Raman Spectra of Amino Acids and Peptides from Machine Learning Polarizabilities

Journal of Chemical Information and Modeling, Journal Year: 2024, Volume and Issue: 64(12), P. 4601 - 4612

Published: June 3, 2024

Raman spectroscopy is an important tool in the study of vibrational properties and composition molecules, peptides, even proteins. spectra can be simulated based on change electronic polarizability with vibrations, which nowadays efficiently obtained via machine learning models trained first-principles data. However, transferability small molecules to larger structures unclear, direct training large prohibitively expensive. In this work, we first train two predict polarizabilities all 20 amino acids. Both are carefully benchmarked compared density functional theory (DFT) calculations, neural network method being found offer better transferability. By combination classical force field molecular dynamics, acids also investigated, showing good agreement experiments. The further extended peptides. We find that adding containing peptide bonds set greatly improves predictions, for peptides not included sets.

Language: Английский

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Generalized bond polarizability model for more accurate atomistic modeling of Raman spectra

The Journal of Chemical Physics, Journal Year: 2025, Volume and Issue: 162(5)

Published: Feb. 4, 2025

Raman spectroscopy is an important tool for studying molecules, liquids and solids. While spectra can be obtained theoretically from molecular dynamics (MD) simulations, this requires the calculation of electronic polarizability along simulation trajectory. First-principles calculations are computationally expensive, motivating development atomistic models evaluation changes in with atomic coordinates system. The bond model (BPM) one oldest simplest such but cannot reproduce effects angular vibrations, leading to inaccurate modeling spectra. Here, we demonstrate that generalization BPM through inclusion terms atom pairs traditionally considered not involved bonding dramatically improves accuracy calculations. generalized (GBPM) reproduces ab initio a range tested molecules (SO2, H2S, H2O, NH3, CH4, CH3OH, CH3CH2OH) high also shows significantly improved agreement results more complex ferroelectric BaTiO3 systems. For liquid water, anisotropic spectrum derived MD simulations using GBPM experimental compared BPM. Thus, used large-scale provides good basis further models.

Language: Английский

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Untangling the Raman spectra of cubic and tetragonal BaZrO3

Physical review. B./Physical review. B, Journal Year: 2025, Volume and Issue: 111(6)

Published: Feb. 18, 2025

Raman spectroscopy is a widely used experimental technique to study the vibrational properties of solids. Atomic scale simulations can be predict such spectra, but reliable studies at finite temperatures are challenging, mainly due requirement accurate and computationally efficient models for dielectric susceptibility. Here, we have molecular dynamics together with density functional theory-based model susceptibility determine spectrum barium zirconate, BaZrO3 (BZO), well-studied oxide perovskite. At ambient conditions, where system cubic, find excellent agreement experimentally measured spectra. Our establishes that relatively sharp spectra seen second-order scattering. higher pressures, BZO tetragonal, all first-order active modes identified. Additionally, slightly below phase transition, in cubic phase, broad central peak appears. The origin this type controversial extensively debated connection halide perovskites. show it also present hard perovskite, originates from highly overdamped R-tilt mode structure. Published by American Physical Society 2025

Language: Английский

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Unified differentiable learning of electric response

Nature Communications, Journal Year: 2025, Volume and Issue: 16(1)

Published: April 29, 2025

Predicting response of materials to external stimuli is a primary objective computational science. However, current methods are limited small-scale simulations due the unfavorable scaling costs. Here, we implement an equivariant machine-learning framework where properties stem from exact differential relationships between generalized potential function and applied fields. Focusing on responses electric fields, method predicts enthalpy, forces, polarization, Born charges, polarizability within unified model enforcing full set physical constraints, symmetries conservation laws. Through application α-SiO2, demonstrate that our approach can be used for predicting vibrational dielectric materials, conducting large-scale dynamics under arbitrary fields at unprecedented accuracy scale. We apply ferroelectric BaTiO3 capture temperature dependence, frequency time evolution hysteresis, revealing underlying intrinsic mechanisms nucleation growth govern domain switching.

Language: Английский

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Boron isotope effects on Raman scattering in bulk BN, BP, and BAs: A density functional theory study

Physical review. B./Physical review. B, Journal Year: 2025, Volume and Issue: 111(20)

Published: May 19, 2025

For many materials, Raman spectra are intricately structured and provide valuable information about compositional stoichiometry crystal quality. Here we use density-functional theory calculations, mass approximation, the intensity weighted Γ-point density of state approach to analyze scattering vibrational modes in zincblende, wurtzite, hexagonal BX (X = N, P, As) structures. The influence structure boron isotope disorder on line shapes is examined. Our results demonstrate that long-range Coulomb interactions significantly evolution cubic wurtzite BN compounds. With rate from B11 B10, a shift toward higher frequencies, as well maximum broadening asymmetry peaks, expected around 1:1 ratio. calculated excellent agreement with available experimental data. This study serves guide for understanding how symmetry affect phonons compounds, which relevant quantum single-photon emitters, heat management, quality assessments. Published by American Physical Society 2025

Language: Английский

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Accuracy and limitations of the bond polarizability model in modeling of Raman scattering from molecular dynamics simulations

The Journal of Chemical Physics, Journal Year: 2024, Volume and Issue: 161(6)

Published: Aug. 12, 2024

Calculation of Raman scattering from molecular dynamics (MD) simulations requires accurate modeling the evolution electronic polarizability system along its MD trajectory. For large systems, this necessitates use atomistic models to represent dependence on atomic coordinates. The bond model (BPM) is simplest such and has been used for spectra systems but not applied solid-state systems. Here, we systematically investigate accuracy limitations BPM parameterized density functional theory results a series simple molecules, as CO2, SO2, H2S, H2O, NH3, CH4; more complex CH2O, CH3OH, CH3CH2OH, thiophene molecules; BaTiO3 CsPbBr3 perovskite solids. We find that can reliably reproduce overall features spectra, shifts peak positions. However, with exception highly symmetric assumption non-interacting bonds limits quantitative BPM; also leads qualitatively inaccurate where deviations ground state structure are present.

Language: Английский

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Rapid Characterization of Point Defects in Solid-State Ion Conductors Using Raman Spectroscopy, Machine-Learning Force Fields, and Atomic Raman Tensors

Journal of the American Chemical Society, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 18, 2024

The successful design of solid-state photo- and electrochemical devices depends on the careful engineering point defects in ion conductors. Characterization is critical to these efforts, but best-developed techniques are difficult time-consuming. Raman spectroscopy─with its exceptional speed, flexibility, accessibility─is a promising alternative. signatures arise from due local symmetry breaking structural distortions. Unfortunately, assignment often hampered by shortage reference compounds corresponding spectra. This issue can be circumvented calculation defect-induced first principles, this computationally demanding. Here, we introduce an efficient computational procedure for prediction defect Our method leverages machine-learning force fields "atomic tensors", i.e., polarizability fluctuations motions individual atoms. We find that our reduces cost up 80% compared existing first-principles frozen-phonon approaches. These efficiency gains enable synergistic computational-experimental investigations, case allowing us precisely interpret spectra Sr(Ti

Language: Английский

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Elastic constant analysis of orthotropic steel sheets using multitask machine learning and the impulse excitation technique

Physica Scripta, Journal Year: 2024, Volume and Issue: 100(1), P. 016014 - 016014

Published: Dec. 11, 2024

Abstract This work presents a novel multitask learning approach featuring dual convolutional neural network (CNN) system for determining the elastic constants of orthotropic rolled steel sheets. In proposed model, resonance frequency spectra from impulse excitation technique are converted into 2D image data. The first CNN focuses on detecting and predicting missing peak intensities, while second utilizes features entire spectrum to predict constants, including E 11 , 22 G 12 . input include raw pixel data alongside three key categories enhanced analysis: image-based (such as mean, median, mode, skewness intensity distributions), spatial relations (including frequency, correlations, local contrast), geometric shape descriptors connectivity). results reveal that optimal number peaks (14), resolution (121 pixels), sample size (20 × 20 0.3 cm) maximize model’s efficiency. Under these conditions, model achieves R 2 values 0.952, 0.902, 0.913, RMSE 1.89 GPa, 3.09 1.92 GPa respectively. It is suggested superior prediction accuracy attributed stability Young’s modulus along rolling direction, which less variable in materials. Furthermore, study finds dependency between weight functions—including features, relations, features—as material’s anisotropy changes, underscoring importance accounting process variability predictive modeling.

Language: Английский

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Low-Frequency Raman Active Modes of Twisted Bilayer MoS$_2$

Journal of Physics Condensed Matter, Journal Year: 2024, Volume and Issue: 36(36), P. 365301 - 365301

Published: May 24, 2024

We study the low-frequency Raman active modes of twisted bilayer MoS

Language: Английский

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A Bond-Based Machine Learning Model for Molecular Polarizabilities and A Priori Raman Spectra

Journal of Chemical Theory and Computation, Journal Year: 2024, Volume and Issue: 20(22), P. 10071 - 10079

Published: Nov. 5, 2024

The use of machine learning (ML) algorithms in molecular simulations has become commonplace recent years. There now exists, for instance, a multitude ML force field that have enabled approaching

Language: Английский

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