1
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Donkor ED, Offei-Danso A, Rodriguez A, Sciortino F, Hassanali A. Beyond Local Structures in Critical Supercooled Water through Unsupervised Learning. J Phys Chem Lett 2024; 15:3996-4005. [PMID: 38574274 DOI: 10.1021/acs.jpclett.4c00383] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/06/2024]
Abstract
The presence of a second critical point in water has been a topic of intense investigation for the last few decades. The molecular origins underlying this phenomenon are typically rationalized in terms of the competition between local high-density (HD) and low-density (LD) structures. Their identification often requires designing parameters that are subject to human intervention. Herein, we use unsupervised learning to discover structures in atomistic simulations of water close to the liquid-liquid critical point (LLCP). Encoding the information on the environment using local descriptors, we do not find evidence for two distinct thermodynamic structures. In contrast, when we deploy nonlocal descriptors that probe instead heterogeneities on the nanometer length scale, this leads to the emergence of LD and HD domains rationalizing the microscopic origins of the density fluctuations close to criticality.
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Affiliation(s)
- Edward Danquah Donkor
- The Abdus Salam International Center for Theoretical Physics (ICTP), Strada Costiera 11, 34151 Trieste, Italy
- Scuola Internazionale Superiore di Studi Avanzati (SISSA), via Bonomea 265, 34136 Trieste, Italy
| | - Adu Offei-Danso
- The Abdus Salam International Center for Theoretical Physics (ICTP), Strada Costiera 11, 34151 Trieste, Italy
- Scuola Internazionale Superiore di Studi Avanzati (SISSA), via Bonomea 265, 34136 Trieste, Italy
| | - Alex Rodriguez
- The Abdus Salam International Center for Theoretical Physics (ICTP), Strada Costiera 11, 34151 Trieste, Italy
- Dipartimento di Matematica, Informatica e Geoscienze, Università degli studi di Trieste, via Valerio 12/1, 34127 Trieste, Italy
| | - Francesco Sciortino
- Dipartimento di Fisica, Sapienza Università di Roma, P. le Aldo Moro 5, 00185 Rome, Italy
| | - Ali Hassanali
- The Abdus Salam International Center for Theoretical Physics (ICTP), Strada Costiera 11, 34151 Trieste, Italy
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2
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Leroux J, Kotobi A, Hirsch K, Lau T, Ortiz-Mahecha C, Maksimov D, Meißner R, Oostenrijk B, Rossi M, Schubert K, Timm M, Trinter F, Unger I, Zamudio-Bayer V, Schwob L, Bari S. Mapping the electronic transitions of protonation sites in peptides using soft X-ray action spectroscopy. Phys Chem Chem Phys 2023; 25:25603-25618. [PMID: 37721108 DOI: 10.1039/d3cp02524a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/19/2023]
Abstract
Near-edge X-ray absorption mass spectrometry (NEXAMS) around the nitrogen and oxygen K-edges was employed on gas-phase peptides to probe the electronic transitions related to their protonation sites, namely at basic side chains, the N-terminus and the amide oxygen. The experimental results are supported by replica exchange molecular dynamics and density-functional theory and restricted open-shell configuration with single calculations to attribute the transitions responsible for the experimentally observed resonances. We studied five tailor-made glycine-based pentapeptides, where we identified the signature of the protonation site of N-terminal proline, histidine, lysine and arginine, at 406 eV, corresponding to N 1s → σ*(NHx+) (x = 2 or 3) transitions, depending on the peptides. We compared the spectra of pentaglycine and triglycine to evaluate the sensitivity of NEXAMS to protomers. Separate resonances have been identified to distinguish two protomers in triglycine, the protonation site at the N-terminus at 406 eV and the protonation site at the amide oxygen characterized by a transition at 403.1 eV.
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Affiliation(s)
- Juliette Leroux
- CIMAP, CEA/CNRS/ENSICAEN/Université de Caen Normandie, 14050 Caen, France
- Deutsches Elektronen-Synchrotron DESY, Germany.
| | - Amir Kotobi
- Deutsches Elektronen-Synchrotron DESY, Germany.
- Helmholtz-Zentrum Hereon, Institute of Surface Science, 21502 Geesthacht, Germany
| | - Konstantin Hirsch
- Abteilung Hochempfindliche Röntgenspektroskopie, Helmholtz-Zentrum Berlin für Materialien und Energie, 12489 Berlin, Germany
| | - Tobias Lau
- Abteilung Hochempfindliche Röntgenspektroskopie, Helmholtz-Zentrum Berlin für Materialien und Energie, 12489 Berlin, Germany
| | - Carlos Ortiz-Mahecha
- Hamburg University of Technology, Institute of Polymers and Composites, 21073 Hamburg, Germany
| | - Dmitrii Maksimov
- Max Planck Institute for the Structure and Dynamics of Matter, 22761 Hamburg, Germany
| | - Robert Meißner
- Helmholtz-Zentrum Hereon, Institute of Surface Science, 21502 Geesthacht, Germany
- Hamburg University of Technology, Institute of Polymers and Composites, 21073 Hamburg, Germany
| | | | - Mariana Rossi
- Max Planck Institute for the Structure and Dynamics of Matter, 22761 Hamburg, Germany
| | | | - Martin Timm
- Abteilung Hochempfindliche Röntgenspektroskopie, Helmholtz-Zentrum Berlin für Materialien und Energie, 12489 Berlin, Germany
| | - Florian Trinter
- Institut für Kernphysik, Goethe-Universität Frankfurt am Main, 60438 Frankfurt am Main, Germany
- Molecular Physics, Fritz-Haber-Institut der Max-Planck-Gesellschaft, 14195 Berlin, Germany
| | - Isaak Unger
- Department of Physics and Astronomy, Uppsala University, 75120 Uppsala, Sweden
| | - Vicente Zamudio-Bayer
- Abteilung Hochempfindliche Röntgenspektroskopie, Helmholtz-Zentrum Berlin für Materialien und Energie, 12489 Berlin, Germany
| | | | - Sadia Bari
- Deutsches Elektronen-Synchrotron DESY, Germany.
- The Hamburg Centre for Ultrafast Imaging, Hamburg, Germany
- Zernike Institute for Advanced Materials, University of Groningen, 9747 AG Groningen, The Netherlands
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3
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Crippa M, Cardellini A, Caruso C, Pavan GM. Detecting dynamic domains and local fluctuations in complex molecular systems via timelapse neighbors shuffling. Proc Natl Acad Sci U S A 2023; 120:e2300565120. [PMID: 37467266 PMCID: PMC10372573 DOI: 10.1073/pnas.2300565120] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Accepted: 05/25/2023] [Indexed: 07/21/2023] Open
Abstract
It is known that the behavior of many complex systems is controlled by local dynamic rearrangements or fluctuations occurring within them. Complex molecular systems, composed of many molecules interacting with each other in a Brownian storm, make no exception. Despite the rise of machine learning and of sophisticated structural descriptors, detecting local fluctuations and collective transitions in complex dynamic ensembles remains often difficult. Here, we show a machine learning framework based on a descriptor which we name Local Environments and Neighbors Shuffling (LENS), that allows identifying dynamic domains and detecting local fluctuations in a variety of systems in an abstract and efficient way. By tracking how much the microscopic surrounding of each molecular unit changes over time in terms of neighbor individuals, LENS allows characterizing the global (macroscopic) dynamics of molecular systems in phase transition, phases-coexistence, as well as intrinsically characterized by local fluctuations (e.g., defects). Statistical analysis of the LENS time series data extracted from molecular dynamics trajectories of, for example, liquid-like, solid-like, or dynamically diverse complex molecular systems allows tracking in an efficient way the presence of different dynamic domains and of local fluctuations emerging within them. The approach is found robust, versatile, and applicable independently of the features of the system and simply provided that a trajectory containing information on the relative motion of the interacting units is available. We envisage that "such a LENS" will constitute a precious basis for exploring the dynamic complexity of a variety of systems and, given its abstract definition, not necessarily of molecular ones.
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Affiliation(s)
- Martina Crippa
- Department of Applied Science and Technology, Politecnico di Torino, Torino10129, Italy
| | - Annalisa Cardellini
- Department of Innovative Technologies, University of Applied Sciences and Arts of Southern Switzerland, Lugano-Viganello6962, Switzerland
| | - Cristina Caruso
- Department of Applied Science and Technology, Politecnico di Torino, Torino10129, Italy
| | - Giovanni M. Pavan
- Department of Applied Science and Technology, Politecnico di Torino, Torino10129, Italy
- Department of Innovative Technologies, University of Applied Sciences and Arts of Southern Switzerland, Lugano-Viganello6962, Switzerland
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4
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Leanza L, Perego C, Pesce L, Salvalaglio M, von Delius M, Pavan GM. Into the dynamics of rotaxanes at atomistic resolution. Chem Sci 2023; 14:6716-6729. [PMID: 37350834 PMCID: PMC10283497 DOI: 10.1039/d3sc01593a] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Accepted: 05/04/2023] [Indexed: 06/24/2023] Open
Abstract
Mechanically-interlocked molecules (MIMs) are at the basis of artificial molecular machines and are attracting increasing interest for various applications, from catalysis to drug delivery and nanoelectronics. MIMs are composed of mechanically-interconnected molecular sub-parts that can move with respect to each other, imparting these systems innately dynamical behaviors and interesting stimuli-responsive properties. The rational design of MIMs with desired functionalities requires studying their dynamics at sub-molecular resolution and on relevant timescales, which is challenging experimentally and computationally. Here, we combine molecular dynamics and metadynamics simulations to reconstruct the thermodynamics and kinetics of different types of MIMs at atomistic resolution under different conditions. As representative case studies, we use rotaxanes and molecular shuttles substantially differing in structure, architecture, and dynamical behavior. Our computational approach provides results in agreement with the available experimental evidence and a direct demonstration of the critical effect of the solvent on the dynamics of the MIMs. At the same time, our simulations unveil key factors controlling the dynamics of these systems, providing submolecular-level insights into the mechanisms and kinetics of shuttling. Reconstruction of the free-energy profiles from the simulations reveals details of the conformations of macrocycles on the binding site that are difficult to access via routine experiments and precious for understanding the MIMs' behavior, while their decomposition in enthalpic and entropic contributions unveils the mechanisms and key transitions ruling the intermolecular movements between metastable states within them. The computational framework presented herein is flexible and can be used, in principle, to study a variety of mechanically-interlocked systems.
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Affiliation(s)
- Luigi Leanza
- Department of Applied Science and Technology, Politecnico di Torino Corso Duca degli Abruzzi, 24 10129 Torino Italy
| | - Claudio Perego
- Department of Innovative Technologies, University of Applied Sciences and Arts of Southern Switzerland, Polo Universitario Lugano Campus Est, Via la Santa 1 6962 Lugano-Viganello Switzerland
| | - Luca Pesce
- Department of Innovative Technologies, University of Applied Sciences and Arts of Southern Switzerland, Polo Universitario Lugano Campus Est, Via la Santa 1 6962 Lugano-Viganello Switzerland
| | - Matteo Salvalaglio
- Department of Chemical Engineering, University College London London WC1E 7JE UK
| | - Max von Delius
- Institute of Organic Chemistry, Ulm University Albert-Einstein-Allee 11 89081 Ulm Germany
| | - Giovanni M Pavan
- Department of Applied Science and Technology, Politecnico di Torino Corso Duca degli Abruzzi, 24 10129 Torino Italy
- Department of Innovative Technologies, University of Applied Sciences and Arts of Southern Switzerland, Polo Universitario Lugano Campus Est, Via la Santa 1 6962 Lugano-Viganello Switzerland
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5
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Ricci E, Vergadou N. Integrating Machine Learning in the Coarse-Grained Molecular Simulation of Polymers. J Phys Chem B 2023; 127:2302-2322. [PMID: 36888553 DOI: 10.1021/acs.jpcb.2c06354] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/09/2023]
Abstract
Machine learning (ML) is having an increasing impact on the physical sciences, engineering, and technology and its integration into molecular simulation frameworks holds great potential to expand their scope of applicability to complex materials and facilitate fundamental knowledge and reliable property predictions, contributing to the development of efficient materials design routes. The application of ML in materials informatics in general, and polymer informatics in particular, has led to interesting results, however great untapped potential lies in the integration of ML techniques into the multiscale molecular simulation methods for the study of macromolecular systems, specifically in the context of Coarse Grained (CG) simulations. In this Perspective, we aim at presenting the pioneering recent research efforts in this direction and discussing how these new ML-based techniques can contribute to critical aspects of the development of multiscale molecular simulation methods for bulk complex chemical systems, especially polymers. Prerequisites for the implementation of such ML-integrated methods and open challenges that need to be met toward the development of general systematic ML-based coarse graining schemes for polymers are discussed.
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Affiliation(s)
- Eleonora Ricci
- Institute of Nanoscience and Nanotechnology, National Center for Scientific Research "Demokritos", GR-15341 Agia Paraskevi, Athens, Greece
- Institute of Informatics and Telecommunications, National Center for Scientific Research "Demokritos", GR-15341 Agia Paraskevi, Athens, Greece
| | - Niki Vergadou
- Institute of Nanoscience and Nanotechnology, National Center for Scientific Research "Demokritos", GR-15341 Agia Paraskevi, Athens, Greece
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6
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Cardellini A, Crippa M, Lionello C, Afrose SP, Das D, Pavan GM. Unsupervised Data-Driven Reconstruction of Molecular Motifs in Simple to Complex Dynamic Micelles. J Phys Chem B 2023; 127:2595-2608. [PMID: 36891625 PMCID: PMC10041528 DOI: 10.1021/acs.jpcb.2c08726] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/10/2023]
Abstract
The reshuffling mobility of molecular building blocks in self-assembled micelles is a key determinant of many their interesting properties, from emerging morphologies and surface compartmentalization, to dynamic reconfigurability and stimuli-responsiveness. However, the microscopic details of such complex structural dynamics are typically nontrivial to elucidate, especially in multicomponent assemblies. Here we show a machine-learning approach that allows us to reconstruct the structural and dynamic complexity of mono- and bicomponent surfactant micelles from high-dimensional data extracted from equilibrium molecular dynamics simulations. Unsupervised clustering of smooth overlap of atomic position (SOAP) data enables us to identify, in a set of multicomponent surfactant micelles, the dominant local molecular environments that emerge within them and to retrace their dynamics, in terms of exchange probabilities and transition pathways of the constituent building blocks. Tested on a variety of micelles differing in size and in the chemical nature of the constitutive self-assembling units, this approach effectively recognizes the molecular motifs populating them in an exquisitely agnostic and unsupervised way, and allows correlating them to their composition in terms of constitutive surfactant species.
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Affiliation(s)
- Annalisa Cardellini
- Department of Innovative Technologies, University of Applied Sciences and Arts of Southern Switzerland, Polo Universitario Lugano, Campus Est, Via la Santa 1, 6962 Lugano-Viganello, Switzerland
| | - Martina Crippa
- Department of Applied Science and Technology, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Torino, Italy
| | - Chiara Lionello
- Department of Applied Science and Technology, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Torino, Italy
| | - Syed Pavel Afrose
- Department of Chemical Sciences and Centre for Advanced Functional Materials, Indian Institute of Science Education and Research (IISER) Kolkata, Mohanpur 741246, India
| | - Dibyendu Das
- Department of Chemical Sciences and Centre for Advanced Functional Materials, Indian Institute of Science Education and Research (IISER) Kolkata, Mohanpur 741246, India
| | - Giovanni M Pavan
- Department of Innovative Technologies, University of Applied Sciences and Arts of Southern Switzerland, Polo Universitario Lugano, Campus Est, Via la Santa 1, 6962 Lugano-Viganello, Switzerland
- Department of Applied Science and Technology, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Torino, Italy
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7
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Kotobi A, Schwob L, Vonbun-Feldbauer GB, Rossi M, Gasparotto P, Feiler C, Berden G, Oomens J, Oostenrijk B, Scuderi D, Bari S, Meißner RH. Reconstructing the infrared spectrum of a peptide from representative conformers of the full canonical ensemble. Commun Chem 2023; 6:46. [PMID: 36869192 PMCID: PMC9984374 DOI: 10.1038/s42004-023-00835-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Accepted: 02/08/2023] [Indexed: 03/05/2023] Open
Abstract
Leucine enkephalin (LeuEnk), a biologically active endogenous opioid pentapeptide, has been under intense investigation because it is small enough to allow efficient use of sophisticated computational methods and large enough to provide insights into low-lying minima of its conformational space. Here, we reproduce and interpret experimental infrared (IR) spectra of this model peptide in gas phase using a combination of replica-exchange molecular dynamics simulations, machine learning, and ab initio calculations. In particular, we evaluate the possibility of averaging representative structural contributions to obtain an accurate computed spectrum that accounts for the corresponding canonical ensemble of the real experimental situation. Representative conformers are identified by partitioning the conformational phase space into subensembles of similar conformers. The IR contribution of each representative conformer is calculated from ab initio and weighted according to the population of each cluster. Convergence of the averaged IR signal is rationalized by merging contributions in a hierarchical clustering and the comparison to IR multiple photon dissociation experiments. The improvements achieved by decomposing clusters containing similar conformations into even smaller subensembles is strong evidence that a thorough assessment of the conformational landscape and the associated hydrogen bonding is a prerequisite for deciphering important fingerprints in experimental spectroscopic data.
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Affiliation(s)
- Amir Kotobi
- grid.7683.a0000 0004 0492 0453Deutsches Elektronen-Synchrotron DESY, Hamburg, Germany
| | - Lucas Schwob
- Deutsches Elektronen-Synchrotron DESY, Hamburg, Germany.
| | - Gregor B. Vonbun-Feldbauer
- grid.6884.20000 0004 0549 1777Hamburg University of Technology, Institute of Advanced Ceramics, Hamburg, Germany
| | - Mariana Rossi
- grid.469852.40000 0004 1796 3508Max Planck Institute for the Structure and Dynamics of Matter, Hamburg, Germany
| | - Piero Gasparotto
- grid.5991.40000 0001 1090 7501Scientific Computing Division, Paul Scherrer Institute, Villigen, Switzerland
| | - Christian Feiler
- grid.24999.3f0000 0004 0541 3699Helmholtz-Zentrum Hereon, Institute of Surface Science, Geesthacht, Germany
| | - Giel Berden
- grid.5590.90000000122931605Radboud University, Institute for Molecules and Materials, FELIX Laboratory, Nijmegen, The Netherlands
| | - Jos Oomens
- grid.5590.90000000122931605Radboud University, Institute for Molecules and Materials, FELIX Laboratory, Nijmegen, The Netherlands
| | - Bart Oostenrijk
- grid.7683.a0000 0004 0492 0453Deutsches Elektronen-Synchrotron DESY, Hamburg, Germany ,grid.9026.d0000 0001 2287 2617The Hamburg Centre for Ultrafast Imaging, Hamburg, Germany
| | - Debora Scuderi
- grid.503243.3Institut de Chimie Physique, CNRS UMR8000, Université Paris-Saclay, Orsay, France
| | - Sadia Bari
- Deutsches Elektronen-Synchrotron DESY, Hamburg, Germany. .,The Hamburg Centre for Ultrafast Imaging, Hamburg, Germany. .,Zernike Institute for Advanced Materials, University of Groningen, Groningen, The Netherlands.
| | - Robert H. Meißner
- grid.24999.3f0000 0004 0541 3699Helmholtz-Zentrum Hereon, Institute of Surface Science, Geesthacht, Germany ,grid.6884.20000 0004 0549 1777Hamburg University of Technology, Institute of Polymers and Composites, Hamburg, Germany
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8
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Lionello C, Perego C, Gardin A, Klajn R, Pavan GM. Supramolecular Semiconductivity through Emerging Ionic Gates in Ion-Nanoparticle Superlattices. ACS NANO 2023; 17:275-287. [PMID: 36548051 PMCID: PMC9835987 DOI: 10.1021/acsnano.2c07558] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Accepted: 12/09/2022] [Indexed: 06/17/2023]
Abstract
The self-assembly of nanoparticles driven by small molecules or ions may produce colloidal superlattices with features and properties reminiscent of those of metals or semiconductors. However, to what extent the properties of such supramolecular crystals actually resemble those of atomic materials often remains unclear. Here, we present coarse-grained molecular simulations explicitly demonstrating how a behavior evocative of that of semiconductors may emerge in a colloidal superlattice. As a case study, we focus on gold nanoparticles bearing positively charged groups that self-assemble into FCC crystals via mediation by citrate counterions. In silico ohmic experiments show how the dynamically diverse behavior of the ions in different superlattice domains allows the opening of conductive ionic gates above certain levels of applied electric fields. The observed binary conductive/nonconductive behavior is reminiscent of that of conventional semiconductors, while, at a supramolecular level, crossing the "band gap" requires a sufficient electrostatic stimulus to break the intermolecular interactions and make ions diffuse throughout the superlattice's cavities.
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Affiliation(s)
- Chiara Lionello
- Department
of Applied Science and Technology, Politecnico
di Torino, Corso Duca degli Abruzzi 24, 10129 Torino, Italy
| | - Claudio Perego
- Department
of Innovative Technologies, University of
Applied Sciences and Arts of Southern Switzerland, Polo Universitario
Lugano, Campus Est, Via
la Santa 1, 6962 Lugano-Viganello, Switzerland
| | - Andrea Gardin
- Department
of Applied Science and Technology, Politecnico
di Torino, Corso Duca degli Abruzzi 24, 10129 Torino, Italy
| | - Rafal Klajn
- Department
of Organic Chemistry, Weizmann Institute
of Science, Rehovot 76100, Israel
| | - Giovanni M. Pavan
- Department
of Applied Science and Technology, Politecnico
di Torino, Corso Duca degli Abruzzi 24, 10129 Torino, Italy
- Department
of Innovative Technologies, University of
Applied Sciences and Arts of Southern Switzerland, Polo Universitario
Lugano, Campus Est, Via
la Santa 1, 6962 Lugano-Viganello, Switzerland
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9
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Gabellini C, Şologan M, Pellizzoni E, Marson D, Daka M, Franchi P, Bignardi L, Franchi S, Posel Z, Baraldi A, Pengo P, Lucarini M, Pasquato L, Posocco P. Spotting Local Environments in Self-Assembled Monolayer-Protected Gold Nanoparticles. ACS NANO 2022; 16:20902-20914. [PMID: 36459668 PMCID: PMC9798909 DOI: 10.1021/acsnano.2c08467] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Accepted: 11/29/2022] [Indexed: 06/17/2023]
Abstract
Organic-inorganic (O-I) nanomaterials are versatile platforms for an incredible high number of applications, ranging from heterogeneous catalysis to molecular sensing, cell targeting, imaging, and cancer diagnosis and therapy, just to name a few. Much of their potential stems from the unique control of organic environments around inorganic sites within a single O-I nanomaterial, which allows for new properties that were inaccessible using purely organic or inorganic materials. Structural and mechanistic characterization plays a key role in understanding and rationally designing such hybrid nanoconstructs. Here, we introduce a general methodology to identify and classify local (supra)molecular environments in an archetypal class of O-I nanomaterials, i.e., self-assembled monolayer-protected gold nanoparticles (SAM-AuNPs). By using an atomistic machine-learning guided workflow based on the Smooth Overlap of Atomic Positions (SOAP) descriptor, we analyze a collection of chemically different SAM-AuNPs and detect and compare local environments in a way that is agnostic and automated, i.e., with no need of a priori information and minimal user intervention. In addition, the computational results coupled with experimental electron spin resonance measurements prove that is possible to have more than one local environment inside SAMs, being the thickness of the organic shell and solvation primary factors in the determining number and nature of multiple coexisting environments. These indications are extended to complex mixed hydrophilic-hydrophobic SAMs. This work demonstrates that it is possible to spot and compare local molecular environments in SAM-AuNPs exploiting atomistic machine-learning approaches, establishes ground rules to control them, and holds the potential for the rational design of O-I nanomaterials instructed from data.
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Affiliation(s)
- Cristian Gabellini
- Department
of Engineering and Architecture, University
of Trieste, 34127 Trieste, Italy
| | - Maria Şologan
- Department
of Chemical and Pharmaceutical Sciences and INSTM Trieste Research
Unit, University of Trieste, 34127 Trieste, Italy
| | - Elena Pellizzoni
- Department
of Chemical and Pharmaceutical Sciences and INSTM Trieste Research
Unit, University of Trieste, 34127 Trieste, Italy
| | - Domenico Marson
- Department
of Engineering and Architecture, University
of Trieste, 34127 Trieste, Italy
| | - Mario Daka
- Department
of Chemical and Pharmaceutical Sciences and INSTM Trieste Research
Unit, University of Trieste, 34127 Trieste, Italy
| | - Paola Franchi
- Department
of Chemistry “G. Ciamician”, University of Bologna, I-40126 Bologna, Italy
| | - Luca Bignardi
- Department
of Physics, University of Trieste, 34127 Trieste, Italy
| | - Stefano Franchi
- Elettra
Sincrotrone Trieste, 34149 Basovizza, Trieste, Italy
| | - Zbyšek Posel
- Department
of Informatics, Jan Evangelista Purkyně
University, 400 96 Ústí nad Labem, Czech Republic
| | | | - Paolo Pengo
- Department
of Chemical and Pharmaceutical Sciences and INSTM Trieste Research
Unit, University of Trieste, 34127 Trieste, Italy
| | - Marco Lucarini
- Department
of Chemistry “G. Ciamician”, University of Bologna, I-40126 Bologna, Italy
| | - Lucia Pasquato
- Department
of Chemical and Pharmaceutical Sciences and INSTM Trieste Research
Unit, University of Trieste, 34127 Trieste, Italy
| | - Paola Posocco
- Department
of Engineering and Architecture, University
of Trieste, 34127 Trieste, Italy
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10
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Gardin A, Perego C, Doni G, Pavan GM. Classifying soft self-assembled materials via unsupervised machine learning of defects. Commun Chem 2022; 5:82. [PMID: 36697761 PMCID: PMC9814741 DOI: 10.1038/s42004-022-00699-z] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Accepted: 06/29/2022] [Indexed: 01/28/2023] Open
Abstract
Unlike molecular crystals, soft self-assembled fibers, micelles, vesicles, etc., exhibit a certain order in the arrangement of their constitutive monomers but also high structural dynamicity and variability. Defects and disordered local domains that continuously form-and-repair in their structures impart to such materials unique adaptive and dynamical properties, which make them, e.g., capable to communicate with each other. However, objective criteria to compare such complex dynamical features and to classify soft supramolecular materials are non-trivial to attain. Here we show a data-driven workflow allowing us to achieve this goal. Building on unsupervised clustering of Smooth Overlap of Atomic Position (SOAP) data obtained from equilibrium molecular dynamics simulations, we can compare a variety of soft supramolecular assemblies via a robust SOAP metric. This provides us with a data-driven "defectometer" to classify different types of supramolecular materials based on the structural dynamics of the ordered/disordered local molecular environments that statistically emerge within them.
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Affiliation(s)
- Andrea Gardin
- Department of Applied Science and Technology, Politecnico di Torino, Torino, Italy
| | - Claudio Perego
- Department of Innovative Technologies, University of Applied Sciences and Arts of Southern Switzerland, Lugano-Viganello, Switzerland
| | - Giovanni Doni
- Department of Innovative Technologies, University of Applied Sciences and Arts of Southern Switzerland, Lugano-Viganello, Switzerland
| | - Giovanni M Pavan
- Department of Applied Science and Technology, Politecnico di Torino, Torino, Italy. .,Department of Innovative Technologies, University of Applied Sciences and Arts of Southern Switzerland, Lugano-Viganello, Switzerland.
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11
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Offei-Danso A, Hassanali A, Rodriguez A. High-Dimensional Fluctuations in Liquid Water: Combining Chemical Intuition with Unsupervised Learning. J Chem Theory Comput 2022; 18:3136-3150. [PMID: 35472272 DOI: 10.1021/acs.jctc.1c01292] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
The microscopic description of the local structure of water remains an open challenge. Here, we adopt an agnostic approach to understanding water's hydrogen bond network using data harvested from molecular dynamics simulations of an empirical water model. A battery of state-of-the-art unsupervised data-science techniques are used to characterize the free-energy landscape of water starting from encoding the water environment using local atomic descriptors, through dimensionality reduction and finally the use of advanced clustering techniques. Analysis of the free energy under ambient conditions was found to be consistent with a rough single basin and independent of the choice of the water model. We find that the fluctuations of the water network occur in a high-dimensional space, which we characterize using a combination of both atomic descriptors and chemical-intuition-based coordinates. We demonstrate that a combination of both types of variables is needed in order to adequately capture the complexity of the fluctuations in the hydrogen bond network at different length scales both at room temperature and also close to the critical point of water. Our results provide a general framework for examining fluctuations in water under different conditions.
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Affiliation(s)
- Adu Offei-Danso
- The Abdus Salam International Center for Theoretical Physics, Strada Costiera 11, 34151 Trieste, Italy.,SISSA─International School for Advanced Studies, Via Bonomea 265, 34136 Trieste, Italy
| | - Ali Hassanali
- The Abdus Salam International Center for Theoretical Physics, Strada Costiera 11, 34151 Trieste, Italy
| | - Alex Rodriguez
- The Abdus Salam International Center for Theoretical Physics, Strada Costiera 11, 34151 Trieste, Italy
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12
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Fabregat R, Fabrizio A, Engel EA, Meyer B, Juraskova V, Ceriotti M, Corminboeuf C. Local Kernel Regression and Neural Network Approaches to the Conformational Landscapes of Oligopeptides. J Chem Theory Comput 2022; 18:1467-1479. [PMID: 35179897 PMCID: PMC8908737 DOI: 10.1021/acs.jctc.1c00813] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
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The application of
machine learning to theoretical chemistry has
made it possible to combine the accuracy of quantum chemical energetics
with the thorough sampling of finite-temperature fluctuations. To
reach this goal, a diverse set of methods has been proposed, ranging
from simple linear models to kernel regression and highly nonlinear
neural networks. Here we apply two widely different approaches to
the same, challenging problem: the sampling of the conformational
landscape of polypeptides at finite temperature. We develop a local
kernel regression (LKR) coupled with a supervised sparsity method
and compare it with a more established approach based on Behler-Parrinello
type neural networks. In the context of the LKR, we discuss how the
supervised selection of the reference pool of environments is crucial
to achieve accurate potential energy surfaces at a competitive computational
cost and leverage the locality of the model to infer which chemical
environments are poorly described by the DFTB baseline. We then discuss
the relative merits of the two frameworks and perform Hamiltonian-reservoir
replica-exchange Monte Carlo sampling and metadynamics simulations,
respectively, to demonstrate that both frameworks can achieve converged
and transferable sampling of the conformational landscape of complex
and flexible biomolecules with comparable accuracy and computational
cost.
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Affiliation(s)
| | | | - Edgar A Engel
- Laboratory of Computational Science and Modeling, IMX, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland
| | | | | | - Michele Ceriotti
- Laboratory of Computational Science and Modeling, IMX, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland
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13
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Bian T, Gardin A, Gemen J, Houben L, Perego C, Lee B, Elad N, Chu Z, Pavan GM, Klajn R. Electrostatic co-assembly of nanoparticles with oppositely charged small molecules into static and dynamic superstructures. Nat Chem 2021; 13:940-949. [PMID: 34489564 PMCID: PMC7611764 DOI: 10.1038/s41557-021-00752-9] [Citation(s) in RCA: 83] [Impact Index Per Article: 27.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2020] [Accepted: 06/14/2021] [Indexed: 02/08/2023]
Abstract
Coulombic interactions can be used to assemble charged nanoparticles into higher-order structures, but the process requires oppositely charged partners that are similarly sized. The ability to mediate the assembly of such charged nanoparticles using structurally simple small molecules would greatly facilitate the fabrication of nanostructured materials and harnessing their applications in catalysis, sensing and photonics. Here we show that small molecules with as few as three electric charges can effectively induce attractive interactions between oppositely charged nanoparticles in water. These interactions can guide the assembly of charged nanoparticles into colloidal crystals of a quality previously only thought to result from their co-crystallization with oppositely charged nanoparticles of a similar size. Transient nanoparticle assemblies can be generated using positively charged nanoparticles and multiply charged anions that are enzymatically hydrolysed into mono- and/or dianions. Our findings demonstrate an approach for the facile fabrication, manipulation and further investigation of static and dynamic nanostructured materials in aqueous environments.
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Affiliation(s)
- Tong Bian
- Department of Organic Chemistry, Weizmann Institute of Science,
Rehovot 76100, Israel
| | - Andrea Gardin
- Department of Innovative Technologies, University of Applied
Sciences and Arts of Southern Switzerland, CH-6928 Manno, Switzerland,Department of Applied Science and Technology, Politecnico di Torino,
10129 Torino, Italy
| | - Julius Gemen
- Department of Organic Chemistry, Weizmann Institute of Science,
Rehovot 76100, Israel
| | - Lothar Houben
- Department of Chemical Research Support, Weizmann Institute of
Science, Rehovot 76100, Israel
| | - Claudio Perego
- Department of Innovative Technologies, University of Applied
Sciences and Arts of Southern Switzerland, CH-6928 Manno, Switzerland
| | - Byeongdu Lee
- X-ray Science Division, Advanced Photon Source, Argonne National
Laboratory, Lemont, IL 60439, USA
| | - Nadav Elad
- Department of Chemical Research Support, Weizmann Institute of
Science, Rehovot 76100, Israel
| | - Zonglin Chu
- Department of Organic Chemistry, Weizmann Institute of Science,
Rehovot 76100, Israel
| | - Giovanni M. Pavan
- Department of Innovative Technologies, University of Applied
Sciences and Arts of Southern Switzerland, CH-6928 Manno, Switzerland,Department of Applied Science and Technology, Politecnico di Torino,
10129 Torino, Italy
| | - Rafal Klajn
- Department of Organic Chemistry, Weizmann Institute of Science,
Rehovot 76100, Israel,
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14
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de Marco AL, Bochicchio D, Gardin A, Doni G, Pavan GM. Controlling Exchange Pathways in Dynamic Supramolecular Polymers by Controlling Defects. ACS NANO 2021; 15:14229-14241. [PMID: 34472834 PMCID: PMC8482751 DOI: 10.1021/acsnano.1c01398] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Accepted: 08/26/2021] [Indexed: 05/23/2023]
Abstract
Supramolecular fibers composed of monomers that self-assemble directionally via noncovalent interactions are ubiquitous in nature, and of great interest in chemistry. In these structures, the constitutive monomers continuously exchange in-and-out the assembly according to a well-defined supramolecular equilibrium. However, unraveling the exchange pathways and their molecular determinants constitutes a nontrivial challenge. Here, we combine coarse-grained modeling, enhanced sampling, and machine learning to investigate the key factors controlling the monomer exchange pathways in synthetic supramolecular polymers having an intrinsic dynamic behavior. We demonstrate how the competition of directional vs. nondirectional interactions between the monomers controls the creation/annihilation of defects in the supramolecular polymers, from where monomers exchange proceeds. This competition determines the exchange pathway, dictating whether a fiber statistically swaps monomers from the tips or from all along its length. Finally, thanks to their generality, our models allow the investigation of molecular approaches to control the exchange pathways in these dynamic assemblies.
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Affiliation(s)
- Anna L. de Marco
- Department
of Innovative Technologies, University of
Applied Sciences and Arts of Southern Switzerland, Polo Universitario Lugano, Campus
Est, Via la Santa 1, 6962 Lugano-Viganello, Switzerland
- Department
of Physics, Universit degli studi di Genova, Via Dodecaneso 33, 16100 Genova, Italy
| | - Davide Bochicchio
- Department
of Innovative Technologies, University of
Applied Sciences and Arts of Southern Switzerland, Polo Universitario Lugano, Campus
Est, Via la Santa 1, 6962 Lugano-Viganello, Switzerland
- Department
of Physics, Universit degli studi di Genova, Via Dodecaneso 33, 16100 Genova, Italy
| | - Andrea Gardin
- Department
of Applied Science and Technology, Politecnico
di Torino, Corso Duca degli Abruzzi 24, 10129 Torino, Italy
| | - Giovanni Doni
- Department
of Innovative Technologies, University of
Applied Sciences and Arts of Southern Switzerland, Polo Universitario Lugano, Campus
Est, Via la Santa 1, 6962 Lugano-Viganello, Switzerland
| | - Giovanni M. Pavan
- Department
of Innovative Technologies, University of
Applied Sciences and Arts of Southern Switzerland, Polo Universitario Lugano, Campus
Est, Via la Santa 1, 6962 Lugano-Viganello, Switzerland
- Department
of Applied Science and Technology, Politecnico
di Torino, Corso Duca degli Abruzzi 24, 10129 Torino, Italy
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15
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Musil F, Grisafi A, Bartók AP, Ortner C, Csányi G, Ceriotti M. Physics-Inspired Structural Representations for Molecules and Materials. Chem Rev 2021; 121:9759-9815. [PMID: 34310133 DOI: 10.1021/acs.chemrev.1c00021] [Citation(s) in RCA: 135] [Impact Index Per Article: 45.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
The first step in the construction of a regression model or a data-driven analysis, aiming to predict or elucidate the relationship between the atomic-scale structure of matter and its properties, involves transforming the Cartesian coordinates of the atoms into a suitable representation. The development of atomic-scale representations has played, and continues to play, a central role in the success of machine-learning methods for chemistry and materials science. This review summarizes the current understanding of the nature and characteristics of the most commonly used structural and chemical descriptions of atomistic structures, highlighting the deep underlying connections between different frameworks and the ideas that lead to computationally efficient and universally applicable models. It emphasizes the link between properties, structures, their physical chemistry, and their mathematical description, provides examples of recent applications to a diverse set of chemical and materials science problems, and outlines the open questions and the most promising research directions in the field.
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Affiliation(s)
- Felix Musil
- Laboratory of Computational Science and Modeling, IMX, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland.,National Centre for Computational Design and Discovery of Novel Materials (MARVEL), École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland
| | - Andrea Grisafi
- Laboratory of Computational Science and Modeling, IMX, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland
| | - Albert P Bartók
- Department of Physics and Warwick Centre for Predictive Modelling, School of Engineering, University of Warwick, Coventry CV4 7AL, United Kingdom
| | - Christoph Ortner
- University of British Columbia, Vancouver, British Columbia V6T 1Z2, Canada
| | - Gábor Csányi
- Engineering Laboratory, University of Cambridge, Trumpington Street, Cambridge CB2 1PZ, United Kingdom
| | - Michele Ceriotti
- Laboratory of Computational Science and Modeling, IMX, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland.,National Centre for Computational Design and Discovery of Novel Materials (MARVEL), École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland
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16
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Capelli R, Gardin A, Empereur-mot C, Doni G, Pavan GM. A Data-Driven Dimensionality Reduction Approach to Compare and Classify Lipid Force Fields. J Phys Chem B 2021; 125:7785-7796. [PMID: 34254518 PMCID: PMC8311647 DOI: 10.1021/acs.jpcb.1c02503] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Revised: 06/30/2021] [Indexed: 01/05/2023]
Abstract
Molecular dynamics simulations of all-atom and coarse-grained lipid bilayer models are increasingly used to obtain useful insights for understanding the structural dynamics of these assemblies. In this context, one crucial point concerns the comparison of the performance and accuracy of classical force fields (FFs), which sometimes remains elusive. To date, the assessments performed on different classical potentials are mostly based on the comparison with experimental observables, which typically regard average properties. However, local differences of the structure and dynamics, which are poorly captured by average measurements, can make a difference, but these are nontrivial to catch. Here, we propose an agnostic way to compare different FFs at different resolutions (atomistic, united-atom, and coarse-grained), by means of a high-dimensional similarity metrics built on the framework of Smooth Overlap of Atomic Position (SOAP). We compare and classify a set of 13 FFs, modeling 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine (POPC) bilayers. Our SOAP kernel-based metrics allows us to compare, discriminate, and correlate different FFs at different model resolutions in an unbiased, high-dimensional way. This also captures differences between FFs in modeling nonaverage events (originating from local transitions), for example, the liquid-to-gel phase transition in dipalmitoylphosphatidylcholine (DPPC) bilayers, for which our metrics allows us to identify nucleation centers for the phase transition, highlighting some intrinsic resolution limitations in implicit versus explicit solvent FFs.
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Affiliation(s)
- Riccardo Capelli
- Department
of Applied Science and Technology, Politecnico
di Torino, Corso Duca Degli Abruzzi 24, I-10129 Torino, Italy
| | - Andrea Gardin
- Department
of Applied Science and Technology, Politecnico
di Torino, Corso Duca Degli Abruzzi 24, I-10129 Torino, Italy
| | - Charly Empereur-mot
- Department
of Innovative Technologies, University of
Applied Sciences and Arts of Southern Switzerland, Polo Universitario
Lugano, Campus Est, Via
la Santa 1, CH-6962 Lugano-Viganello, Switzerland
| | - Giovanni Doni
- Department
of Innovative Technologies, University of
Applied Sciences and Arts of Southern Switzerland, Polo Universitario
Lugano, Campus Est, Via
la Santa 1, CH-6962 Lugano-Viganello, Switzerland
| | - Giovanni M. Pavan
- Department
of Applied Science and Technology, Politecnico
di Torino, Corso Duca Degli Abruzzi 24, I-10129 Torino, Italy
- Department
of Innovative Technologies, University of
Applied Sciences and Arts of Southern Switzerland, Polo Universitario
Lugano, Campus Est, Via
la Santa 1, CH-6962 Lugano-Viganello, Switzerland
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17
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Shyshov O, Haridas SV, Pesce L, Qi H, Gardin A, Bochicchio D, Kaiser U, Pavan GM, von Delius M. Living supramolecular polymerization of fluorinated cyclohexanes. Nat Commun 2021; 12:3134. [PMID: 34035277 PMCID: PMC8149861 DOI: 10.1038/s41467-021-23370-y] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2020] [Accepted: 04/23/2021] [Indexed: 02/07/2023] Open
Abstract
The development of powerful methods for living covalent polymerization has been a key driver of progress in organic materials science. While there have been remarkable reports on living supramolecular polymerization recently, the scope of monomers is still narrow and a simple solution to the problem is elusive. Here we report a minimalistic molecular platform for living supramolecular polymerization that is based on the unique structure of all-cis 1,2,3,4,5,6-hexafluorocyclohexane, the most polar aliphatic compound reported to date. We use this large dipole moment (6.2 Debye) not only to thermodynamically drive the self-assembly of supramolecular polymers, but also to generate kinetically trapped monomeric states. Upon addition of well-defined seeds, we observed that the dormant monomers engage in a kinetically controlled supramolecular polymerization. The obtained nanofibers have an unusual double helical structure and their length can be controlled by the ratio between seeds and monomers. The successful preparation of supramolecular block copolymers demonstrates the versatility of the approach.
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Affiliation(s)
| | | | - Luca Pesce
- Department of Innovative Technologies, University of Applied Sciences and Arts of Southern Switzerland, Lugano-Viganello, Switzerland
| | - Haoyuan Qi
- Central Facility of Electron Microscopy, Electron Microscopy Group of Materials Science, University of Ulm, Ulm, Germany
- Center for Advancing Electronics Dresden (cfaed) and Faculty of Chemistry and Food Chemistry, Technical University of Dresden, Dresden, Germany
| | - Andrea Gardin
- Department of Applied Science and Technology, Politecnico di Torino, Torino, Italy
| | - Davide Bochicchio
- Department of Innovative Technologies, University of Applied Sciences and Arts of Southern Switzerland, Lugano-Viganello, Switzerland
- Department of Physics, Università degli studi di Genova, Genova, Italy
| | - Ute Kaiser
- Central Facility of Electron Microscopy, Electron Microscopy Group of Materials Science, University of Ulm, Ulm, Germany
| | - Giovanni M Pavan
- Department of Innovative Technologies, University of Applied Sciences and Arts of Southern Switzerland, Lugano-Viganello, Switzerland.
- Department of Applied Science and Technology, Politecnico di Torino, Torino, Italy.
| | - Max von Delius
- Institute of Organic Chemistry, University of Ulm, Ulm, Germany.
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18
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Giberti F, Tribello GA, Ceriotti M. Global Free-Energy Landscapes as a Smoothly Joined Collection of Local Maps. J Chem Theory Comput 2021; 17:3292-3308. [PMID: 34003008 DOI: 10.1021/acs.jctc.0c01177] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Enhanced sampling techniques have become an essential tool in computational chemistry and physics, where they are applied to sample activated processes that occur on a time scale that is inaccessible to conventional simulations. Despite their popularity, it is well known that they have constraints that hinder their application to complex problems. The core issue lies in the need to describe the system using a small number of collective variables (CVs). Any slow degree of freedom that is not properly described by the chosen CVs will hinder sampling efficiency. However, the exploration of configuration space is also hampered by including variables that are not relevant for the activated process under study. This paper presents the Adaptive Topography of Landscape for Accelerated Sampling (ATLAS), a new biasing method capable of working with many CVs. The root idea of ATLAS is to apply a divide-and-conquer strategy, where the high-dimensional CVs space is divided into basins, each of which is described by an automatically determined, low-dimensional set of variables. A well-tempered metadynamics-like bias is constructed as a function of these local variables. Indicator functions associated with the basins switch on and off the local biases so that the sampling is performed on a collection of low-dimensional CV spaces that are smoothly combined to generate an effectively high-dimensional bias. The unbiased Boltzmann distribution is recovered through reweighing, making the evaluation of conformational and thermodynamic properties straightforward. The decomposition of the free-energy landscape in local basins can be updated iteratively as the simulation discovers new (meta)stable states.
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Affiliation(s)
- F Giberti
- Laboratory of Computational Science and Modeling, Institute of Materials, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland
| | - G A Tribello
- Atomistic Simulation Centre, School of Mathematics and Physics, Queen's University Belfast, Belfast BT14 7EN, United Kingdom
| | - M Ceriotti
- Laboratory of Computational Science and Modeling, Institute of Materials, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland
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19
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Musil F, Veit M, Goscinski A, Fraux G, Willatt MJ, Stricker M, Junge T, Ceriotti M. Efficient implementation of atom-density representations. J Chem Phys 2021; 154:114109. [DOI: 10.1063/5.0044689] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Affiliation(s)
- Félix Musil
- Laboratory of Computational Science and Modeling, Institute of Materials, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland
- National Center for Computational Design and Discovery of Novel Materials (MARVEL), Lausanne, Switzerland
| | - Max Veit
- Laboratory of Computational Science and Modeling, Institute of Materials, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland
- National Center for Computational Design and Discovery of Novel Materials (MARVEL), Lausanne, Switzerland
| | - Alexander Goscinski
- Laboratory of Computational Science and Modeling, Institute of Materials, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland
| | - Guillaume Fraux
- Laboratory of Computational Science and Modeling, Institute of Materials, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland
| | - Michael J. Willatt
- Laboratory of Computational Science and Modeling, Institute of Materials, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland
| | - Markus Stricker
- Laboratory for Multiscale Mechanics Modeling, Institute of Mechanical Engineering, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland
- Interdisciplinary Centre for Advanced Materials Simulation, Ruhr-University Bochum, Universitätsstraße 150, 44801 Bochum, Germany
| | - Till Junge
- Laboratory for Multiscale Mechanics Modeling, Institute of Mechanical Engineering, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland
| | - Michele Ceriotti
- Laboratory of Computational Science and Modeling, Institute of Materials, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland
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20
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Gasparotto P, Fischer M, Scopece D, Liedke MO, Butterling M, Wagner A, Yildirim O, Trant M, Passerone D, Hug HJ, Pignedoli CA. Mapping the Structure of Oxygen-Doped Wurtzite Aluminum Nitride Coatings from Ab Initio Random Structure Search and Experiments. ACS APPLIED MATERIALS & INTERFACES 2021; 13:5762-5771. [PMID: 33464807 DOI: 10.1021/acsami.0c19270] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/27/2023]
Abstract
Machine learning is changing how we design and interpret experiments in materials science. In this work, we show how unsupervised learning, combined with ab initio random structure searching, improves our understanding of structural metastability in multicomponent alloys. We focus on the case of Al-O-N alloys where the formation of aluminum vacancies in wurtzite AlN upon the incorporation of substitutional oxygen can be seen as a general mechanism of solids where crystal symmetry is reduced to stabilize defects. The ideal AlN wurtzite crystal structure occupation cannot be matched due to the presence of an aliovalent hetero-element into the structure. The traditional interpretation of the c-lattice shrinkage in sputter-deposited Al-O-N films from X-ray diffraction (XRD) experiments suggests the existence of a solubility limit at 8 at % oxygen content. Here, we show that such naive interpretation is misleading. We support XRD data with accurate ab initio modeling and dimensionality reduction on advanced structural descriptors to map structure-property relationships. No signs of a possible solubility limit are found. Instead, the presence of a wide range of non-equilibrium oxygen-rich defective structures emerging at increasing oxygen contents suggests that the formation of grain boundaries is the most plausible mechanism responsible for the lattice shrinkage measured in Al-O-N sputtered films. We further confirm our hypothesis using positron annihilation lifetime spectroscopy.
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Affiliation(s)
- Piero Gasparotto
- nanotech@surfaces Laboratory, Empa, Swiss Federal Laboratories for Materials Science and Technology Überlandstrasse 129, 8600 Dübendorf, Switzerland
| | - Maria Fischer
- Laboratory for Magnetic and Functional Thin Films, Empa, Swiss Federal Laboratories for Materials Science and Technology, Überlandstrasse 129, 8600 Dübendorf, Switzerland
| | - Daniele Scopece
- nanotech@surfaces Laboratory, Empa, Swiss Federal Laboratories for Materials Science and Technology Überlandstrasse 129, 8600 Dübendorf, Switzerland
| | - Maciej O Liedke
- Institute of Radiation Physics, Helmholtz-Zentrum Dresden-Rossendorf, Bautzner Landstrasse 400, 01328 Dresden, Germany
| | - Maik Butterling
- Institute of Radiation Physics, Helmholtz-Zentrum Dresden-Rossendorf, Bautzner Landstrasse 400, 01328 Dresden, Germany
| | - Andreas Wagner
- Institute of Radiation Physics, Helmholtz-Zentrum Dresden-Rossendorf, Bautzner Landstrasse 400, 01328 Dresden, Germany
| | - Oguz Yildirim
- Laboratory for Magnetic and Functional Thin Films, Empa, Swiss Federal Laboratories for Materials Science and Technology, Überlandstrasse 129, 8600 Dübendorf, Switzerland
| | - Mathis Trant
- Laboratory for Magnetic and Functional Thin Films, Empa, Swiss Federal Laboratories for Materials Science and Technology, Überlandstrasse 129, 8600 Dübendorf, Switzerland
| | - Daniele Passerone
- nanotech@surfaces Laboratory, Empa, Swiss Federal Laboratories for Materials Science and Technology Überlandstrasse 129, 8600 Dübendorf, Switzerland
| | - Hans J Hug
- Laboratory for Magnetic and Functional Thin Films, Empa, Swiss Federal Laboratories for Materials Science and Technology, Überlandstrasse 129, 8600 Dübendorf, Switzerland
- Department of Physics, University of Basel, Klingelbergstrasse 82, 4056 Basel, Switzerland
| | - Carlo A Pignedoli
- nanotech@surfaces Laboratory, Empa, Swiss Federal Laboratories for Materials Science and Technology Überlandstrasse 129, 8600 Dübendorf, Switzerland
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21
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Jablonka K, Ongari D, Moosavi SM, Smit B. Big-Data Science in Porous Materials: Materials Genomics and Machine Learning. Chem Rev 2020; 120:8066-8129. [PMID: 32520531 PMCID: PMC7453404 DOI: 10.1021/acs.chemrev.0c00004] [Citation(s) in RCA: 145] [Impact Index Per Article: 36.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2020] [Indexed: 12/16/2022]
Abstract
By combining metal nodes with organic linkers we can potentially synthesize millions of possible metal-organic frameworks (MOFs). The fact that we have so many materials opens many exciting avenues but also create new challenges. We simply have too many materials to be processed using conventional, brute force, methods. In this review, we show that having so many materials allows us to use big-data methods as a powerful technique to study these materials and to discover complex correlations. The first part of the review gives an introduction to the principles of big-data science. We show how to select appropriate training sets, survey approaches that are used to represent these materials in feature space, and review different learning architectures, as well as evaluation and interpretation strategies. In the second part, we review how the different approaches of machine learning have been applied to porous materials. In particular, we discuss applications in the field of gas storage and separation, the stability of these materials, their electronic properties, and their synthesis. Given the increasing interest of the scientific community in machine learning, we expect this list to rapidly expand in the coming years.
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Affiliation(s)
- Kevin
Maik Jablonka
- Laboratory of Molecular Simulation
(LSMO), Institut des Sciences et Ingénierie Chimiques (ISIC), École Polytechnique Fédérale
de Lausanne (EPFL), Sion, Switzerland
| | - Daniele Ongari
- Laboratory of Molecular Simulation
(LSMO), Institut des Sciences et Ingénierie Chimiques (ISIC), École Polytechnique Fédérale
de Lausanne (EPFL), Sion, Switzerland
| | - Seyed Mohamad Moosavi
- Laboratory of Molecular Simulation
(LSMO), Institut des Sciences et Ingénierie Chimiques (ISIC), École Polytechnique Fédérale
de Lausanne (EPFL), Sion, Switzerland
| | - Berend Smit
- Laboratory of Molecular Simulation
(LSMO), Institut des Sciences et Ingénierie Chimiques (ISIC), École Polytechnique Fédérale
de Lausanne (EPFL), Sion, Switzerland
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22
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Rowe P, Deringer VL, Gasparotto P, Csányi G, Michaelides A. An accurate and transferable machine learning potential for carbon. J Chem Phys 2020; 153:034702. [DOI: 10.1063/5.0005084] [Citation(s) in RCA: 71] [Impact Index Per Article: 17.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Affiliation(s)
- Patrick Rowe
- Thomas Young Centre, London Centre for Nanotechnology, and Department of Physics and Astronomy, University College London, Gower Street, London, WC1E 6BT, United Kingdom
| | - Volker L. Deringer
- Department of Chemistry, Inorganic Chemistry Laboratory, University of Oxford, Oxford OX1 3QR, United Kingdom
| | - Piero Gasparotto
- Thomas Young Centre, London Centre for Nanotechnology, and Department of Physics and Astronomy, University College London, Gower Street, London, WC1E 6BT, United Kingdom
| | - Gábor Csányi
- Engineering Laboratory, University of Cambridge, Trumpington Street, Cambridge CB2 1PZ, United Kingdom
| | - Angelos Michaelides
- Thomas Young Centre, London Centre for Nanotechnology, and Department of Physics and Astronomy, University College London, Gower Street, London, WC1E 6BT, United Kingdom
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23
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Smith J, Pfaendtner J. Elucidating the Molecular Interactions between Uremic Toxins and the Sudlow II Binding Site of Human Serum Albumin. J Phys Chem B 2020; 124:3922-3930. [DOI: 10.1021/acs.jpcb.0c02015] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Affiliation(s)
- Josh Smith
- Department of Chemical Engineering, University of Washington, Seattle, Washington 98195-1750, United States
| | - Jim Pfaendtner
- Department of Chemical Engineering, University of Washington, Seattle, Washington 98195-1750, United States
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24
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Røe I, Selbach SM, Schnell SK. Crystal Structure Influences Migration along Li and Mg Surfaces. J Phys Chem Lett 2020; 11:2891-2895. [PMID: 32208701 PMCID: PMC7311081 DOI: 10.1021/acs.jpclett.0c00819] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2020] [Accepted: 03/25/2020] [Indexed: 05/14/2023]
Abstract
Dendrite formation on Li metal anodes hinders commercialization of more energy-dense rechargeable batteries. Here, we use the migration energy barrier (MEB) for surface transport as a descriptor for dendrite nucleation and compare Li to Mg. Density functional theory calculations show that the MEB for the hexagonal close-packed structure is 40 and 270 meV lower than that of the body-centered cubic structure for Li and Mg, respectively. This is suggested as a reason why Mg surfaces are less prone to form dendrites than Li. We show that the close-packed facets exhibit lower MEBs because of smaller changes in atomic coordination during migration and thereby less surface distortion.
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Affiliation(s)
- Ingeborg
Treu Røe
- Department of Materials Science and
Engineering, Norwegian University of Science
and Technology, NTNU, NO-7491 Trondheim, Norway
| | - Sverre M. Selbach
- Department of Materials Science and
Engineering, Norwegian University of Science
and Technology, NTNU, NO-7491 Trondheim, Norway
| | - Sondre Kvalvåg Schnell
- Department of Materials Science and
Engineering, Norwegian University of Science
and Technology, NTNU, NO-7491 Trondheim, Norway
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25
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Gasparotto P, Bochicchio D, Ceriotti M, Pavan GM. Identifying and Tracking Defects in Dynamic Supramolecular Polymers. J Phys Chem B 2020; 124:589-599. [PMID: 31888337 DOI: 10.1021/acs.jpcb.9b11015] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
A central paradigm of self-assembly is to create ordered structures starting from molecular monomers that spontaneously recognize and interact with each other via noncovalent interactions. In recent years, great efforts have been directed toward perfecting the design of a variety of supramolecular polymers and materials with different architectures. The resulting structures are often thought of as ideally perfect, defect-free supramolecular fibers, micelles, vesicles, etc., having an intrinsic dynamic character, which are typically studied at the level of statistical ensembles to assess their average properties. However, molecular simulations recently demonstrated that local defects that may be present or may form in these assemblies, and which are poorly captured by conventional approaches, are key to controlling their dynamic behavior and properties. The study of these defects poses considerable challenges, as the flexible/dynamic nature of these soft systems makes it difficult to identify what effectively constitutes a defect and to characterize its stability and evolution. Here, we demonstrate the power of unsupervised machine-learning techniques to systematically identify and compare defects in supramolecular polymer variants in different conditions, using as a benchmark 5 Å resolution coarse-grained molecular simulations of a family of supramolecular polymers. We show that this approach allows a complete data-driven characterization of the internal structure and dynamics of these complex assemblies and of the dynamic pathways for defects formation and resorption. This provides a useful, generally applicable approach to unambiguously identify defects in these dynamic self-assembled materials and to classify them based on their structure, stability, and dynamics.
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Affiliation(s)
- Piero Gasparotto
- Laboratory of Computational Science and Modeling, Institute des Materiaux , Ecole polytechnique fédérale de Lausanne , CH-1015 Lausanne , Switzerland.,Thomas Young Centre and Department of Physics and Astronomy , University College London , Gower Street , London WC1E 6BT , United Kingdom
| | - Davide Bochicchio
- Department of Innovative Technologies , University of Applied Sciences and Arts of Southern Switzerland , Galleria 2, Via Cantonale 2c , CH-6928 Manno , Switzerland
| | - Michele Ceriotti
- Laboratory of Computational Science and Modeling, Institute des Materiaux , Ecole polytechnique fédérale de Lausanne , CH-1015 Lausanne , Switzerland
| | - Giovanni M Pavan
- Department of Innovative Technologies , University of Applied Sciences and Arts of Southern Switzerland , Galleria 2, Via Cantonale 2c , CH-6928 Manno , Switzerland.,Department of Applied Science and Technology , Politecnico di Torino , Corso Duca degli Abruzzi 24 , 10129 Torino , Italy
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26
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Swanson K, Trivedi S, Lequieu J, Swanson K, Kondor R. Deep learning for automated classification and characterization of amorphous materials. SOFT MATTER 2020; 16:435-446. [PMID: 31803878 DOI: 10.1039/c9sm01903k] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/27/2023]
Abstract
It is difficult to quantify structure-property relationships and to identify structural features of complex materials. The characterization of amorphous materials is especially challenging because their lack of long-range order makes it difficult to define structural metrics. In this work, we apply deep learning algorithms to accurately classify amorphous materials and characterize their structural features. Specifically, we show that convolutional neural networks and message passing neural networks can classify two-dimensional liquids and liquid-cooled glasses from molecular dynamics simulations with greater than 0.98 AUC, with no a priori assumptions about local particle relationships, even when the liquids and glasses are prepared at the same inherent structure energy. Furthermore, we demonstrate that message passing neural networks surpass convolutional neural networks in this context in both accuracy and interpretability. We extract a clear interpretation of how message passing neural networks evaluate liquid and glass structures by using a self-attention mechanism. Using this interpretation, we derive three novel structural metrics that accurately characterize glass formation. The methods presented here provide a procedure to identify important structural features in materials that could be missed by standard techniques and give unique insight into how these neural networks process data.
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Affiliation(s)
- Kirk Swanson
- Department of Computer Science, The University of Chicago, Chicago, IL 60637, USA.
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27
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Zhang J, Lei YK, Che X, Zhang Z, Yang YI, Gao YQ. Deep Representation Learning for Complex Free-Energy Landscapes. J Phys Chem Lett 2019; 10:5571-5576. [PMID: 31476868 DOI: 10.1021/acs.jpclett.9b02012] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
In this Letter, we analyzed the inductive bias underlying complex free-energy landscapes (FELs) and exploited it to train deep neural networks that yield reduced and clustered representation for the FEL. Our parametric method, called information distilling of metastability (IDM), is end-to-end differentiable and thus scalable to ultralarge data sets. IDM is able to perform clustering in the meantime of reducing the dimensionality. Besides, as an unsupervised learning method, IDM differs from many existing dimensionality reduction and clustering methods in that it requires neither a cherry-picked distance metric nor the ground-true number of clusters defined a priori, and it can be used to unroll and zoom in on the hierarchical FEL with respect to different time scales. Through multiple experiments, we show that IDM can achieve physically meaningful representations that partition the FEL into well-defined metastable states that hence are amenable for downstream tasks such as mechanism analysis and kinetic modeling.
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Affiliation(s)
- Jun Zhang
- Institute of Theoretical and Computational Chemistry, College of Chemistry and Molecular Engineering , Peking University , 100871 Beijing , China
- Biodynamic Optical Imaging Center , Peking University , 100871 Beijing , China
| | - Yao-Kun Lei
- Institute of Theoretical and Computational Chemistry, College of Chemistry and Molecular Engineering , Peking University , 100871 Beijing , China
| | - Xing Che
- Institute of Molecular Biophysics , Florida State University , Tallahassee , Florida 32306 , United States
| | - Zhen Zhang
- Institute of Theoretical and Computational Chemistry, College of Chemistry and Molecular Engineering , Peking University , 100871 Beijing , China
- Department of Physics , Tangshan Normal University , 063000 Tangshan , China
| | - Yi Isaac Yang
- Institute of Systems Biology , Shenzhen Bay Laboratory , 518055 Shenzhen , China
| | - Yi Qin Gao
- Institute of Theoretical and Computational Chemistry, College of Chemistry and Molecular Engineering , Peking University , 100871 Beijing , China
- Biodynamic Optical Imaging Center , Peking University , 100871 Beijing , China
- Institute of Systems Biology , Shenzhen Bay Laboratory , 518055 Shenzhen , China
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28
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Tribello GA, Gasparotto P. Using Dimensionality Reduction to Analyze Protein Trajectories. Front Mol Biosci 2019; 6:46. [PMID: 31275943 PMCID: PMC6593086 DOI: 10.3389/fmolb.2019.00046] [Citation(s) in RCA: 39] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2019] [Accepted: 05/31/2019] [Indexed: 11/24/2022] Open
Abstract
In recent years the analysis of molecular dynamics trajectories using dimensionality reduction algorithms has become commonplace. These algorithms seek to find a low-dimensional representation of a trajectory that is, according to a well-defined criterion, optimal. A number of different strategies for generating projections of trajectories have been proposed but little has been done to systematically compare how these various approaches fare when it comes to analysing trajectories for biomolecules in explicit solvent. In the following paper, we have thus analyzed a molecular dynamics trajectory of the C-terminal fragment of the immunoglobulin binding domain B1 of protein G of Streptococcus modeled in explicit solvent using a range of different dimensionality reduction algorithms. We have then tried to systematically compare the projections generated using each of these algorithms by using a clustering algorithm to find the positions and extents of the basins in the high-dimensional energy landscape. We find that no algorithm outshines all the other in terms of the quality of the projection it generates. Instead, all the algorithms do a reasonable job when it comes to building a projection that separates some of the configurations that lie in different basins. Having said that, however, all the algorithms struggle to project the basins because they all have a large intrinsic dimensionality.
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Affiliation(s)
- Gareth A Tribello
- Atomistic Simulation Centre, School of Mathematics and Physics, Queen's University Belfast, Belfast, United Kingdom
| | - Piero Gasparotto
- Department of Physics and Astronomy, Thomas Young Centre, University College London, London, United Kingdom
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29
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Ceriotti M. Unsupervised machine learning in atomistic simulations, between predictions and understanding. J Chem Phys 2019; 150:150901. [PMID: 31005087 DOI: 10.1063/1.5091842] [Citation(s) in RCA: 75] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
Automated analyses of the outcome of a simulation have been an important part of atomistic modeling since the early days, addressing the need of linking the behavior of individual atoms and the collective properties that are usually the final quantity of interest. Methods such as clustering and dimensionality reduction have been used to provide a simplified, coarse-grained representation of the structure and dynamics of complex systems from proteins to nanoparticles. In recent years, the rise of machine learning has led to an even more widespread use of these algorithms in atomistic modeling and to consider different classification and inference techniques as part of a coherent toolbox of data-driven approaches. This perspective briefly reviews some of the unsupervised machine-learning methods-that are geared toward classification and coarse-graining of molecular simulations-seen in relation to the fundamental mathematical concepts that underlie all machine-learning techniques. It discusses the importance of using concise yet complete representations of atomic structures as the starting point of the analyses and highlights the risk of introducing preconceived biases when using machine learning to rationalize and understand structure-property relations. Supervised machine-learning techniques that explicitly attempt to predict the properties of a material given its structure are less susceptible to such biases. Current developments in the field suggest that using these two classes of approaches side-by-side and in a fully integrated mode, while keeping in mind the relations between the data analysis framework and the fundamental physical principles, will be key to realizing the full potential of machine learning to help understand the behavior of complex molecules and materials.
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Affiliation(s)
- Michele Ceriotti
- Laboratory of Computational Science and Modeling, Institute des Materiaux, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland
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30
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Helfrecht BA, Gasparotto P, Giberti F, Ceriotti M. Atomic Motif Recognition in (Bio)Polymers: Benchmarks From the Protein Data Bank. Front Mol Biosci 2019; 6:24. [PMID: 31058166 PMCID: PMC6482324 DOI: 10.3389/fmolb.2019.00024] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2018] [Accepted: 03/26/2019] [Indexed: 11/13/2022] Open
Abstract
Rationalizing the structure and structure-property relations for complex materials such as polymers or biomolecules relies heavily on the identification of local atomic motifs, e.g., hydrogen bonds and secondary structure patterns, that are seen as building blocks of more complex supramolecular and mesoscopic structures. Over the past few decades, several automated procedures have been developed to identify these motifs in proteins given the atomic structure. Being based on a very precise understanding of the specific interactions, these heuristic criteria formulate the question in a way that implies the answer, by defining a list of motifs based on those that are known to be naturally occurring. This makes them less likely to identify unexpected phenomena, such as the occurrence of recurrent motifs in disordered segments of proteins, and less suitable to be applied to different polymers whose structure is not driven by hydrogen bonds, or even to polypeptides when appearing in unusual, non-biological conditions. Here we discuss how unsupervised machine learning schemes can be used to recognize patterns based exclusively on the frequency with which different motifs occur, taking high-resolution structures from the Protein Data Bank as benchmarks. We first discuss the application of a density-based motif recognition scheme in combination with traditional representations of protein structure (namely, interatomic distances and backbone dihedrals). Then, we proceed one step further toward an entirely unbiased scheme by using as input a structural representation based on the atomic density and by employing supervised classification to objectively assess the role played by the representation in determining the nature of atomic-scale patterns.
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Affiliation(s)
- Benjamin A Helfrecht
- Laboratory of Computational Science and Modeling, Institute of Materials, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Piero Gasparotto
- Laboratory of Computational Science and Modeling, Institute of Materials, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Federico Giberti
- Laboratory of Computational Science and Modeling, Institute of Materials, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Michele Ceriotti
- Laboratory of Computational Science and Modeling, Institute of Materials, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
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31
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Abstract
This chapter discusses the way in which dimensionality reduction algorithms such as diffusion maps and sketch-map can be used to analyze molecular dynamics trajectories. The first part discusses how these various algorithms function as well as practical issues such as landmark selection and how these algorithms can be used when the data to be analyzed comes from enhanced sampling trajectories. In the later part a comparison between the results obtained by applying various algorithms to two sets of sample data is performed and discussed. This section is then followed by a summary of how one algorithm in particular, sketch-map, has been applied to a range of problems. The chapter concludes with a discussion on the directions that we believe this field is currently moving.
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32
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Imbalzano G, Anelli A, Giofré D, Klees S, Behler J, Ceriotti M. Automatic selection of atomic fingerprints and reference configurations for machine-learning potentials. J Chem Phys 2018; 148:241730. [DOI: 10.1063/1.5024611] [Citation(s) in RCA: 163] [Impact Index Per Article: 27.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023] Open
Affiliation(s)
- Giulio Imbalzano
- Laboratory of Computational Science and Modeling, IMX, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland
| | - Andrea Anelli
- Laboratory of Computational Science and Modeling, IMX, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland
| | - Daniele Giofré
- Laboratory of Computational Science and Modeling, IMX, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland
| | - Sinja Klees
- Lehrstuhl für Theoretische Chemie, Ruhr-Universität Bochum, 44801 Bochum, Germany
| | - Jörg Behler
- Lehrstuhl für Theoretische Chemie, Ruhr-Universität Bochum, 44801 Bochum, Germany
- Universität Göttingen, Institut für Physikalische Chemie, Theoretische Chemie, Tammannstr. 6, 37077 Göttingen, Germany
| | - Michele Ceriotti
- Laboratory of Computational Science and Modeling, IMX, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland
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