Extending Machine Learning to Fermion–Boson Coupled Systems and Excited-State Calculations

2021 ◽  
Vol 1 ◽  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Excited State ◽  
Wave Functions ◽  
Coupled Systems ◽  
Many Body

We have demonstrated the power of machine learning in representing quantum many-body states accurately. We have extended the applicability of neural-network wave functions and shown their usefulness in fermion-boson-coupled systems and excited-state calculations.

Digraph states and their neural network representations

2021 ◽  
Author(s):  
Ying Yang ◽  
Huaixin Cao
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Body Problem ◽  
Theoretical Foundation ◽  
Rapid Development ◽  
Quantum States ◽  
Machine Learning Method ◽  
Learning Method ◽  
Many Body ◽  
Graph State

Abstract With the rapid development of machine learning, artificial neural networks provide a powerful tool to represent or approximate many-body quantum states. It was proved that every graph state can be generated by a neural network. In this paper, we aim to introduce digraph states and explore their neural network representations (NNRs). Based on some discussions about digraph states and neural network quantum states (NNQSs), we construct explicitly the NNR for any digraph state, implying every digraph state is an NNQS. The obtained results will provide a theoretical foundation for solving the quantum many-body problem with machine learning method whenever the wave-function is known as an unknown digraph state or it can be approximated by digraph states.

Anti-Kasha System by Design: A New Gateway for Cell Differentiation Through Machine Learning

2020 ◽  
Author(s):  
Junyi Gong ◽  
Peifa Wei ◽  
Junkai Liu ◽  
Yuncong Chen ◽  
zheng zhao ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Excited State ◽  
Cell Differentiation ◽  
High Energy ◽  
High Accuracy ◽  
Widespread Application ◽  
New Gateway ◽  
Photoluminescent Materials ◽  
Rule System

<div>Kasha’s rule, which claimed that all emissions of excitons are from the lowest excited state and independent of excitation energy, makes the utility of high energy excitons difficult and severely hinder the widespread application of organic photoluminescent materials in real-world. For decades, scientists try to break it to unleash the power of high energy excitons but get minimal progress, no rational guiding principles, and few applications. So far, anti-Kasha’s rule is still a purely academic concept. In this contribution, we proposed a designing principle for pure organic anti-Kasha’s rule system and synthesized a series of compounds by following this designing rule which are all display evident S 2 emission in dilute solutions as proposed. Besides, we introduced a convolutional neural network as an assistant to apply the anti-Kasha’s rule luminogens to cell differentiations with high accuracy (~98.3%), which provide a new direction of applications of anti-Kasha system.</div>

Anti-Kasha System by Design: A New Gateway for Cell Differentiation Through Machine Learning

2020 ◽  
Author(s):  
Junyi Gong ◽  
Peifa Wei ◽  
Junkai Liu ◽  
Yuncong Chen ◽  
zheng zhao ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Excited State ◽  
Cell Differentiation ◽  
High Energy ◽  
High Accuracy ◽  
Widespread Application ◽  
New Gateway ◽  
Photoluminescent Materials ◽  
Rule System

<div>Kasha’s rule, which claimed that all emissions of excitons are from the lowest excited state and independent of excitation energy, makes the utility of high energy excitons difficult and severely hinder the widespread application of organic photoluminescent materials in real-world. For decades, scientists try to break it to unleash the power of high energy excitons but get minimal progress, no rational guiding principles, and few applications. So far, anti-Kasha’s rule is still a purely academic concept. In this contribution, we proposed a designing principle for pure organic anti-Kasha’s rule system and synthesized a series of compounds by following this designing rule which are all display evident S 2 emission in dilute solutions as proposed. Besides, we introduced a convolutional neural network as an assistant to apply the anti-Kasha’s rule luminogens to cell differentiations with high accuracy (~98.3%), which provide a new direction of applications of anti-Kasha system.</div>

scholarly journals Quantum restricted Boltzmann machine is universal for quantum computation

2020 ◽  
Author(s):  
Yusen Wu ◽  
Chunyan Wei ◽  
Su-Juan Qin ◽  
Qiaoyan Wen ◽  
Fei Gao
Keyword(s):  
Neural Network ◽  
Quantum Computation ◽  
Single Layer ◽  
Wave Functions ◽  
Restricted Boltzmann Machine ◽  
Boltzmann Machine ◽  
Many Body ◽  
Acceptable Error ◽  
Quantum Neural Network ◽  
The Many

Abstract The challenge posed by the many-body problem in quantum physics originates from the difficulty of describing the nontrivial correlations encoded in the many-body wave functions with high complexity. Quantum neural network provides a powerful tool to represent the large-scale wave function, which has aroused widespread concerns in the quantum superiority era. A significant open problem is what exactly the representational power boundary of the single-layer quantum neural network is. In this paper, we design a 2-local Hamiltonian and then give a kind of Quantum Restricted Boltzmann Machine (QRBM, i.e. single-layer quantum neural network) based on it. The proposed QRBM has the following two salient features. (1) It is proved universal for implementing quantum computation tasks. (2) It can be efficiently implemented on the Noisy Intermediate-Scale Quantum (NISQ) devices. We successfully utilize the proposed QRBM to compute the wave functions for the notable cases of physical interest including the ground state as well as the Gibbs state (thermal state) of molecules on the superconducting quantum chip. The experimental results illustrate the proposed QRBM can compute the above wave functions with an acceptable error.

Nanosecond Photodynamics Simulations of a Cis-Trans Isomerization Are Enabled by Machine Learning

2020 ◽  
Author(s):  
Jingbai Li ◽  
Patrick Reiser ◽  
André Eberhard ◽  
Pascal Friederich ◽  
Steven Lopez
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Excited State ◽  
Adaptive Sampling ◽  
Computational Cost ◽  
Ground Truth ◽  
Absolute Error ◽  
Photochemical Reactions ◽  
Computational Techniques ◽  
Full Potential

<p>Photochemical reactions are being increasingly used to construct complex molecular architectures with mild and straightforward reaction conditions. Computational techniques are increasingly important to understand the reactivities and chemoselectivities of photochemical isomerization reactions because they offer molecular bonding information along the excited-state(s) of photodynamics. These photodynamics simulations are resource-intensive and are typically limited to 1–10 picoseconds and 1,000 trajectories due to high computational cost. Most organic photochemical reactions have excited-state lifetimes exceeding 1 picosecond, which places them outside possible computational studies. Westermeyr <i>et al.</i> demonstrated that a machine learning approach could significantly lengthen photodynamics simulation times for a model system, methylenimmonium cation (CH<sub>2</sub>NH<sub>2</sub><sup>+</sup>).</p><p>We have developed a Python-based code, Python Rapid Artificial Intelligence <i>Ab Initio</i> Molecular Dynamics (PyRAI<sup>2</sup>MD), to accomplish the unprecedented 10 ns <i>cis-trans</i> photodynamics of <i>trans</i>-hexafluoro-2-butene (CF<sub>3</sub>–CH=CH–CF<sub>3</sub>) in 3.5 days. The same simulation would take approximately 58 years with ground-truth multiconfigurational dynamics. We proposed an innovative scheme combining Wigner sampling, geometrical interpolations, and short-time quantum chemical trajectories to effectively sample the initial data, facilitating the adaptive sampling to generate an informative and data-efficient training set with 6,232 data points. Our neural networks achieved chemical accuracy (mean absolute error of 0.032 eV). Our 4,814 trajectories reproduced the S<sub>1</sub> half-life (60.5 fs), the photochemical product ratio (<i>trans</i>: <i>cis</i> = 2.3: 1), and autonomously discovered a pathway towards a carbene. The neural networks have also shown the capability of generalizing the full potential energy surface with chemically incomplete data (<i>trans</i> → <i>cis</i> but not <i>cis</i> → <i>trans</i> pathways) that may offer future automated photochemical reaction discoveries.</p>

Adsorption Isotherm Predictions for Multiple Molecules in MOFs Using the Same Deep Learning Model

2019 ◽  
Author(s):  
Ryther Anderson ◽  
Achay Biong ◽  
Diego Gómez-Gualdrón
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Molecular Simulation ◽  
Large Scale ◽  
Learning Model ◽  
Operating Conditions ◽  
Small Subset ◽  
Screening Methods ◽  
Large Set ◽  
Metal Organic

<div>Tailoring the structure and chemistry of metal-organic frameworks (MOFs) enables the manipulation of their adsorption properties to suit specific energy and environmental applications. As there are millions of possible MOFs (with tens of thousands already synthesized), molecular simulation, such as grand canonical Monte Carlo (GCMC), has frequently been used to rapidly evaluate the adsorption performance of a large set of MOFs. This allows subsequent experiments to focus only on a small subset of the most promising MOFs. In many instances, however, even molecular simulation becomes prohibitively time consuming, underscoring the need for alternative screening methods, such as machine learning, to precede molecular simulation efforts. In this study, as a proof of concept, we trained a neural network as the first example of a machine learning model capable of predicting full adsorption isotherms of different molecules not included in the training of the model. To achieve this, we trained our neural network only on alchemical species, represented only by their geometry and force field parameters, and used this neural network to predict the loadings of real adsorbates. We focused on predicting room temperature adsorption of small (one- and two-atom) molecules relevant to chemical separations. Namely, argon, krypton, xenon, methane, ethane, and nitrogen. However, we also observed surprisingly promising predictions for more complex molecules, whose properties are outside the range spanned by the alchemical adsorbates. Prediction accuracies suitable for large-scale screening were achieved using simple MOF (e.g. geometric properties and chemical moieties), and adsorbate (e.g. forcefield parameters and geometry) descriptors. Our results illustrate a new philosophy of training that opens the path towards development of machine learning models that can predict the adsorption loading of any new adsorbate at any new operating conditions in any new MOF.</div>

Artificial neural network models for coronary artery disease

2020 ◽  
Vol 15 ◽  
Author(s):  
Elham Shamsara ◽  
Sara Saffar Soflaei ◽  
Mohammad Tajfard ◽  
Ivan Yamshchikov ◽  
Habibollah Esmaili ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Coronary Artery Disease ◽  
Pattern Recognition ◽  
Artificial Neural Network ◽  
Coronary Artery ◽  
Diagnostic Model ◽  
Early Prediction ◽  
Artificial Neural ◽  
Artery Disease

Background: Coronary artery disease (CAD) is an important cause of mortality and morbidity globally. Objective : The early prediction of the CAD would be valuable in identifying individuals at risk, and in focusing resources on its prevention. In this paper, we aimed to establish a diagnostic model to predict CAD by using three approaches of ANN (pattern recognition-ANN, LVQ-ANN, and competitive ANN). Methods: One promising method for early prediction of disease based on risk factors is machine learning. Among different machine learning algorithms, the artificial neural network (ANN) algo-rithms have been applied widely in medicine and a variety of real-world classifications. ANN is a non-linear computational model, that is inspired by the human brain to analyze and process complex datasets. Results: Different methods of ANN that are investigated in this paper indicates in both pattern recognition ANN and LVQ-ANN methods, the predictions of Angiography+ class have high accuracy. Moreover, in CNN the correlations between the individuals in cluster ”c” with the class of Angiography+ is strongly high. This accuracy indicates the significant difference among some of the input features in Angiography+ class and the other two output classes. A comparison among the chosen weights in these three methods in separating control class and Angiography+ shows that hs-CRP, FSG, and WBC are the most substantial excitatory weights in recognizing the Angiography+ individuals although, HDL-C and MCH are determined as inhibitory weights. Furthermore, the effect of decomposition of a multi-class problem to a set of binary classes and random sampling on the accuracy of the diagnostic model is investigated. Conclusion : This study confirms that pattern recognition-ANN had the most accuracy of performance among different methods of ANN. That’s due to the back-propagation procedure of the process in which the network classify input variables based on labeled classes. The results of binarization show that decomposition of the multi-class set to binary sets could achieve higher accuracy.

Using artificial neural network condensation to facilitate adaption of machine learning in medical settings by reducing computational burden (Preprint)

2020 ◽  
Author(s):  
Dianbo Liu
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Third World ◽  
Mortality Prediction ◽  
Neural Net ◽  
Medical Settings ◽  
Hidden Layer ◽  
Applications Of Machine Learning ◽  
Computational Resources ◽  
Developed Nations

BACKGROUND Applications of machine learning (ML) on health care can have a great impact on people’s lives. At the same time, medical data is usually big, requiring a significant amount of computational resources. Although it might not be a problem for wide-adoption of ML tools in developed nations, availability of computational resource can very well be limited in third-world nations and on mobile devices. This can prevent many people from benefiting of the advancement in ML applications for healthcare. OBJECTIVE In this paper we explored three methods to increase computational efficiency of either recurrent neural net-work(RNN) or feedforward (deep) neural network (DNN) while not compromising its accuracy. We used in-patient mortality prediction as our case analysis upon intensive care dataset. METHODS We reduced the size of RNN and DNN by applying pruning of “unused” neurons. Additionally, we modified the RNN structure by adding a hidden-layer to the RNN cell but reduce the total number of recurrent layers to accomplish a reduction of total parameters in the network. Finally, we implemented quantization on DNN—forcing the weights to be 8-bits instead of 32-bits. RESULTS We found that all methods increased implementation efficiency–including training speed, memory size and inference speed–without reducing the accuracy of mortality prediction. CONCLUSIONS This improvements allow the implementation of sophisticated NN algorithms on devices with lower computational resources.

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