8 hasil untuk "Pinheiro, Max"
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Artikel Jurnal Teks lengkap
Simulations of molecular photodynamics in long timescales
Nonadiabatic dynamics simulations in the long timescale (much longer than 10 ps) are the next challenge in computational photochemistry. This paper delimits the scope of what we expect from methods to run such simulations: they should work in full nuclear dimensionality, be general enough to tackle…
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Artikel Jurnal Teks lengkap
Choosing the right molecular machine learning potential
Quantum-chemistry simulations based on potential energy surfaces of molecules provide invaluable insight into the physicochemical processes at the atomistic level and yield such important observables as reaction rates and spectra. Machine learning potentials promise to significantly reduce the…
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Artikel Jurnal Teks lengkap
Temperature effects on the internal conversion of excited adenine and adenosine
This work aims to elucidate the dependence of the excited-state lifetime of adenine and adenosine on temperature. So far, it has been experimentally shown that while adenine's lifetime is unaffected by temperature, adenosine's lifetime strongly depends on it. However, the non-Arrhenius temperature…
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Artikel Jurnal Teks lengkap
Modeling the heating and cooling of a chromophore after photoexcitation
The heating of a chromophore due to internal conversion and its cooling down due to energy dissipation to the solvent are crucial phenomena to characterize molecular photoprocesses. In this work, we simulated the ab initio nonadiabatic dynamics of cytosine, a prototypical chromophore undergoing…
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Artikel Jurnal Teks lengkap
Correction: Modeling the heating and cooling of a chromophore after photoexcitation
Correction for ‘Modeling the heating and cooling of a chromophore after photoexcitation’ by Elizete Ventura et al., Phys. Chem. Chem. Phys., 2022, 24, 9403–9410, https://doi.org/10.1039/D2CP00686C.
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Artikel Jurnal Teks lengkap
MLatom 2: An Integrative Platform for Atomistic Machine Learning
Atomistic machine learning (AML) simulations are used in chemistry at an ever-increasing pace. A large number of AML models has been developed, but their implementations are scattered among different packages, each with its own conventions for input and output. Thus, here we give an overview of our…
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Artikel Jurnal Teks lengkap
Machine learning for nonadiabatic molecular dynamics: best practices and recent progress
Exploring molecular excited states holds immense significance across organic chemistry, chemical biology, and materials science. Understanding the photophysical properties of molecular chromophores is crucial for designing nature-inspired functional molecules, with applications ranging from…
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Artikel Jurnal Teks lengkap
ULaMDyn: enhancing excited-state dynamics analysis through streamlined unsupervised learning
The analysis of nonadiabatic molecular dynamics (NAMD) data presents significant challenges due to its high dimensionality and complexity. To address these issues, we introduce ULaMDyn, a Python-based, open-source package designed to automate the unsupervised analysis of large datasets generated by…