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Quantum Theory

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Deep learning study of tyrosine reveals that roaming can lead to photodamage.

Nature chemistry
Amino acids are among the building blocks of life, forming peptides and proteins, and have been carefully 'selected' to prevent harmful reactions caused by light. To prevent photodamage, molecules relax from electronic excited states to the ground st...

Reconstruction of Nuclear Ensemble Approach Electronic Spectra Using Probabilistic Machine Learning.

Journal of chemical theory and computation
The theoretical prediction of molecular electronic spectra by means of quantum mechanical (QM) computations is fundamental to gain a deep insight into many photophysical and photochemical processes. A computational strategy that is attracting signifi...

Brain-inspired computing needs a master plan.

Nature
New computing technologies inspired by the brain promise fundamentally different ways to process information with extreme energy efficiency and the ability to handle the avalanche of unstructured and noisy data that we are generating at an ever-incre...

Predicting the future of excitation energy transfer in light-harvesting complex with artificial intelligence-based quantum dynamics.

Nature communications
Exploring excitation energy transfer (EET) in light-harvesting complexes (LHCs) is essential for understanding the natural processes and design of highly-efficient photovoltaic devices. LHCs are open systems, where quantum effects may play a crucial ...

CIDER: An Expressive, Nonlocal Feature Set for Machine Learning Density Functionals with Exact Constraints.

Journal of chemical theory and computation
Machine learning (ML) has recently gained attention as a means to develop more accurate exchange-correlation (XC) functionals for density functional theory, but functionals developed thus far need to be improved on several metrics, including accuracy...

Disruptive innovation in psychiatry.

Annals of the New York Academy of Sciences
Disruptive innovation is a cornerstone of various disciplines, particularly in the business world, where paradigm-altering approaches are often lauded. As a construct, disruptive psychiatry can be considered to embody such an approach by the pursuit ...

TorsionNet: A Deep Neural Network to Rapidly Predict Small-Molecule Torsional Energy Profiles with the Accuracy of Quantum Mechanics.

Journal of chemical information and modeling
Fast and accurate assessment of small-molecule dihedral energetics is crucial for molecular design and optimization in medicinal chemistry. Yet, accurate prediction of torsion energy profiles remains challenging as the current molecular mechanics (MM...

Variational quantum classifiers through the lens of the Hessian.

PloS one
In quantum computing, the variational quantum algorithms (VQAs) are well suited for finding optimal combinations of things in specific applications ranging from chemistry all the way to finance. The training of VQAs with gradient descent optimization...

QPoweredCompound2DeNovoDrugPropMax - a novel programmatic tool incorporating deep learning and methods for automated in silico bio-activity discovery for any compound of interest.

Journal of biomolecular structure & dynamics
Network data is composed of nodes and edges. Successful application of machine learning/deep learning algorithms on network data to make node classification and link prediction have been shown in the area of social networks through which highly custo...

Edge Caching in Fog-Based Sensor Networks through Deep Learning-Associated Quantum Computing Framework.

Computational intelligence and neuroscience
Fog computing (FC) based sensor networks have emerged as a propitious archetype for next-generation wireless communication technology with caching, communication, and storage capacity services in the edge. Mobile edge computing (MEC) is a new era of ...