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Integration of theory, simulation, artificial intelligence and virtual reality: a four-pillar approach for reconciling accuracy and interpretability in computational spectroscopy.
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In this article, the authors describe new perspectives for computational spectroscopy, in the framework of a strategy in which computational methodologies at the state of the art, high-performance computing, artificial intelligence and virtual reality tools are integrated with the aim of improving research throughput and achieving goals otherwise not possible.Abstract:
The established pillars of computational spectroscopy are theory and computer based simulations. Recently, artificial intelligence and virtual reality are becoming the third and fourth pillars of an integrated strategy for the investigation of complex phenomena. The main goal of the present contribution is the description of some new perspectives for computational spectroscopy, in the framework of a strategy in which computational methodologies at the state of the art, high-performance computing, artificial intelligence and virtual reality tools are integrated with the aim of improving research throughput and achieving goals otherwise not possible. Some of the key tools (e.g., continuous molecular perception model and virtual multifrequency spectrometer) and theoretical developments (e.g., non-periodic boundaries, joint variational-perturbative models) are shortly sketched and their application illustrated by means of representative case studies taken from recent work by the authors. Some of the results presented are already well beyond the state of the art in the field of computational spectroscopy, thereby also providing a proof of concept for other research fields.read more
Citations
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Extending the Applicability of the Semi-experimental Approach by Means of "Template Molecule" and "Linear Regression" Models on Top of DFT Computations.
TL;DR: In this article, a template molecule approach is used to account for the modifications occurring when going from the isolated fragment to the molecular system under investigation, with the linear regression (LR) model employed to correct the linkage between the different fragments.
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Fast exploration of Potential Energy Surfaces with a joint venture of Quantum Chemistry, Evolutionary Algorithms and Unsupervised Learning
TL;DR: In this paper, a flat potential energy surface (PES) involving a large number of energy minima with comparable stability is used to study flexible molecules, whose conformational behavior is ruled by flat PESs.
Journal ArticleDOI
Benchmark Structures and Conformational Landscapes of Amino Acids in the Gas Phase: A Joint Venture of Machine Learning, Quantum Chemistry, and Rotational Spectroscopy
TL;DR: In this paper , a new computational setup rooted in quantum-chemical computations of increasing accuracy guided by machine learning tools is proposed to validate a new set of representative amino acids (glycine, alanine, serine, cysteine, threonine, aspartic acid and asparagine).
Journal ArticleDOI
Molecular Dynamics Simulations Enforcing Nonperiodic Boundary Conditions: New Developments and Application to the Solvent Shifts of Nitroxide Magnetic Parameters
Giordano Mancini,Marco Fusè,Filippo Lipparini,Michele Nottoli,Giovanni Scalmani,Vincenzo Barone +5 more
TL;DR: The effectiveness of the approach, even in its present form, is demonstrated by the accuracy of the results obtained through an unsupervised approach characterized by a strongly reduced computational cost as compared to that of conventional QM/MM models, without any appreciable deterioration of accuracy.
Journal ArticleDOI
Toward Accurate yet Effective Computations of Rotational Spectroscopy Parameters for Biomolecule Building Blocks
TL;DR: In this paper , a model based on density functional theory is proposed to predict rotational constants with an accuracy of 0.3% or better, which can be used to characterize larger flexible building blocks.
References
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