Current Advances in Genome Modeling Across Length Scales DOI Creative Commons
E Schultz, Joseph Kaplan, Yiheng Wu

и другие.

Wiley Interdisciplinary Reviews Computational Molecular Science, Год журнала: 2025, Номер 15(3)

Опубликована: Май 1, 2025

ABSTRACT The physical organization of DNA within the nucleus is fundamental to a wide range biological processes. experimental investigation structure genomic remains challenging due its large size and hierarchical arrangement. These challenges present considerable opportunities for combined modeling approaches. Physics‐based computational models, in particular, have emerged as essential tools probing chromatin dynamics across length scales. Such models must necessarily be capable bridging scales, each scale presents own subtleties intricacies. This review discusses recent methodological advances structural modeling, emphasizing need multiscale integration capture molecular mechanisms that underlie function. We an analysis state‐of‐the‐art methods, well perspective on future scales ranging from bare nucleosomes fibers, up TAD chromosome‐scale models. emphasize connect genome gene expression, leverage emerging machine learning capabilities, develop examine gaps data are poised address propose directions research bridge theory experiment biology.

Язык: Английский

Stable molecular dynamics simulations of halide perovskites from a temperature-ensemble gradient-domain machine learning approach DOI Creative Commons

Oz Y. Mendelsohn,

Michal Hartstein,

Stefan Chmiela

и другие.

Chemical Physics Letters, Год журнала: 2025, Номер unknown, С. 141964 - 141964

Опубликована: Фев. 1, 2025

Язык: Английский

Процитировано

0
Static Subspace Approximation for Random Phase Approximation Correlation Energies: Applications to Materials for Catalysis and Electrochemistry DOI
Jacob M. Clary, Olivia Hull, Daniel Weinberg

и другие.

Journal of Chemical Theory and Computation, Год журнала: 2025, Номер unknown

Опубликована: Апрель 14, 2025

Modeling complex materials using high-fidelity, ab initio methods at low cost is a fundamental goal for quantum chemical software packages. The GW approximation and random phase (RPA) provide unified description of both electronic structure total energies the same physics in many-body perturbative approach that can be more accurate than generalized-gradient density functional theory (DFT) methods. However, GW/RPA implementations have historically been limited to either specific classes or application toward small systems. static subspace allows reduced full-frequency calculations has previously benchmarked thoroughly calculations. Here, we describe our including partial occupations orbitals RPA study electrocatalysts. We energy across diverse test suite variety computational parameters. benchmarking quantifies impact different extrapolation procedures representing polarizability infinite screened cutoff, shows cutoffs above 20-25 Ryd result diminishing accuracy returns predicting energies. Additionally, moderately sized electrocatalytic models, 2-3 times fewer resources are used compute by with 20-30% basis, an error approximately 0.01 eV better adsorption Finally, show these electrochemical models shift DFT shifts up 0.5 frequently eigenvalues surface adsorbate states 0.5-1 eV.

Язык: Английский

Процитировано

0
Current Advances in Genome Modeling Across Length Scales DOI Creative Commons
E Schultz, Joseph Kaplan, Yiheng Wu

и другие.

Wiley Interdisciplinary Reviews Computational Molecular Science, Год журнала: 2025, Номер 15(3)

Опубликована: Май 1, 2025

ABSTRACT The physical organization of DNA within the nucleus is fundamental to a wide range biological processes. experimental investigation structure genomic remains challenging due its large size and hierarchical arrangement. These challenges present considerable opportunities for combined modeling approaches. Physics‐based computational models, in particular, have emerged as essential tools probing chromatin dynamics across length scales. Such models must necessarily be capable bridging scales, each scale presents own subtleties intricacies. This review discusses recent methodological advances structural modeling, emphasizing need multiscale integration capture molecular mechanisms that underlie function. We an analysis state‐of‐the‐art methods, well perspective on future scales ranging from bare nucleosomes fibers, up TAD chromosome‐scale models. emphasize connect genome gene expression, leverage emerging machine learning capabilities, develop examine gaps data are poised address propose directions research bridge theory experiment biology.

Язык: Английский

Процитировано

0