AIMC Topic: Quantum Theory

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Quantum computing for near-term applications in generative chemistry and drug discovery.

Drug discovery today
In recent years, drug discovery and life sciences have been revolutionized with machine learning and artificial intelligence (AI) methods. Quantum computing is touted to be the next most significant leap in technology; one of the main early practical...

Exploring the Advantages of Quantum Generative Adversarial Networks in Generative Chemistry.

Journal of chemical information and modeling
De novo drug design with desired biological activities is crucial for developing novel therapeutics for patients. The drug development process is time- and resource-consuming, and it has a low probability of success. Recent advances in machine learni...

An Efficient Approach to Large-Scale Ab Initio Conformational Energy Profiles of Small Molecules.

Molecules (Basel, Switzerland)
Accurate conformational energetics of molecules are of great significance to understand maby chemical properties. They are also fundamental for high-quality parameterization of force fields. Traditionally, accurate conformational profiles are obtaine...

DeePKS + ABACUS as a Bridge between Expensive Quantum Mechanical Models and Machine Learning Potentials.

The journal of physical chemistry. A
Recently, the development of machine learning (ML) potentials has made it possible to perform large-scale and long-time molecular simulations with the accuracy of quantum mechanical (QM) models. However, for different levels of QM methods, such as de...

Integration of Quantum Chemistry, Statistical Mechanics, and Artificial Intelligence for Computational Spectroscopy: The UV-Vis Spectrum of TEMPO Radical in Different Solvents.

Journal of chemical theory and computation
The ongoing integration of quantum chemistry, statistical mechanics, and artificial intelligence is paving the route toward more effective and accurate strategies for the investigation of the spectroscopic properties of medium-to-large size chromopho...

Orbital Mixer: Using Atomic Orbital Features for Basis-Dependent Prediction of Molecular Wavefunctions.

Journal of chemical theory and computation
Leveraging ab initio data at scale has enabled the development of machine learning models capable of extremely accurate and fast molecular property prediction. A central paradigm of many previous studies focuses on generating predictions for only a f...

Quantum Neural Networks and Topological Quantum Field Theories.

Neural networks : the official journal of the International Neural Network Society
Our work intends to show that: (1) Quantum Neural Networks (QNNs) can be mapped onto spin-networks, with the consequence that the level of analysis of their operation can be carried out on the side of Topological Quantum Field Theory (TQFT); (2) A nu...

Quantum Perturbation Theory Using Tensor Cores and a Deep Neural Network.

Journal of chemical theory and computation
Time-independent quantum response calculations are performed using Tensor cores. This is achieved by mapping density matrix perturbation theory onto the computational structure of a deep neural network. The main computational cost of each deep layer ...

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...