Erlangen colleagues synthesized a series of N-heterotriangulenes, which can be used for dye-sensitized solar cells and potentially for fluorescent pH sensors.
Dr. Pavlo O. Dral
Computer Chemistry
Pyridyl N-Heterotriangulenes for Photovoltaics
Benchmark of Semiempirical Methods
What is the best semiempirical method to use for your system? Find out in the most extensive benchmark study.
Details on OMx Methods
Details about theory and implementation of up-to-now most advanced semiempirical quantum-chemical methods are published.
Highlight about Δ-ML Approach Most Viewed in 2015
Δ-ML approach drastically reduces mean absolute error (MAE) in atomization enthalpies for PM7 and B3LYP methods in respect to accurate G4MP2 values
A highlight by Jan Jensen about the Δ-ML approach proposed by us [1] was the most viewed highlight in Computational Chemistry Highlights in 2015. This marks a pleasant ending of the last-year research on improving accuracy of computationally less demanding methods for computational chemistry. The Δ-ML approach along with the automatic parametrization technique (APT) [2] corrects faults of both lower level quantum mechanics (QM) and ML approaches by combining them in hybrid QM/ML techniques. The highlighted technique uses ML to correct errors of quantum chemical calculations after they were performed, while APT uses ML to improve quantum chemical method before running calculations with it.
1. Pavlo O. Dral, O. Anatole von Lilienfeld, Walter Thiel, Machine Learning of Parameters for Accurate Semiempirical Quantum Chemical Calculations. J. Chem. Theory Comput. 2015, 11, 2120–2125. DOI: 10.1021/acs.jctc.5b00141.
2. Raghunathan Ramakrishnan, Pavlo O. Dral, Matthias Rupp, O. Anatole von Lilienfeld, Big Data meets Quantum Chemistry Approximations: The Δ-Machine Learning Approach. J. Chem. Theory Comput. 2015, ASAP. DOI: 10.1021/acs.jctc.5b00099.
arXiv:1503.04987 [physics.chem-ph].
New Script for Translating Wikipedia Links
Wikipedia is treasure trove of free knowledge shared by volunteers. While English Wikipedia is the most known and has the largest number of articles, you may find information in more than 200 different languages. Obviously some topics are covered better in specific languages. For instance, geography of Germany is way better described in German Wikipedia than in English.
Correcting Differences with Machine Learning
Pavlo Dral
Posted in Machine Learning in Chemistry, Method Development, News, Semiempirical Methods
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1 Comment ↓
Δ-ML approach drastically reduces mean absolute error (MAE) in atomization enthalpies for PM7 and B3LYP methods in respect to accurate G4MP2 values
In our recent study, we propose using machine learning (ML) to correct differences in properties calculated at two quantum chemical (QC) methods with different accuracy.
In the Δ-ML approach ML model is trained on differences between some property \(P_t\) calculated at the target level of theory and property \(P’_b\) calculated at the baseline level of theory. Then this ML model is used to predict \(\Delta_b^t\) for out-of-sample molecules.
Machine Learning of Semiempirical Parameters
Pavlo Dral
Posted in Machine Learning in Chemistry, Method Development, News, Semiempirical Methods
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No Comments ↓
We propose using machine learning (ML) for improving semiempirical Hamiltonian. Given sufficiently large training set ML can be used to correct parameters of semiempirical quantum chemical (SQC) method individually for any target molecule.
Improvement of a semiempirical quantum chemical (SQC) method using Machine Learning (ML)
Such automatic parametrization technique (APT) stands in stark contrast to the traditional special-purpose reparametrization (SPR), when parameters are optimized for specific type of molecules and then resulting rSQC method is used unchanged for every other target molecule.
For our studies we used subset of huge database published by us. Hybrid ML-SQC approach has much lower error in predicted atomization enthalpies in comparison with SQC method with standard parameters.
National Wealth and High-Quality Research
In a response to my previous post “Geography of High-Quality Science” Rasmus Persson has raised an interesting question: to what extent does or does not national GDP correlate with the “research output”? Here I try to answer it and analyze trends.
To plot GDP values vs respective weighted fractional counts (WFCs) for each country covered by Nature Index, I have taken the same WFCs as in my previous post and GDP estimates derived from purchasing power parity (PPP) calculations as listed in the CIA World Factbook (in billions of international dollars, data retrieved from the Wikipedia article, the GDP for Vatican is derived from National Geographic page). Keep in mind that GDP values are different in different sources, but the general trends must be similar.
Geography of High-Quality Science
Nature publishing group introduced Nature Index at the end of 2014. This index is essentially a collection of author affiliations mentioned in the research papers published in 68 selected high-quality journals. Nature index can be used (and respective tools are provided in NPG website) to assess the high-quality scientific output by region, research institution and subject. Obviously Index by itself does not say anything about the quality level of an individual study or researcher. Nevertheless, it is quite useful to estimate general trends.
New 1D Coordination Polymers
New paddlewheel one-dimensional coordination complexes, M = Rh, Ru and Mo
In our study we reported synthesis, and experimental and theoretical characterization of new one-dimensional coordination polymers. Research article “Multiply Bonded Metal(II) Acetate (Rhodium, Ruthenium, and Molybdenum) Complexes with the trans-1,2-Bis(N-methylimidazol-2-yl)ethylene Ligand” (DOI: 10.1021/ic501435a) was published on November 13th in the Inorganic Chemistry. The experimental work was done in the group of Prof. Dr. Nicolai Burzlaff and theoretical part in the group of Prof. Dr. Timothy Clark.
Tagged with: Inorg. Chem.
