scholarly journals Machine Learning Enables Highly Accurate Predictions of Photophysical Properties of Organic Fluorescent Materials: Emission Wavelengths and Quantum Yields

2020 ◽  
Author(s):  
Cheng-Wei Ju ◽  
Hanzhi Bai ◽  
Bo Li ◽  
Rizhang Liu
Keyword(s):  
Machine Learning ◽  
Density Functional ◽  
Large Scale ◽  
Fluorescent Dyes ◽  
Photophysical Properties ◽  
Quantum Yields ◽  
Substitution Effects ◽  
Quantum Mechanical ◽  
Quantum Mechanical Calculations ◽  
Fluorescent Materials

<div> <p>The development of functional organic fluorescent materials calls for fast and accurate predictions of photophysical parameters for processes such as high-throughput virtual screening, while the task is challenged by the limitations of quantum mechanical calculations. We establish a database covering >4,300 solvated organic fluorescent dyes and develop new machine learning (ML) approach aimed at efficient and accurate predictions of emission wavelength and photoluminescence quantum yield (PLQY). Our feature engineering has given rise to Functionalized Structure Descriptor (FSD) and Comprehensive General Solvent Descriptor (CGSD), whereby a highly black-box computational framework is realized with consistently good accuracy across different dye families, ability of describing substitution effects and solvent effects, efficiency for large-scale predictions and workability with on-the-fly learning. Evaluations with unseen molecules suggests a remarkable MAE of 0.13 for PLQY and 0.080 eV for emission energy, the latter comparable to time-dependent density functional theory (TD-DFT) calculations. An online prediction platform was constructed based on the ensemble model to make prediction in various solvents (https://www.chemfluor.top/). Our statistical learning methodology will complement quantum mechanical calculations as an efficient alternative approach for the prediction of these parameters.<br></p> </div><p> <br></p>

scholarly journals Machine Learning Enables Highly Accurate Predictions of Photophysical Properties of Organic Fluorescent Materials: Emission Wavelengths and Quantum Yields

2020 ◽  
Author(s):  
Cheng-Wei Ju ◽  
Hanzhi Bai ◽  
Bo Li ◽  
Rizhang Liu
Keyword(s):  
Machine Learning ◽  
Density Functional ◽  
Large Scale ◽  
Fluorescent Dyes ◽  
Photophysical Properties ◽  
Quantum Yields ◽  
Substitution Effects ◽  
Quantum Mechanical ◽  
Quantum Mechanical Calculations ◽  
Fluorescent Materials

<div> <p>The development of functional organic fluorescent materials calls for fast and accurate predictions of photophysical parameters for processes such as high-throughput virtual screening, while the task is challenged by the limitations of quantum mechanical calculations. We establish a database covering >4,300 solvated organic fluorescent dyes and develop new machine learning (ML) approach aimed at efficient and accurate predictions of emission wavelength and photoluminescence quantum yield (PLQY). Our feature engineering has given rise to Functionalized Structure Descriptor (FSD) and Comprehensive General Solvent Descriptor (CGSD), whereby a highly black-box computational framework is realized with consistently good accuracy across different dye families, ability of describing substitution effects and solvent effects, efficiency for large-scale predictions and workability with on-the-fly learning. Evaluations with unseen molecules suggests a remarkable MAE of 0.13 for PLQY and 0.080 eV for emission energy, the latter comparable to time-dependent density functional theory (TD-DFT) calculations. An online prediction platform was constructed based on the ensemble model to make prediction in various solvents (https://www.chemfluor.top/). Our statistical learning methodology will complement quantum mechanical calculations as an efficient alternative approach for the prediction of these parameters.<br></p> </div><p> <br></p>

Can Machine Learning Be More Accurate Than TD-DFT? Prediction of Emission Wavelengths and Quantum Yields of Organic Fluorescent Materials

2020 ◽  
Author(s):  
Cheng-Wei Ju ◽  
Hanzhi Bai ◽  
Rizhang Liu ◽  
Bo Li
Keyword(s):  
Machine Learning ◽  
Fluorescent Dyes ◽  
Practical Importance ◽  
Quantum Yields ◽  
Quantum Mechanical ◽  
Molecular Fingerprints ◽  
Photophysical Parameters ◽  
Td Dft ◽  
Fluorescent Materials ◽  
Qsar Models

The prediction of photophysical parameters is of crucial practical importance for the development of functional organic fluorescent materials, whereas the expense of quantum mechanical calculations and the relatively low universality of QSAR models have challenged the task. New avenues opened up by machine learning (ML), we establish a database of solvated organic fluorescent dyes and develop highly efficient ML models for the predictions of maximum emission/absorption wavelength and photoluminescence quantum yield (PLQY), providing a reliable and efficient potential approach to high-throughput screenings. Various combinations of ML algorithms and molecular fingerprints were investigated. For emission wavelengths, TD-DFT accuracy was achieved under realworld conditions. Reliable identification of strong fluorescent materials was also demonstrated. We show that the easily obtainable fingerprint inputs combined with proper ML algorithms enables efficient re-training based on additional datapoints, whereby systematic improvements of the ML models can be achieved utilizing experimental feedbacks.

scholarly journals Machine Learning Enables Highly Accurate Predictions of Photophysical Properties of Organic Fluorescent Materials: Emission Wavelengths and Quantum Yields

2020 ◽  
Author(s):  
Cheng-Wei Ju ◽  
Hanzhi Bai ◽  
Bo Li ◽  
Rizhang Liu
Keyword(s):  
Machine Learning ◽  
Fluorescent Dyes ◽  
Photophysical Properties ◽  
Practical Importance ◽  
Quantum Yields ◽  
Molecular Fingerprints ◽  
Photophysical Parameters ◽  
Td Dft ◽  
Fluorescent Materials ◽  
Qsar Models

<div> <p>The predictions of photophysical parameters are of crucial practical importance for the development of functional organic fluorescent materials, whereas the expense of quantum mechanical calculations and the relatively low universality of QSAR models have challenged the task. New avenues opened up by machine learning (ML), we establish a database of solvated organic fluorescent dyes and develop highly efficient ML models for the predictions of maximum emission/absorption wavelength and photoluminescence quantum yields, providing a reliable and efficient approach to high-throughput screenings. Various combinations of ML algorithms and molecular fingerprints were investigated. For emission wavelengths, TD-DFT accuracy was achieved under real-world conditions. Reliable identification of strong fluorescent materials was also demonstrated. We show that the easily obtainable consensus fingerprint inputs combined with proper ML algorithms enables efficient re-training based on additional datapoints whereby systematic improvements of our ML models can be achieved. </p></div>

scholarly journals Open-source Δ-quantum machine learning for medicinal chemistry

2021 ◽  
Author(s):  
Kenneth Atz ◽  
Clemens Isert ◽  
Markus N. A. Böcker ◽  
José Jiménez-Luna ◽  
Gisbert Schneider
Keyword(s):  
Machine Learning ◽  
Open Source ◽  
Density Functional ◽  
Large Scale ◽  
Molecular Design ◽  
State Of The Art ◽  
Computational Cost ◽  
Quantum Mechanical ◽  
Quantum Observables ◽  
Graph Neural Networks

Certain molecular design tasks benefit from fast and accurate calculations of quantum-mechanical (QM) properties. However, the computational cost of QM methods applied to drug-like compounds currently makes large-scale applications of quantum chemistry challenging. In order to mitigate this problem, we developed DelFTa, an open-source toolbox for predicting small-molecule electronic properties at the density functional (DFT) level of theory, using the Δ-machine learning principle. DelFTa employs state-of-the-art E(3)-equivariant graph neural networks that were trained on the QMugs dataset of QM properties. It provides access to a wide array of quantum observables by predicting approximations to ωB97X-D/def2-SVP values from a GFN2-xTB semiempirical baseline. Δ-learning with DelFTa was shown to outperform direct DFT learning for most of the considered QM endpoints. The software is provided as open-source code with fully-documented command-line and Python APIs.

scholarly journals Assessing Conformer Energies using Electronic Structure and Machine Learning Methods

2020 ◽  
Author(s):  
Dakota Folmsbee ◽  
Geoffrey Hutchison
Keyword(s):  
Machine Learning ◽  
Electronic Structure ◽  
Density Functional ◽  
Large Scale ◽  
Single Point ◽  
Semiempirical Method ◽  
Coupled Cluster ◽  
Scale Evaluation ◽  
Machine Learning Methods ◽  
Electronic Structure Methods

We have performed a large-scale evaluation of current computational methods, including conventional small-molecule force fields, semiempirical, density functional, ab initio electronic structure methods, and current machine learning (ML) techniques to evaluate relative single-point energies. Using up to 10 local minima geometries across ~700 molecules, each optimized by B3LYP-D3BJ with single-point DLPNO-CCSD(T) triple-zeta energies, we consider over 6,500 single points to compare the correlation between different methods for both relative energies and ordered rankings of minima. We find promise from current ML methods and recommend methods at each tier of the accuracy-time tradeoff, particularly the recent GFN2 semiempirical method, the B97-3c density functional approximation, and RI-MP2 for accurate conformer energies. The ANI family of ML methods shows promise, particularly the ANI-1ccx variant trained in part on coupled-cluster energies. Multiple methods suggest continued improvements should be expected in both performance and accuracy.

scholarly journals Assessing Conformer Energies using Electronic Structure and Machine Learning Methods

2020 ◽  
Author(s):  
Dakota Folmsbee ◽  
Geoffrey Hutchison
Keyword(s):  
Machine Learning ◽  
Electronic Structure ◽  
Density Functional ◽  
Large Scale ◽  
Single Point ◽  
Semiempirical Method ◽  
Coupled Cluster ◽  
Scale Evaluation ◽  
Machine Learning Methods ◽  
Electronic Structure Methods

We have performed a large-scale evaluation of current computational methods, including conventional small-molecule force fields, semiempirical, density functional, ab initio electronic structure methods, and current machine learning (ML) techniques to evaluate relative single-point energies. Using up to 10 local minima geometries across ~700 molecules, each optimized by B3LYP-D3BJ with single-point DLPNO-CCSD(T) triple-zeta energies, we consider over 6,500 single points to compare the correlation between different methods for both relative energies and ordered rankings of minima. We find promise from current ML methods and recommend methods at each tier of the accuracy-time tradeoff, particularly the recent GFN2 semiempirical method, the B97-3c density functional approximation, and RI-MP2 for accurate conformer energies. The ANI family of ML methods shows promise, particularly the ANI-1ccx variant trained in part on coupled-cluster energies. Multiple methods suggest continued improvements should be expected in both performance and accuracy.

Plasmonic resonances of nanoparticles from large-scale quantum mechanical simulations

2017 ◽  
Vol 31 (24) ◽  
pp. 1740003 ◽  
Author(s):  
Xu Zhang ◽  
Hongping Xiang ◽  
Mingliang Zhang ◽  
Gang Lu
Keyword(s):  
Density Functional Theory ◽  
Metallic Nanoparticles ◽  
Density Functional ◽  
Large Scale ◽  
Incident Light ◽  
Time Dependent ◽  
Coherent Motion ◽  
Quantum Mechanical ◽  
Functional Theory ◽  
Conduction Electrons

Plasmonic resonance of metallic nanoparticles results from coherent motion of its conduction electrons, driven by incident light. For the nanoparticles less than 10 nm in diameter, localized surface plasmonic resonances become sensitive to the quantum nature of the conduction electrons. Unfortunately, quantum mechanical simulations based on time-dependent Kohn–Sham density functional theory are computationally too expensive to tackle metal particles larger than 2 nm. Herein, we introduce the recently developed time-dependent orbital-free density functional theory (TD-OFDFT) approach which enables large-scale quantum mechanical simulations of plasmonic responses of metallic nanostructures. Using TD-OFDFT, we have performed quantum mechanical simulations to understand size-dependent plasmonic response of Na nanoparticles and plasmonic responses in Na nanoparticle dimers and trimers. An outlook of future development of the TD-OFDFT method is also presented.

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