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๐Ÿ” deepesh data ๐Ÿ“‚ Chemistry
Showing 2651 results for "deepesh data" in Chemistry
Chemistry Preprint PDF DOI

Stepping up enhanced rate calculations with EATR-flooding

Nicodemo Mazzaferro, Willmor J Pena Ccoa, Pilar Cossio, Glen M. Hocky ยท 2026

Several recent methods have shown that it is possible to compute rate constants of very slow biomolecular processes using simulations where a time-dependent bias is added along one or several collectiโ€ฆ

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Chemistry Preprint PDF DOI

Experimentally Accurate Graph Neural Network Predictions of Core-Electron Binding Energies

Adam E. A. Fouda, Joshua Zhou, Rodrigo Ferreira, Patrick Phillips, Valay Agarawal, Bhavnesh Jangid, Jacob J. Wardzala, Rui Ding, Junhong Chen, Nicole Tebaldi, Phay J. Ho, Laura Gagliardi, Linda Young ยท 2026

Graph neural network architectures are advantageous for predicting core-electron binding energies which depend on local bond environment effects, as the number of message passing layers defines the toโ€ฆ

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Chemistry Preprint PDF DOI

AI-Powered Surrogate Modelling for Multiscale Combustion: A Critical Review and Opportunities

Amirali Shateri, Zhiyin Yang, Yuying Yan, Manosh C. Paul, Jianfei Xie ยท 2026

Recent advances in combustion science have led to the generation of large volumes of data from high-fidelity simulations, detailed chemical-kinetic calculations and engine-relevant measurements and crโ€ฆ

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Chemistry Preprint PDF DOI

Accelerated Surface Hopping via Scaling the Spin--Orbit Coupling: Opportunities for Machine Learning

Jakub Martinka, Mahesh Kumar Sit, Pavlo O. Dral, Jiri Pittner ยท 2026

Surface hopping (SH) methods are typically employed to simulate ultrafast nonadiabatic processes, but long timescales often remain beyond their reach. To address this, accelerated SH scheme mitigate tโ€ฆ

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Chemistry Preprint PDF DOI

Errors that matter: Uncertainty-aware universal machine-learning potentials calibrated on experiments

Matthias Kellner, Teitur Hansen, Thomas Bligaard, Karsten Wedel Jacobsen, Michele Ceriotti ยท 2026

Machine-learning models of atomic-scale interactions achieve the accuracy of the quantum mechanical calculations on which they are trained, but at a dramatically lower computational cost. Their predicโ€ฆ

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Chemistry Preprint PDF DOI

Broadband impulsive stimulated Raman spectroscopy reveals electronic state-specific vibronic coupling and vibrational coherence transfer through nonadiabatic electronic coupling

Ramandeep Kaur, Shaina Dhamija, Garima Bhutani, Amit Kumar, Arijit K. De ยท 2026

Vibrational wavepacket dynamics in the ground (X) and excited (B) electronic states of iodine under impulsive-pump/broadband-probe excitation are revisited. A method for accurate chirp correction, necโ€ฆ

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Chemistry Preprint PDF DOI

Chaos Gated Tunneling Drives Molecular Reactivity in Astrophysical Environments

Saptarshi G. Dastider, K. Prashant, P. Shruti, C. Sudheesh, Jobin Cyriac ยท 2026

Accurate modeling of ion-molecule reaction networks is essential for understanding the chemical evolution of planetary ionospheres, particularly for giant planets where proton-transfer chains drive atโ€ฆ

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Chemistry Preprint PDF DOI

Chromatographic Peak Shape from a Stochastic-Diffusive Model with Multiple Retention Mechanisms: Analytic Time-Domain Expression and Derivatives

Hernan R. Sanchez ยท 2026

A time-domain analytic expression for chromatographic peak shapes is derived within a stochastic-diffusive framework that incorporates axial diffusion (molecular and multipath/Eddy), finite initial spโ€ฆ

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Chemistry Preprint PDF DOI

Beyond the Virial Expansion: Microscopic Origins of Partial Molar Volumes in LiCl Solutions

Chun-Ting Lin, Diganta Dasgupta, Tinglu Yang, Cesare Malosso, Giulia Sormani, Colin Egan, Giovanni Bussi, Ali Hassanali, Paul S. Cremer ยท 2026

Although electrolyte density measurements have been reported for over a century, employing them to obtain accurate partial molar volume (PMV) profiles as a function of salt concentration has remained โ€ฆ

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Chemistry Preprint PDF DOI

A Chemical Space Perspective on Diastereomeric Barriers in Alkylperoxy-to-Hydroperoxyalkyl Isomerization

Raghunathan Ramakrishnan ยท 2026

Low-temperature hydrocarbon autooxidation involves radical intermediates whose reactivity depends not only on the stereochemistry of the intermediates themselves, but also on that of the transient speโ€ฆ

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Chemistry Preprint PDF DOI

Predicting Solvation Free Energies of Molecules and Ions via First-Principles and Machine-Learning Molecular Dynamics

Junting Yu, Shuo-Hui Li, Ding Pan ยท 2026

The solvation free energy (SFE) of molecules and ions is a fundamental property governing their solvation behavior and solubility. Molecular simulations offer a route to compute SFEs using alchemical โ€ฆ

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Chemistry Preprint PDF DOI

Molecular Dynamics Force Field Genetic Optimization for Tri-n-butyl Phosphate Liquid

Faranak Hatami, Valmor F.de Almeida ยท 2026

An iterative optimization algorithm with MD simulations in the loop is developed and applied to optimize Lennard-Jones (LJ) parameters specific for liquid tri-n-butyl phosphate (TBP). The optimizaโ€ฆ

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Chemistry Preprint PDF DOI

Free energy differences and coexistence of clathrate structures II and H via lattice-switch Monte Carlo

Olivia S. Moro, Nigel B. Wilding, Vincent Ballenegger ยท 2026

We introduce a simulation technique to compute the free energy difference between two hydrate structures of different stoichiometry connected to a reservoir of gas molecules at a prescribed pressure. โ€ฆ

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Chemistry Preprint PDF DOI

Transferable excited-state dynamics enable screening of fluorescent protein chromophores

Rhyan Barrett, Sophia Wesely, Julia Westermayr ยท 2026

Transferable excited-state dynamics offer a route to efficient screening of photophysical behavior across molecular systems, but conventional nonadiabatic simulations remain prohibitively expensive. Hโ€ฆ

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Chemistry Preprint PDF DOI

Exact tunneling splittings from path-integral hybrid Monte Carlo with enveloping bridging potentials

Yu-Chen Wang, Jeremy O. Richardson ยท 2026

A path-integral hybrid Monte Carlo approach with enveloping bridging potentials (PIHMC-EBP) is proposed for calculating numerically exact tunneling splittings in molecular systems. The central idea isโ€ฆ

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Chemistry Preprint PDF DOI

Improving Molecular Force Fields with Minimal Temporal Information

Ali Mollahosseini, Mohammed Haroon Dupty, Wee Sun Lee ยท 2026

Accurate prediction of energy and forces for 3D molecular systems is one of fundamental challenges at the core of AI for Science applications. Many powerful and data-efficient neural networks predict โ€ฆ

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Chemistry Preprint PDF DOI

Inverse Design of Inorganic Compounds with Generative AI

Hannes Kneiding, Lucia Moran-Gonzalez, Nishamol Kuriakose, Ainara Nova, David Balcells ยท 2026

Machine learning is revolutionizing chemistry. Beyond the value of predictive models accelerating virtual screening, generative AI aims at enabling inverse design, reversing the compound-to-property pโ€ฆ

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Chemistry Preprint PDF DOI

Transferable FB-GNN-MBE Framework for Potential Energy Surfaces: Data-Adaptive Transfer Learning in Deep Learned Many-Body Expansion Theory

Siqi Chen, Zhiqiang Wang, Yili Shen, Xianqi Deng, Xi Cheng, Cheng-Wei Ju, Jun Yi, Guo Ling, Dieaa Alhmoud, Hui Guan, Zhou Lin ยท 2026

Mechanistic understanding and rational design of complex chemical systems depend on fast and accurate predictions of electronic structures beyond individual building blocks. However, if the system excโ€ฆ

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Chemistry Preprint PDF DOI

Experimental proof of strong $\Pi$-$\Sigma$ mixing in the Renner-Teller and Pseudo-Jahn-Teller affected CCH$^+$ ($^3\Pi$) ion

Kim Steenbakkers, P. Bryan Changala, Weslley G. D. P. Silva, John F. Stanton, Filippo Lipparini, Jurgen Gauss, Oskar Asvany, Gerrit C. Groenenboom, Britta Redlich, Stephan Schlemmer, Sandra Brunken ยท 2026

The ethynyl radical cation, CCH$^+$ ($^3\Pi$), offers a unique system for fundamental spectroscopic studies of non-adiabatic effects due to its open-shell linear structure and the presence of a low-lyโ€ฆ

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Chemistry Preprint PDF DOI

Active Learning for Generalizable Detonation Performance Prediction of Energetic Materials

R. Seaton Ullberg, Megan C. Davis, Jeremy N. Schroeder, Andrew H. Salij, M. J. Cawkwell, Christopher J. Snyder, Wilton J. M. Kort-Kamp, Ivana Matanovic ยท 2026

The discovery of new energetic materials is critical for advancing technologies from defense to private industry. However, experimental approaches remain slow and expensive while computational alternaโ€ฆ

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