Article
Computer Science, Interdisciplinary Applications
Ugur G. Abdulla, Roby Poteau
Summary: A numerical method for identifying parameters in large-scale systems of nonlinear ODEs in systems biology is introduced, combining optimization, sensitivity analysis, and regularization. The method demonstrates superlinear convergence in testing on canonical benchmark models, making it suitable for partial and noisy measurements. The developed software package qlopt shows advantages over popular methods/software like lsqnonlin, finincon, and nl2sol.
JOURNAL OF COMPUTATIONAL PHYSICS
(2021)
Article
Chemistry, Physical
Yiying Zhu, Hao Wang, Lingkang Wu, Mo Li
Summary: Crystalline materials undergo phase transitions from ordered to disordered state when heated, with the transition also possible via various stimuli like chemical mixing, irradiation, or hydrogen permeation. Research has shown that the first order transition can become continuous in certain diffusion couples, with anisotropic amorphization transition observed at the interface between crystal and amorphous alloy.
JOURNAL OF ALLOYS AND COMPOUNDS
(2021)
Article
Mechanics
Claudio Cremaschini, Jiri Kovar, Zdenek Stuchlik, Massimo Tessarotto
Summary: This article reviews the original thermodynamic formulation of the Tolman-Ehrenfest effect and discusses the different statistical temperature distributions and their properties in non-ideal fluids.
Article
Automation & Control Systems
Sanaz Sajedi-Amin, Hamid Abdollahi, Abdolhossein Naseri
Summary: The paper introduces the coupled kinetic-equilibrium process as a new approach to solving the issue of determining correlated model parameters in nonlinear model fitting. This strategy can be applied to uniquely measure rate constants in incomplete concentration data as well as parameter determination in multi-step kinetic reactions. By conducting model-based global analysis, optimal parameters and their standard deviations can be obtained for complex reaction mechanisms.
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS
(2021)
Article
Biochemical Research Methods
Jonathan R. Bowles, Caroline Hoppe, Hilary L. Ashe, Magnus Rattray
Summary: This study presents a scalable implementation of the cpHMM for fast inference of promoter activity and transcriptional kinetic parameters. The method can model genes of arbitrary length and accurately infer kinetic parameters within a computationally feasible timeframe.
Article
Developmental Biology
Maria Yampolskaya, Michael J. Herriges, Laertis Ikonomou, Darrell N. Kotton, Pankaj Mehta
Summary: Advances in single-cell RNA sequencing have provided a new way to understand cellular identity. The scTOP method, a statistical and physics-inspired approach, accurately classifies cells, visualizes developmental trajectories, and evaluates engineered cells without feature selection or dimensional reduction. Its application on human and mouse datasets has demonstrated its power in characterizing cellular populations and differentiation.
Article
Chemistry, Physical
Yagyik Goswami, Srikanth Sastry
Summary: This paper presents a method to estimate free energy surfaces with respect to multiple order parameters from a steady state ensemble of trajectories, and applies it to reconstruct the free energy surface for supercooled liquid silicon. The results are consistent with previous findings from umbrella sampling.
JOURNAL OF CHEMICAL PHYSICS
(2023)
Article
Mathematics, Interdisciplinary Applications
Lucas Kenji Arima Miranda, Raphael Moratta, Celia Mayumi Kuwana, Makoto Yoshida, Juliano Antonio de Oliveira, Edson Denis Leonel
Summary: In this study, an order parameter is identified in the transition from limited to unlimited chaotic diffusion in a dissipative standard mapping. The suppression of unlimited chaotic diffusion is proven to be caused by the presence of a continuous phase transition. The investigation of the main properties of the transition for long-time dynamics is enabled by obtaining the average squared action. The main questions focus on characterizing the order of this phase transition and understanding the elementary excitation of the dynamics that affects the transport of particles in the system.
CHAOS SOLITONS & FRACTALS
(2022)
Article
Physics, Fluids & Plasmas
Dominik Wilde, Andreas Kraemer, Dirk Reith, Holger Foysi
Summary: The study presents a new three-dimensional semi-Lagrangian lattice Boltzmann method that overcomes the limitations of traditional methods in simulating turbulent compressible flows, accurately capturing shocks and turbulence without the need for additional filtering or stabilizing techniques.
Article
Chemistry, Multidisciplinary
Ivan Bondarchuk, Valery Perevozkin, Sergey Bondarchuk, Alexander Vorozhtsov
Summary: A mathematical model is proposed for estimating the characteristics of thermal inactivation of vegetative bacterial cells and their spores. The model is used to solve the inverse problem of identifying the model parameters from experimental data, and the results show its adequacy and accuracy. The developed model can be used for selective deactivation of pathogens in food products.
APPLIED SCIENCES-BASEL
(2022)
Article
Automation & Control Systems
Yutao Tang, Peng Yi
Summary: In this article, a Nash equilibrium seeking problem for a class of high-order multiagent systems with unknown dynamics is considered. The objective is to steer the outputs of these uncertain high-order agents to the Nash equilibrium of some noncooperative game in a distributed manner. To overcome the difficulties brought by the high-order structure, unknown nonlinearities, and the regulation requirement, a virtual player is introduced for each agent and an auxiliary noncooperative game is solved. A distributed adaptive protocol is developed by embedding this auxiliary game dynamics into some proper tracking controller for the original agent to resolve this problem. The parameter convergence issue is also discussed under certain persistence of excitation conditions. The efficacy of the algorithms is verified by numerical examples.
IEEE TRANSACTIONS ON CONTROL OF NETWORK SYSTEMS
(2023)
Article
Chemistry, Multidisciplinary
Lei-Min Zhao, Li-Shuo Zheng, Xiaoping Wang, Wei Jiang
Summary: This study reports a molecular shuttle based on a rotaxane, which is influenced by an acid-responsive asymmetric macrocycle. Upon protonation induced by trifluoroacetic acid (TFA), the macrocycle translocates and the shuttling kinetics are hindered by steric hindrance. The shuttling kinetics depend on the concentration of TFA and a kinetic intermediate can be captured.
ANGEWANDTE CHEMIE-INTERNATIONAL EDITION
(2022)
Article
Chemistry, Multidisciplinary
Recep Uzek, Serap Senel, Adil Denizli
Summary: In this study, a novel imprinted solid-phase extraction cartridge was developed for the selective adsorption of Bisphenol A (BPA). The optimal conditions for BPA adsorption were determined by analyzing various parameters, and the adsorption properties were examined using isotherm models, thermodynamic parameters, and kinetic models. The results demonstrated the advantages of the developed cartridge in terms of selectivity.
Article
Thermodynamics
Jianjian Tao, Xuezhe Wei, Pingwen Ming, Xueyuan Wang, Shangfeng Jiang, Haifeng Dai
Summary: A one-dimensional transient model of PEMFC for cold start was developed and validated through parameter identification, showing good consistency with experimental results. The reduced model effectively captured the multiplicity behaviors of the detailed model while significantly reducing computation time, suitable for real-time estimation and control strategy development.
ENERGY CONVERSION AND MANAGEMENT
(2022)
Article
Chemistry, Physical
Gang Sun, Peter Harrowell
Summary: This paper proposes a method to develop a general atomic level description of amorphous solidification by measuring a measure of atomic restraint. By defining the measure using instantaneous normal modes, the differences between fragile and strong liquids and the collective length scale of the supercooled liquid can be taken into account.
JOURNAL OF CHEMICAL PHYSICS
(2022)
Article
Chemistry, Physical
Luigi Sbailo, Manuel Dibak, Frank Noe
Summary: The proposed method uses generative neural networks to connect metastable regions directly, propose new configurations in the Markov chain, and optimize the acceptance probability of large jumps between modes in the configuration space. It effectively increases the convergence speed of systems with multiple metastable states.
JOURNAL OF CHEMICAL PHYSICS
(2021)
Article
Multidisciplinary Sciences
Tim Hempel, Mauricio J. del Razo, Christopher T. Lee, Bryn C. Taylor, Rommie E. Amaro, Frank Noe
Summary: In this study, a technique called independent Markov decomposition (IMD) is introduced, which leverages weak coupling between subsystems to compute a global kinetic model without requiring sampling of all combinatorial states of subsystems. It is demonstrated that IMD models can reproduce experimental conductance measurements with significant reduction in sampling compared to a standard approach. The study also proposes a method to find the optimal partition of all-atom protein simulations into weakly coupled subunits.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
(2021)
Article
Chemistry, Physical
Mauricio J. del Razo, Manuel Dibak, Christof Schuette, Frank Noe
Summary: A novel approach, MSM/RD, is developed to simulate protein-ligand systems at large time and length scales by coupling Markov state models of molecular kinetics with particle-based reaction-diffusion simulations. The framework is capable of modeling protein-protein interactions and handling multiple molecules, addressing limitations such as isotropic ligands and single conformational states. The code has been published for reproducibility.
JOURNAL OF CHEMICAL PHYSICS
(2021)
Article
Chemistry, Physical
Andreas Mardt, Frank Noe
Summary: Recent advances in deep learning frameworks have provided valuable tools for analyzing the long-timescale behavior of complex systems, particularly in the field of biophysics. The method includes physical constraints like time-reversibility and incorporates experimental observables to compensate for biases in simulation data. A new neural network layer is developed for a hierarchical model with an attention mechanism highlighting important residues for classification.
JOURNAL OF CHEMICAL PHYSICS
(2021)
Article
Chemistry, Physical
Mohsen Sadeghi, Frank Noe
Summary: Biomembrane remodeling is crucial for cellular trafficking, with membrane-binding proteins being key players. A coarse-grained model parametrized to reflect local curvatures and lateral dynamics of proteins was developed to study the formation and breakup of protein clusters on the membrane surface, demonstrating the role of protein flexibility and concentration in aggregation behavior.
JOURNAL OF PHYSICAL CHEMISTRY LETTERS
(2021)
Article
Multidisciplinary Sciences
Kalyan S. Chakrabarti, Simon Olsson, Supriya Pratihar, Karin Giller, Kerstin Overkamp, Ko On Lee, Vytautas Gapsys, Kyoung-Seok Ryu, Bert L. de Groot, Frank Noe, Stefan Becker, Donghan Lee, Thomas R. Weikl, Christian Griesinger
Summary: The study presents a theoretical and experimental framework to investigate protein binding mechanisms on sub-millisecond timescales. Using nuclear magnetic resonance and molecular dynamics simulations, the authors find that the binding mechanism between ubiquitin and the SH3 domain is based on conformational selection.
NATURE COMMUNICATIONS
(2022)
Article
Chemistry, Physical
Felix Musil, Iryna Zaporozhets, Frank Noe, Cecilia Clementi, Venkat Kapil
Summary: This research develops a method for accurately calculating vibrational spectra of molecular systems using a reduced computational cost path-integral formulation. By leveraging advances in machine-learned coarse-graining and a simple temperature elevation scheme, significant computational savings and improved accuracy are achieved compared to more expensive reference approaches. This method has the potential for routine calculations of vibrational spectra for a wide range of molecular systems with an explicit treatment of the quantum nature of nuclei.
JOURNAL OF CHEMICAL PHYSICS
(2022)
Article
Biochemistry & Molecular Biology
Tim Hempel, Simon Olsson, Frank Noe
Summary: With recent advances in structural biology, scalable molecular dynamics methods are required for large biomolecular systems. Current approaches focus on global state modeling, but are not applicable to large-scale systems. To address this, we propose using a set of coupled models to describe the local structure of molecular systems. Markov field models, including various models, are evaluated for their use in computational molecular biology.
CURRENT OPINION IN STRUCTURAL BIOLOGY
(2022)
Article
Multidisciplinary Sciences
Andreas Mardt, Tim Hempel, Cecilia Clementi, Frank Noe
Summary: This study addresses the challenge of modeling the dynamics of large molecular systems by introducing a method that simultaneously decomposes and models the system, providing an effective summary of the complex dynamics. While the issue of learning the dynamical coupling between subsystems still remains, it is a significant step towards learning Ising models of large molecular complexes from simulation data.
NATURE COMMUNICATIONS
(2022)
Article
Chemistry, Physical
Z. Schaetzle, P. B. Szabo, M. Mezera, J. Hermann, F. Noe
Summary: Computing accurate and efficient approximations to solve the Schrödinger equation in computational chemistry has been a challenge for decades. Quantum Monte Carlo methods, with their highly parallel and scalable algorithm, show promise in achieving high accuracy in a variety of molecular systems. The use of machine-learned parametrizations, relying on neural networks as universal function approximators, has further improved the accuracy of these methods. The development of software libraries like DEEPQMC aims to provide a common framework for future investigations and make this field accessible to practitioners from both the quantum chemistry and machine learning communities.
JOURNAL OF CHEMICAL PHYSICS
(2023)
Review
Chemistry, Multidisciplinary
Jan Hermann, James Spencer, Kenny Choo, Antonio Mezzacapo, W. M. C. Foulkes, David Pfau, Giuseppe Carleo, Frank Noe
Summary: Deep learning methods have surpassed human capabilities in pattern recognition and data processing, and have become increasingly important in scientific discovery. In molecular science, a key application of machine learning is to learn potential energy surfaces or force fields from ab initio solutions of the electronic Schrodinger equation obtained with various quantum chemistry methods. This review discusses a complementary approach that uses machine learning to directly solve quantum chemistry problems from first principles, focusing on quantum Monte Carlo methods with neural-network ansatzes to solve the electronic Schrodinger equation.
NATURE REVIEWS CHEMISTRY
(2023)
Article
Physics, Multidisciplinary
Paolo A. Erdman, Alberto Rolandi, Paolo Abiuso, Marti Perarnau-Llobet, Frank Noe
Summary: The full optimization of a quantum heat engine requires trade-offs between power, efficiency, and fluctuations. A general framework is proposed to identify Pareto-optimal cycles that balance these objectives. Reinforcement learning is used to find the Pareto front of a quantum dot-based engine, revealing abrupt changes in optimal cycles when switching between optimizing two and three objectives. Analytical results accurately describe different regions of the Pareto front in fast- and slow-driving regimes.
PHYSICAL REVIEW RESEARCH
(2023)
Article
Physics, Multidisciplinary
Manuel Dibak, Leon Klein, Andreas Kraemer, Frank Noe
Summary: Boltzmann generators solve the sampling problem in many-body physics by combining a normalizing flow and a statistical reweighting method. Temperature steerable flows (TSFs) are proposed to generate a family of probability densities parametrized by a choosable temperature parameter, allowing for sampling of a physical system across multiple thermodynamic states.
PHYSICAL REVIEW RESEARCH
(2022)
Article
Computer Science, Artificial Intelligence
Soren Ager Meldgaard, Jonas Koehler, Henrik Lund Mortensen, Mads-Peter Christiansen, Frank Noe, Bjork Hammer
Summary: This study proposes a reinforcement learning approach for generating molecules in chemical space and predicting their stability using quantum chemistry. By combining imitation learning and reinforcement learning, the sample efficiency is improved, and low energy molecules are generated under different stoichiometries conditions.
MACHINE LEARNING-SCIENCE AND TECHNOLOGY
(2022)
Article
Chemistry, Multidisciplinary
Tim Hempel, Lluis Raich, Simon Olsson, Nurit P. Azouz, Andrea M. Klingler, Markus Hoffmann, Stefan Pohlmann, Marc E. Rothenberg, Frank Noe
Summary: The study elucidates the molecular mechanism of camostat and nafamostat inhibiting the entry of SARS-CoV-2 into human lung cells and provides a model of the drugs binding to TMPRSS2. Nafamostat is found to have a higher propensity to form a stable covalent enzyme-substrate intermediate compared to camostat, explaining its higher potency.