Universal and Accessible Entropy Estimation Using a Compression Algorithm
Entropy and free-energy estimation are key in thermodynamic characterization of simulated systems ranging from spin models through polymers, colloids, protein structure, and drug design. Current techniques suffer from being model specific, requiring abundant computation resources and simulation at c...
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| Veröffentlicht in: | Physical review letters Jg. 123; H. 17; S. 1 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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25.10.2019
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| ISSN: | 0031-9007, 1079-7114, 1079-7114 |
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| Abstract | Entropy and free-energy estimation are key in thermodynamic characterization of simulated systems ranging from spin models through polymers, colloids, protein structure, and drug design. Current techniques suffer from being model specific, requiring abundant computation resources and simulation at conditions far from the studied realization. Here, we present a universal scheme to calculate entropy using lossless-compression algorithms and validate it on simulated systems of increasing complexity. Our results show accurate entropy values compared to benchmark calculations while being computationally effective. In molecular-dynamics simulations of protein folding, we exhibit unmatched detection capability of the folded states by measuring previously undetectable entropy fluctuations along the simulation timeline. Such entropy evaluation opens a new window onto the dynamics of complex systems and allows efficient free-energy calculations. |
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| AbstractList | Entropy and free-energy estimation are key in thermodynamic characterization of simulated systems ranging from spin models through polymers, colloids, protein structure, and drug design. Current techniques suffer from being model specific, requiring abundant computation resources and simulation at conditions far from the studied realization. Here, we present a universal scheme to calculate entropy using lossless-compression algorithms and validate it on simulated systems of increasing complexity. Our results show accurate entropy values compared to benchmark calculations while being computationally effective. In molecular-dynamics simulations of protein folding, we exhibit unmatched detection capability of the folded states by measuring previously undetectable entropy fluctuations along the simulation timeline. Such entropy evaluation opens a new window onto the dynamics of complex systems and allows efficient free-energy calculations.Entropy and free-energy estimation are key in thermodynamic characterization of simulated systems ranging from spin models through polymers, colloids, protein structure, and drug design. Current techniques suffer from being model specific, requiring abundant computation resources and simulation at conditions far from the studied realization. Here, we present a universal scheme to calculate entropy using lossless-compression algorithms and validate it on simulated systems of increasing complexity. Our results show accurate entropy values compared to benchmark calculations while being computationally effective. In molecular-dynamics simulations of protein folding, we exhibit unmatched detection capability of the folded states by measuring previously undetectable entropy fluctuations along the simulation timeline. Such entropy evaluation opens a new window onto the dynamics of complex systems and allows efficient free-energy calculations. Entropy and free-energy estimation are key in thermodynamic characterization of simulated systems ranging from spin models through polymers, colloids, protein structure, and drug design. Current techniques suffer from being model specific, requiring abundant computation resources and simulation at conditions far from the studied realization. Here, we present a universal scheme to calculate entropy using lossless-compression algorithms and validate it on simulated systems of increasing complexity. Our results show accurate entropy values compared to benchmark calculations while being computationally effective. In molecular-dynamics simulations of protein folding, we exhibit unmatched detection capability of the folded states by measuring previously undetectable entropy fluctuations along the simulation timeline. Such entropy evaluation opens a new window onto the dynamics of complex systems and allows efficient free-energy calculations. |
| ArticleNumber | 178102 |
| Author | Kornreich, Micha Avinery, Ram Beck, Roy |
| Author_xml | – sequence: 1 givenname: Ram orcidid: 0000-0002-9580-4989 surname: Avinery fullname: Avinery, Ram – sequence: 2 givenname: Micha surname: Kornreich fullname: Kornreich, Micha – sequence: 3 givenname: Roy surname: Beck fullname: Beck, Roy |
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| Cites_doi | 10.1002/j.1538-7305.1948.tb00917.x 10.1103/PhysRev.65.117 10.1090/S0002-9947-1970-0259068-3 10.1103/PhysRev.79.357 10.1146/annurev-biophys-042910-155245 10.1002/prot.22689 10.1016/j.sbi.2013.12.006 10.1080/00207166808803030 10.1109/TBME.2006.883696 10.1103/PhysRevE.79.046208 10.1111/jep.12068 10.1103/PhysRevLett.88.048702 10.1103/PhysRevE.91.023306 10.1016/j.physa.2011.09.005 10.1007/BF01044436 10.1103/PhysRevLett.62.361 10.1017/CBO9780511976155 10.1109/69.908985 10.1109/TIT.1977.1055714 10.1109/TIT.1978.1055934 10.1017/CBO9781139696463 10.1162/089976604322860677 10.1103/PhysRevX.9.011031 10.1016/j.jmb.2006.03.034 10.1016/j.fluid.2004.09.017 10.1021/jp0761665 10.1021/ct500161f 10.1103/PhysRevE.96.062133 10.1109/18.108250 10.1073/pnas.0502495102 10.1073/pnas.1201811109 |
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| References | PhysRevLett.123.178102Cc32R1 PhysRevLett.123.178102Cc10R1 PhysRevLett.123.178102Cc31R1 PhysRevLett.123.178102Cc26R1 PhysRevLett.123.178102Cc29R1 PhysRevLett.123.178102Cc28R1 PhysRevLett.123.178102Cc23R1 PhysRevLett.123.178102Cc25R1 PhysRevLett.123.178102Cc24R1 PhysRevLett.123.178102Cc1R1 D. P. Landau (PhysRevLett.123.178102Cc5R1) 2014 PhysRevLett.123.178102Cc21R1 PhysRevLett.123.178102Cc20R1 PhysRevLett.123.178102Cc15R1 PhysRevLett.123.178102Cc16R1 T. M. Cover (PhysRevLett.123.178102Cc27R1) 2006 PhysRevLett.123.178102Cc17R1 PhysRevLett.123.178102Cc18R1 D. Frenkel (PhysRevLett.123.178102Cc6R1) 2001 PhysRevLett.123.178102Cc11R1 PhysRevLett.123.178102Cc34R1 PhysRevLett.123.178102Cc33R1 PhysRevLett.123.178102Cc13R1 PhysRevLett.123.178102Cc14R1 PhysRevLett.123.178102Cc35R1 PhysRevLett.123.178102Cc7R1 T. Downarowicz (PhysRevLett.123.178102Cc12R1) 2011 PhysRevLett.123.178102Cc8R1 PhysRevLett.123.178102Cc9R1 PhysRevLett.123.178102Cc2R1 PhysRevLett.123.178102Cc19R1 PhysRevLett.123.178102Cc3R1 PhysRevLett.123.178102Cc4R1 I. M. Pu (PhysRevLett.123.178102Cc30R1) 2005 |
| References_xml | – ident: PhysRevLett.123.178102Cc10R1 doi: 10.1002/j.1538-7305.1948.tb00917.x – ident: PhysRevLett.123.178102Cc25R1 doi: 10.1103/PhysRev.65.117 – ident: PhysRevLett.123.178102Cc13R1 doi: 10.1090/S0002-9947-1970-0259068-3 – ident: PhysRevLett.123.178102Cc26R1 doi: 10.1103/PhysRev.79.357 – ident: PhysRevLett.123.178102Cc1R1 doi: 10.1146/annurev-biophys-042910-155245 – ident: PhysRevLett.123.178102Cc34R1 doi: 10.1002/prot.22689 – ident: PhysRevLett.123.178102Cc3R1 doi: 10.1016/j.sbi.2013.12.006 – ident: PhysRevLett.123.178102Cc11R1 doi: 10.1080/00207166808803030 – ident: PhysRevLett.123.178102Cc15R1 doi: 10.1109/TBME.2006.883696 – ident: PhysRevLett.123.178102Cc31R1 doi: 10.1103/PhysRevE.79.046208 – ident: PhysRevLett.123.178102Cc14R1 doi: 10.1111/jep.12068 – volume-title: Elements of Information Theory year: 2006 ident: PhysRevLett.123.178102Cc27R1 – ident: PhysRevLett.123.178102Cc16R1 doi: 10.1103/PhysRevLett.88.048702 – volume-title: Fundamental Data Compression year: 2005 ident: PhysRevLett.123.178102Cc30R1 – ident: PhysRevLett.123.178102Cc18R1 doi: 10.1103/PhysRevE.91.023306 – ident: PhysRevLett.123.178102Cc19R1 doi: 10.1016/j.physa.2011.09.005 – ident: PhysRevLett.123.178102Cc24R1 doi: 10.1007/BF01044436 – ident: PhysRevLett.123.178102Cc23R1 doi: 10.1103/PhysRevLett.62.361 – volume-title: Entropy in Dynamical Systems year: 2011 ident: PhysRevLett.123.178102Cc12R1 doi: 10.1017/CBO9780511976155 – ident: PhysRevLett.123.178102Cc32R1 doi: 10.1109/69.908985 – ident: PhysRevLett.123.178102Cc28R1 doi: 10.1109/TIT.1977.1055714 – ident: PhysRevLett.123.178102Cc29R1 doi: 10.1109/TIT.1978.1055934 – volume-title: A Guide to Monte Carlo Simulations in Statistical Physics year: 2014 ident: PhysRevLett.123.178102Cc5R1 doi: 10.1017/CBO9781139696463 – volume-title: Understanding Molecular Simulation: From Algorithms to Applications year: 2001 ident: PhysRevLett.123.178102Cc6R1 – ident: PhysRevLett.123.178102Cc17R1 doi: 10.1162/089976604322860677 – ident: PhysRevLett.123.178102Cc21R1 doi: 10.1103/PhysRevX.9.011031 – ident: PhysRevLett.123.178102Cc7R1 doi: 10.1016/j.jmb.2006.03.034 – ident: PhysRevLett.123.178102Cc4R1 doi: 10.1016/j.fluid.2004.09.017 – ident: PhysRevLett.123.178102Cc9R1 doi: 10.1021/jp0761665 – ident: PhysRevLett.123.178102Cc8R1 doi: 10.1021/ct500161f – ident: PhysRevLett.123.178102Cc20R1 doi: 10.1103/PhysRevE.96.062133 – ident: PhysRevLett.123.178102Cc33R1 doi: 10.1109/18.108250 – ident: PhysRevLett.123.178102Cc35R1 doi: 10.1073/pnas.0502495102 – ident: PhysRevLett.123.178102Cc2R1 doi: 10.1073/pnas.1201811109 |
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| Title | Universal and Accessible Entropy Estimation Using a Compression Algorithm |
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