Article
Polymer Science
Anastassia Rissanou, Antonis Chazirakis, Patrycja Polinska, Craig Burkhart, Manolis Doxastakis, Vagelis Harmandaris
Summary: The study presents a bottom-up methodology to obtain coarse-grained models for copolymers through detailed atomistic simulations, using a dual-stage multi-component iterative Boltzmann inversion optimization scheme to derive effective CG interactions. The transferability of the PB copolymer model across molecular weight and copolymer composition is examined, with a focus on the impact of different isomers on conformational properties. The CG model is used to predict time mapping factors for segmental and center-of-mass dynamics of PB copolymers, showing potential applications in predicting polymer behavior.
Article
Polymer Science
Christina A. Gatsiou, Andreas Bick, Xenophon Krokidis, Ioannis G. Economou
Summary: This research presents a generic methodology for deriving coarse-grained forcefields and a simulation strategy for obtaining relaxed structures of amorphous amylose. A specific coarse-grained forcefield for amylose was constructed and compared to the Martini forcefield, showing its superiority in predicting structural properties. This work has the potential for studying other bio-based polymers.
Article
Chemistry, Physical
Marloes Arts, Victor Garcia Satorras, Chin-Wei Huang, Daniel Zuegner, Marco Federici, Cecilia Clementi, Frank Noe, Robert Pinsler, Rianne van den Berg
Summary: In this study, we employed score-based generative models and molecular dynamics to learn a force field for coarse-grained molecular dynamics. By training a diffusion generative model, we obtained an approximate force field that can be directly used to simulate coarse-grained molecular dynamics without requiring force inputs. Compared to previous work, our method significantly simplifies the training setup and demonstrates improved performance in protein simulations.
JOURNAL OF CHEMICAL THEORY AND COMPUTATION
(2023)
Article
Biochemistry & Molecular Biology
Andrew P. Latham, Bin Zhang
Summary: This study provides a critical review of existing coarse-grained force fields for disordered proteins and discusses the challenges in their application to folded proteins. It proposes an optimization strategy to improve the transferability of computer models across different protein types.
CURRENT OPINION IN STRUCTURAL BIOLOGY
(2022)
Article
Chemistry, Physical
Xinqiang Ding, Bin Zhang
Summary: Coarse-grained models are useful for simulating complex systems and providing molecular insights. This study introduces a new method, potential contrasting, for efficiently learning force fields that accurately reproduce the conformational distribution produced by all-atom simulations. The technique has been applied to the Trp-cage protein and shows promise in capturing the thermodynamics of the folding process and improving force field transferability.
JOURNAL OF CHEMICAL THEORY AND COMPUTATION
(2022)
Article
Chemistry, Physical
Anirban Chandra, Troy Loeffler, Henry Chan, Xiaoyu Wang, G. B. Stephenson, Michael J. Servis, Subramanian K. R. S. Sankaranarayanan
Summary: Exploring mesoscopic physical phenomena remains challenging for all-atom molecular dynamics simulations. Coarse-graining of all-atom models provides a solution to study mesoscale physics but without sacrificing desired structural features. In this study, a hybrid bond-order coarse-grained forcefield (HyCG) is proposed to model mesoscale aggregation in liquid-liquid mixtures. The potential is parameterized using a reinforcement learning algorithm and accurately captures critical fluctuations in binary extraction systems. This approach could be applied to explore inaccessible mesoscale phenomena with the developed potential model and training workflow.
JOURNAL OF CHEMICAL PHYSICS
(2023)
Article
Chemistry, Physical
Aditi Khot, Brett M. Savoie
Summary: Coarse-grained molecular dynamics (CGMD) simulations address critical lengthscales and timescales in chemical and material applications. The development of black-box CGMD methodologies similar to density functional theory for electronic structure is still lacking. Machine learning (ML)-based CGMD potentials show promise in simplifying model development, but they have yet to outperform physics-based CGMD methods. In this study, λ-learning models are explored to combine the advantages of both approaches. The λ-models outperform ML-only CGMD models and provide essentially free gains in reproducing atomistic properties. However, neither the λ-learning models nor the ML-only models significantly outperform elementary pairwise models in reproducing atomistic properties due to the large irreducible force errors associated with coarse-graining.
JOURNAL OF CHEMICAL PHYSICS
(2023)
Article
Materials Science, Multidisciplinary
C. H. Wu, R. Jiang, L. C. Zhang, Y. C. Wang, Y. Chen, Y. D. Song
Summary: This study investigates the fatigue crack growth (FCG) mechanisms of Ni-based superalloy FGH4098 at temperatures ranging from 650 to 800 degrees Celsius. The results show that the FCG rate increases by 2-3 orders of magnitude with increased temperature and prolonged dwell time, which is attributed to grain boundary oxidation. Characterization of the crack tips reveals that deformation, such as dislocations and stacking faults, mainly occurs within the gamma channel, accompanied by a change in grain orientation due to severe deformation. Pre-formed oxides at uncracked grain boundaries facilitate the FCG process. A mechanism map of the interaction between deformation-assisted grain boundary oxidation and FCG is proposed.
Article
Biochemical Research Methods
Damien Jefferies, Syma Khalid
Summary: Membrane proteins are amphipathic macromolecules with diverse chemical compositions and biological functions. Traditional experimental techniques and molecular modeling software, especially molecular dynamics algorithms, can provide comprehensive insights into the properties and dynamic behaviors of membrane proteins.
Article
Chemistry, Physical
Otello Maria Roscioni, Matteo Ricci, Claudio Zannoni, Gabriele D'Avino
Summary: The quality of amorphous molecular morphologies obtained with a new coarse-grained model is compared with reference atomistic data. The study focuses on small-molecule organic semiconductors in their pristine and doped forms, analyzing their structural features and electronic properties. The results demonstrate that the accurate coarse-grained model produces molecular glasses that are highly similar to atomistic samples, with even better agreement after back-mapping. The electronic properties of the back-mapped morphologies are almost indistinguishable from the atomistic reference, supporting the feasibility of large-scale simulations of complex molecular systems at a reduced computational cost.
JOURNAL OF PHYSICAL CHEMISTRY C
(2023)
Article
Chemistry, Physical
Ganesh Sivaraman, Nicholas E. Jackson
Summary: Scalable electronic predictions are critical for soft materials design. The Electronic Coarse-Graining (ECG) method uses deep neural networks (DNNs) to renormalize all-atom quantum chemical (QC) predictions to coarse-grained (CG) resolutions. The GPU-accelerated Deep Kernel Learning (DKL) framework enables CG QC predictions with a significant speedup, accurately reproducing molecular orbital energies.
JOURNAL OF CHEMICAL THEORY AND COMPUTATION
(2022)
Article
Polymer Science
Christopher Balzer, Amalie L. Frischknecht
Summary: The structure and morphology of ionic aggregates in ionomer melts significantly affect their ion transport properties. By incorporating polarization in ionomer melts, this study examines the role of polarization in the structure and dynamics of pendant ionomers and compares it to non-polarizable systems. The results show that polarization leads to smaller ionic aggregates and less overall ion structuring. Additionally, the time scale for free counterion diffusion is found to be independent of the morphology under certain conditions.
Article
Biochemistry & Molecular Biology
Martina Pannuzzo, Alessia Felici, Paolo Decuzzi
Summary: In this study, a computational model for poly-lactic-glycolic-acid (PLGA) was developed and validated to analyze the mesoscopic characteristics of PLGA-based delivery systems. The model, using coarse grained (CG) models and molecular dynamics (MD) simulations, predicted the translocation free energy barrier of drugs in the PLGA matrix and compared it with experimental release data. The proposed computational framework allows for predicting the pharmacological behavior of polymeric implants with different drug payloads under various conditions, reducing experimental workload and costs.
Article
Chemistry, Physical
Zack Jarin, James Newhouse, Gregory A. Voth
Summary: Using the MARTINI coarse-grained model as a test case, this study analyzes the adherence of top-down coarse-grained molecular dynamics models to the known features of statistical mechanics for all-atom representations. The MARTINI models do not completely capture the lipid structure seen in atomistic simulations, leading to issues of accuracy and temperature transferability. Differences are also observed in the association of embedded amphipathic helices and membrane height fluctuation between the MARTINI model and the all-atom simulations.
JOURNAL OF CHEMICAL THEORY AND COMPUTATION
(2021)
Article
Chemistry, Physical
Carles Navarro, Maciej Majewski, Gianni De Fabritiis
Summary: Developing accurate and efficient coarse-grained representations of proteins is crucial for understanding their folding, function, and interactions over extended time scales. This study presents a methodology that utilizes molecular dynamics simulations and neural networks to train a model that can accurately simulate protein folding events. The method eliminates the need for extensive simulations or labeled data and showcases extrapolation capabilities.
JOURNAL OF CHEMICAL THEORY AND COMPUTATION
(2023)
Article
Chemistry, Physical
Flora D. Tsourtou, Stavros D. Peroukidis, Loukas D. Peristeras
Summary: This study investigates the phase behavior of aqueous solutions of the cetyltrimethyl ammonium chloride surfactant and explores the transition from the micellar phase to the hexagonal columnar phase using all-atom molecular dynamics simulations. The results show that the system self-assembles into a mixture of spherical and rod-like micelles at lower concentrations, and forms the hexagonal columnar phase at higher concentrations. The research provides a basis for studying the phase behavior of other surfactants at high concentrations.
Article
Thermodynamics
Qianhui Wu, Lei Ding, Lei Zhang, Jijiang Ge, Mohammad Azizur Rahman, Dominique Guerillot, Ioannis G. Economou
Summary: The feasibility of using polymer enhanced foam (PEF) for enhanced oil recovery (EOR) in strongly oil-wet and heterogeneous carbonate reservoirs was investigated. The study focused on the effect of polymer type, flow rate, and brine composition on the foam strength and stability. It was found that adding an associative polymer or a nonionic polymer to the foam greatly enhanced its strength and stability, leading to significant improvements in oil recovery efficiency. The study highlights the importance of the interactions between the polymer and surfactant in PEF behavior.
Article
Thermodynamics
Nefeli Novak, Georgios M. Kontogeorgis, Marcelo Castier, Ioannis G. Economou
Summary: In this study, the eSAFT-VR Mie equation of state is extended to low relative permittivity, non-aqueous solutions. Different relative permittivity relations and approaches for the characteristic diameters are investigated. Results show that a mole fraction based mixing rule for the relative permittivity yields the best extrapolation from aqueous to non-aqueous solutions.
FLUID PHASE EQUILIBRIA
(2023)
Article
Engineering, Chemical
Panagiotis Krokidas, Marcelle B. M. Spera, Lamprini G. Boutsika, Ioannis Bratsos, Georgia Charalambopoulou, Ioannis G. Economou, Theodore Steriotis
Summary: In this study, the researchers demonstrated how molecular-scale modifications of zeolitic-imidazolate frameworks (ZIFs) can greatly improve the CO2/CH4 separation performance of mixed-matrix membranes (MMMs) when used as additives. Through molecular simulations and experiments, they identified modified ZIF fillers that exhibit unprecedented CO2/CH4 separation performance, surpassing any reported MMM. This work highlights the effectiveness of ZIF nanoengineering in developing highly selective CO2 membranes.
SEPARATION AND PURIFICATION TECHNOLOGY
(2023)
Article
Chemistry, Medicinal
Elli-Anna Stylianaki, Christiana Magkrioti, Eleni M. Ladopoulou, Konstantinos D. Papavasileiou, Panagiotis Lagarias, Georgia Melagraki, Martina Samiotaki, George Panayotou, Skarlatos G. Dedos, Antreas Afantitis, Vassilis Aidinis, Alexios N. Matralis
Summary: Robust experimental evidence has shown the importance of the ATX/LPA axis in chronic inflammatory conditions, fibroproliferative diseases, and cancer. Various ATX inhibitors have been discovered, with the first-in-class inhibitor currently in advanced clinical trials for idiopathic pulmonary fibrosis. The mode of inhibition and binding mechanism of these inhibitors to ATX have been studied.
EUROPEAN JOURNAL OF MEDICINAL CHEMISTRY
(2023)
Review
Biochemistry & Molecular Biology
Nikoletta-Maria Koutroumpa, Konstantinos D. Papavasileiou, Anastasios G. Papadiamantis, Georgia Melagraki, Antreas Afantitis
Summary: The process of discovering and developing new drugs is lengthy and expensive. Recent advancements in artificial intelligence have had a positive impact on the drug development pipeline. Deep learning algorithms have been used to address challenges and enhance the performance of drug discovery. This systematic review summarizes different deep learning architectures used in drug discovery and validates them with in vivo experiments.
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
(2023)
Article
Thermodynamics
Ilias K. Nikolaidis, Romain Privat, Jean-Noel Jaubert, Ioannis G. Economou
Summary: This study assesses the performance of the Perturbed Chain Statistical Associating Fluid Theory (PC-SAFT) equation of state (EoS) using various mixing rules and regression parameters, based on a high-quality reference database. The model's ability to qualitatively reproduce experimental data is quantified using a performance indicator. The effects of induced-association and cross-association on the EoS are investigated, and the final optimized EoS is determined. The average mark for the optimized EoS is 9.3/20.
JOURNAL OF CHEMICAL AND ENGINEERING DATA
(2023)
Biographical-Item
Chemistry, Physical
Edward J. Maginn, Ioannis G. Economou, Randall Q. Snurr, Arup K. Chakraborty
JOURNAL OF PHYSICAL CHEMISTRY B
(2023)
Article
Chemistry, Physical
Angel D. Cortes Morales, Nikolaos Diamantonis, Ioannis G. Economou, Cornelis J. Peters, J. Ilja Siepmann
Summary: The behavior of binary systems containing a low-boiling compound and a high-boiling compound near the critical point of the low-boiling compound can be unexpected. Increasing the pressure near the critical temperature of the low-boiling compound and for compositions rich in the low-boiling compound may result in the crossing of dew-point and bubble-point curves multiple times. This phenomenon, known as double retrograde vaporization, has implications in oil field operations and gas transport through pipelines, but the underlying reasons for its occurrence are not well understood. Monte Carlo simulations were conducted to provide molecular insights into the fluid properties associated with double retrograde vaporization using the united-atom version of the TraPPE force field for a methane/n-butane mixture.
JOURNAL OF PHYSICAL CHEMISTRY B
(2023)
Article
Chemistry, Physical
Noura Dawass, Manolis Vasileiadis, Loukas D. Peristeras, Konstantinos D. Papavasileiou, Ioannis G. Economou
Summary: Shale gas production is a rapidly growing sector in the oil and gas industry, and accurate prediction of its adsorption and transport is crucial for estimating production capacity. This study used molecular simulations to investigate the adsorption and diffusion of methane, ethane, and shale gas in a composite pore model representing heterogeneous shale formations. The addition of an inner slit pore significantly increased the adsorption of methane, and the saturation of the composite pore with methane occurred at a higher pressure than ethane. Carbon dioxide adsorption was not strongly affected by pressure, and its affinity to kerogen micropores was observed in all conditions. Diffusion coefficients were found to increase with the width of the empty slab inside the composite pore. The results provide insights into the adsorption mechanisms occurring inside the pore.
JOURNAL OF PHYSICAL CHEMISTRY C
(2023)
Review
Engineering, Chemical
Nefeli Novak, Georgios M. Kontogeorgis, Marcelo Castier, Ioannis G. Economou
Summary: This study focuses on the mixed-solvent electrolyte solutions and addresses the mean ionic activity coefficients, vapor-liquid equilibrium, and liquid-liquid equilibrium. The existing models and equations of state for electrolyte systems are reviewed, with emphasis on physical and electrolyte terms, relative static permittivity, and parameterization. A predictive model, eSAFT-VR Mie, is used to predict the activity coefficients, vapor-liquid equilibrium, and liquid-liquid equilibrium without any new adjustable parameters. The model shows excellent performance in predicting the activity coefficients and vapor-liquid equilibrium, while the prediction for liquid-liquid equilibrium is more challenging but has potential with accurate composition capturing.
INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH
(2023)
Article
Chemistry, Physical
Ioannis G. Diamataris, Loukas D. Peristeras, Konstantinos D. Papavasileiou, Vasilios S. Melissas, Georgios C. Boulougouris
Summary: This study presents a novel algorithm that infers kinetic parameters from the system's time evolution to reconstruct distributions of stochastic processes. The proposed approach accurately replicates rate constants of evolving stochastic reaction networks over time and can be successfully used to estimate rate constants of association and dissociation events in molecular dynamics simulations.
JOURNAL OF PHYSICAL CHEMISTRY B
(2023)
Article
Chemistry, Physical
Panagiotis Krokidas, Stelios Karozis, Salvador Moncho, George Giannakopoulos, Edward N. Brothers, Michael E. Kainourgiakis, Ioannis G. Economou, Theodore A. Steriotis
Summary: This study uses machine learning to investigate the effect of pore structure on molecular diffusivity in metal organic frameworks (MOFs), providing new theoretical and methodological insights into molecular sieving.
JOURNAL OF MATERIALS CHEMISTRY A
(2022)
