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๐Ÿ” deepak pathak ๐Ÿ“‚ Chemistry
Showing 340 results for "deepak pathak" in Chemistry
Chemistry Preprint PDF DOI

A Machine-Learned Symbolic Committor for a Chemical Reaction: Retinal Isomerization

Kai Topfer, Gianmarco Lazzeri, Vittoria Ossanna, Florian Renner, Gianluca Lattanzi, Roberto Covino, Bettina G. Keller ยท 2026

The thermal cis-trans isomerization around the C$_{13}$=C$_{14}$ double bond of retinal is a prototypical high-barrier reaction whose mechanism hinges on subtle out-of-plane bending motions. We apply โ€ฆ

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Chemistry Preprint PDF DOI

Reweighting Estimators for Density Response in Path Integral Monte Carlo: Applications to linear, nonlinear and cross-species density response

Pontus Svensson, Thomas Chuna, Jan Vorberger, Zhandos A. Moldabekov, Paul Hamann, Sebastian Schwalbe, Panagiotis Tolias, Tobias Dornheim ยท 2026

We present density response estimators for Monte Carlo simulations that are based on a reweighting procedure, where the samples of an unperturbed system are used to estimate the properties of a systemโ€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

Exact tunneling splittings of rotationally excited states from symmetrized path-integral molecular dynamics

Lea Zupan, Yu-Chen Wang, Jeremy O. Richardson ยท 2026

We extend our previous symmetrized path-integral molecular dynamics approach to calculate tunneling splittings of molecules in rotationally excited states. In this new formalism, the system is rigorouโ€ฆ

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Chemistry Preprint PDF DOI

Comparing and Contrasting Vibrational Wavepacket Dynamics and Impulsive Stimulating Raman Scattering Descriptions of Pump-Probe Spectroscopy: A Theoretical Study

Subho Mitra, Arijit K. De ยท 2026

We simulate a third-order nonlinear signal in a pump-probe spectroscopy from the interference between first- and second-order wavepackets (WPs), as well as from a state-to-state transition for Stokes โ€ฆ

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Chemistry Preprint PDF DOI

Path Integral Methods in Atomistic Modelling: An Introduction

Michele Ceriotti, David E. Manolopoulos, Thomas E. Markland, Mariana Rossi ยท 2026

This book provides an introduction to path integral methods and their application to modeling atomistic processes. The book covers both the foundational theory and recently developed simulation techniโ€ฆ

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Chemistry Preprint PDF DOI

Geometric Phase Effect in Thermodynamic Properties and in the Imaginary-Time Multi-Electronic-State Path Integral Formulation

Yu Zhai, Youhao Shang, Jian Liu ยท 2026

The geometric phase (GP) is a fundamental quantum effect arising from conical intersections (CIs), with profound consequences for vibronic energy levels. Standard imaginary-time path integral moleculaโ€ฆ

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Chemistry Preprint PDF DOI

A Priori Sampling of Transition States with Guided Diffusion

Hyukjun Lim, Soojung Yang, Lucas Pinede, Miguel Steiner, Yuanqi Du, Rafael Gomez-Bombarelli ยท 2026

Transition states, the first-order saddle points on the potential energy surfaces, govern the kinetics and mechanisms of chemical reactions and conformational changes. Locating them is challenging becโ€ฆ

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Chemistry Preprint PDF DOI

A sustainable photocatalytic pathway for concurrent hydrogen and value-added chemical production utilizing microalgae as bio-scavenger in water

Ho Truong Nam Hai, Augusto Ducati Luchessi, Kaveh Edalati ยท 2026

Microalgae are an abundant bioorganic material source and play a significant role in life on Earth by conducting photosynthesis for carbon dioxide (CO2) capture and its conversion to oxygen (O2). In tโ€ฆ

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Chemistry Preprint PDF DOI

An Accurate Tensorial Model for Prediction of Full Zeolite NMR Spectra

Carlos Bornes, Chiheb Ben Mahmoud, Volker L. Deringer, Christopher J. Heard, Lukas Grajciar ยท 2026

Solid state nuclear magnetic resonance (ss-NMR) is one of the most sensitive and popular techniques for structure elucidation in geometrically complex crystalline materials, such as zeolites. Synergisโ€ฆ

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Chemistry Preprint PDF DOI

TERS-ABNet: A Deep Learning Approach for Automated Single-Molecule Structure Reconstruction with Atomic Precision from TERS Mapping

Jie Cui, Yao Zhang, Yang Zhang, Yi Luo, Zhen-Chao Dong ยท 2026

Determining the chemical structure for a single molecule on surface from spectroscopic data represents a challenging high-dimensional inverse problem. Tip-enhanced Raman spectroscopy (TERS) enables chโ€ฆ

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Chemistry Preprint PDF DOI

Reproducible Orchestration of Best Practices for Reaction Path Optimization with the Nudged Elastic Band

Rohit Goswami ยท 2026

The nudged elastic band (NEB) method is the standard approach for finding minimum energy paths and transition states on potential energy surfaces. Practical NEB calculations require several pre-procesโ€ฆ

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Chemistry Preprint PDF DOI

Nonadiabatic rare events from transition-path sampling of MASH trajectories

Danial Ghamari, Jeremy O. Richardson ยท 2026

Rare nonadiabatic reactions are a key component of many important molecular processes but are challenging to capture with direct dynamical simulations. In this paper, we combine our recently developedโ€ฆ

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Chemistry Preprint PDF DOI

Nuclear Quantum Effects in Multi-Step Condensed Matter Chemistry: A Path Integral Molecular Dynamics Study of Thermal Decomposition

Jalen Macatangay, Alejandro Strachan ยท 2026

Nuclear quantum effects (NQEs) are often central to a predictive understanding of chemical reactions and rates. While their incorporation in gas-phase reactions is well established, studies involving โ€ฆ

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Chemistry Preprint PDF DOI

Structure-resolved free energy estimation of the 38-atom Lennard Jones cluster via population annealing

Akie Kowaguchi, Koji Hukushima ยท 2026

We systematically investigate the thermodynamic landscape of the 38-atom Lennard--Jones cluster LJ$_{38}$ using Population Annealing (PA), a method suited for systems with challenging double-funnel enโ€ฆ

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Chemistry Preprint PDF DOI

Expanding Universal Machine Learning Interatomic Potentials to 97 Elements Towards Nuclear Applications

Naoya Kuroda, Kenji Ishihara, Tomoya Shiota, Wataru Mizukami ยท 2026

Machine learning interatomic potentials (MLIPs) evaluate potential energy surfaces orders of magnitude faster while maintaining accuracy comparable to first-principles calculations, and universal MLIPโ€ฆ

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Chemistry Preprint PDF DOI

Rigorous Quantum Thermodynamics from Entropic Path Integral Coarse-Graining

Jing Shen, Ziyan Ye, Ming-Zheng Du, Shi-Yu He, Dong H. Zhang, Jia-Xi Zeng, Venkat Kapil, Wei Fang ยท 2026

Nuclear quantum effects (NQEs) remain a major challenge for molecular simulations, as rigorous treatment requires imaginary-time path-integral methods with heavy computational overhead. Neglecting NQEโ€ฆ

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Chemistry Preprint PDF DOI

Efficient Simulation of Non-Markovian Path Integrals via Imaginary Time Evolution of an Effective Hamiltonian

Xiaoyu Yang, Limin Liu, Wencheng Zhao, Jiajun Ren, Wei-Hai Fang ยท 2026

Accurately simulating the non-Markovian dynamics of open quantum systems remains a significant challenge. While the recently proposed time-evolving matrix product operator (TEMPO) algorithm based on pโ€ฆ

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Chemistry Preprint PDF DOI

fix pimd/langevin: An Efficient Implementation of Path Integral Molecular Dynamics in LAMMPS

Yifan Li, Axel Gomez, Kehan Cai, Chunyi Zhang, Li Fu, Weile Jia, Yotam M. Y. Feldman, Ofir Blumer, Jacob Higer, Barak Hirshberg, Shenzhen Xu, Axel Kohlmeyer, Roberto Car ยท 2026

Path integral molecular dynamics (PIMD), which maps a quantum particle onto a fictitious classical system of ring polymers and propagates the "beads" of this extended classical system using molecular โ€ฆ

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Chemistry Preprint PDF DOI

A Hardware-Native Realisation of Semi-Empirical Electronic Structure Theory on Field-Programmable Gate Arrays

Xincheng Miao, Roland Mitric ยท 2026

High-throughput quantum-chemical calculations underpin modern molecular modelling, materials discovery, and machine-learning workflows, yet even semi-empirical methods become restrictive when many molโ€ฆ

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