, Susan M. Mniszewski, Christian F. A. Negre, Anders M. N. Niklasson
Summary: In this paper, density matrix perturbation theory is mapped onto the computational structure of a deep neural network, and time-independent quantum response calculations are performed using Tensor cores. The main computational cost of each deep layer is dominated by tensor contractions in mixed-precision arithmetics, achieving close to peak performance. Quantum response calculations are demonstrated and analyzed with self-consistent charge density-functional tight-binding theory and coupled-perturbed Hartree-Fock theory. A novel parameter-free convergence criterion is presented for linear response calculations, suitable for numerically noisy low-precision floating point operations, and a peak performance of almost 200 Tflops is demonstrated using the Tensor cores of two Nvidia A100 GPUs.
JOURNAL OF CHEMICAL THEORY AND COMPUTATION
(2022)
Article
Computer Science, Software Engineering
Emanuel H. Rubensson, Elias Rudberg, Anastasia Kruchinina, Anton G. Artemov
Summary: We introduce a C++ header-only parallel sparse matrix library based on a sparse quadtree representation and the Chunks and Tasks programming model. This library dynamically exploits data locality to avoid data movement and demonstrates successful distributed memory parallelization of block-sparse matrix-matrix multiplication for matrices with different nonzero structures.