jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Anurag Kumar Sinha | Kristoffer Skovbo Winther
Source: Communications Biology (article, 2021-03-31)
Field: Materials Science — Materials Chemistry

The RelA hydrolase domain acts as a molecular switch for (p)ppGpp synthesis Bacteria synthesize guanosine tetra- and penta phosphate (commonly referred to as (p)ppGpp) in response to environmental stresses. (p)ppGpp reprograms cell physiology and is essential for stress survival, virulence and antibiotic tolerance. Proteins of the RSH superfamily (RelA/SpoT Homologues) are ubiquitously distributed and hydrolyze or synthesize (p)ppGpp. Structural studies have suggested that the shift between hydrolysis and synthesis is governed by conformational antagonism between the two active sites in RSHs. RelA proteins of γ-proteobacteria exclusively synthesize (p)ppGpp and encode an inactive pseudo-hydrolase domain. Escherichia coli RelA synthesizes (p)ppGpp in response to amino acid starvation with cognate uncharged tRNA at the ribosomal A-site, however, mechanistic details to the regulation of the enzymatic activity remain elusive. Here, we show a role of the enzymatically inactive hydrolase domain in modulating the activity of the synthetase domain of RelA. Using mutagenesis screening and functional studies, we identify a loop region (residues 114-130) in the hydrolase domain, which controls the synthetase activity. We show that a synthetase-inactive loop mutant of RelA is not affected for tRNA binding, but binds the ribosome less efficiently than wild type RelA. Our data support the model that the hydrolase domain acts as a molecular switch to regulate the synthetase activity. Hydrolase ; Chemistry ; Cell biology ; Domain (mathematical analysis) ; Computational biology ; Biology ; Biochemistry ; Enzyme ; Mathematics Enzyme Structure and Function ; RNA and protein synthesis mechanisms ; Bacterial Genetics and Biotechnology
Authors: Yuxin Li | Wenhui Yang | Rongzhi Dong | Jianjun Hu
Source: ACS Omega (article, 2021-04-20)
Field: Materials Science — Materials Chemistry

Mlatticeabc: Generic Lattice Constant Prediction of Crystal Materials Using Machine Learning Lattice constants such as unit cell edge lengths and plane angles are important parameters of the periodic structures of crystal materials. Predicting crystal lattice constants has wide applications in crystal structure prediction and materials property prediction. Previous work has used machine learning models such as neural networks and support vector machines combined with composition features for lattice constant prediction and has achieved a maximum performance for cubic structures with an average coefficient of determination (<i>R</i> <sup>2</sup>) of 0.82. Other models tailored for special materials family of a fixed form such as ABX<sub>3</sub> perovskites can achieve much higher performance due to the homogeneity of the structures. However, these models trained with small data sets are usually not applicable to generic lattice parameter prediction of materials with diverse compositions. Herein, we report MLatticeABC, a random forest machine learning model with a new descriptor set for lattice unit cell edge length (<i>a</i>, <i>b</i>, <i>c</i>) prediction which achieves an <i>R</i> <sup>2</sup> score of 0.973 for lattice parameter <i>a</i> of cubic crystals with an average <i>R</i> <sup>2</sup> score of 0.80 for <i>a</i> prediction of all crystal systems. The <i>R</i> <sup>2</sup> scores are between 0.498 and 0.757 over lattice <i>b</i> and <i>c</i> prediction performance of the model, which could be used by just inputting the molecular formula of the crystal material to get the lattice constants. Our results also show significant performance improvement for lattice angle predictions. Source code and trained models can be freely accessed at https://github.com/usccolumbia/MLatticeABC. Lattice constant ; Lattice (music) ; Crystal structure ; Lattice plane ; Homogeneity (statistics) ; Crystal (programming language) ; Artificial intelligence ; Crystal structure prediction ; Materials science ; Machine learning ; Computer science ; Algorithm ; Statistical physics ; Mathematics ; Reciprocal lattice ; Crystallography ; Physics ; Optics ; Diffraction ; Chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electronic and Structural Properties of Oxides
Authors: Yonglan Liu | Dong Zhang | Yijing Tang | Yanxian Zhang | Xiong Gong | Shaowen Xie | Jie Zheng
Source: Chemical Engineering Journal (article, 2021-04-19)
Field: Materials Science — Surfaces, Coatings and Films

Machine Learning-Enabled Repurposing and Design of Antifouling Polymer Brushes Biofouling ; Repurposing ; Support vector machine ; Computer science ; Artificial intelligence ; Benchmark (surveying) ; Machine learning ; Polymer ; Materials science ; Engineering ; Chemistry ; Composite material Polymer Surface Interaction Studies ; Marine Biology and Environmental Chemistry ; Knee injuries and reconstruction techniques
Authors: Kai Ge | Yuanhui Ji
Source: Industrial & Engineering Chemistry Research (article, 2021-06-18)
Field: Chemistry — Spectroscopy

Novel Computational Approach by Combining Machine Learning with Molecular Thermodynamics for Predicting Drug Solubility in Solvents In this work, a novel strategy that combined molecular thermodynamic and machine learning was proposed to accurately predict the solubility of drugs in various solvents. The strategy was based on 16 molecular descriptors representing drug–drug interactions and drug–solvent interactions including physical parameters, pure perturbed-chain statistical associating fluid theory (PC-SAFT) parameters of drugs and solvents, and mixing rules. These molecular descriptors were inputted into five machine learning algorithms [multiple linear regression (MLR), artificial neural network (ANN), random forest (RF), extremely randomized trees (ET), and support vector machine (SVM)] to train the predictive model. A single-hidden-layer neural network was finally determined as the predictive model for predicting the solubility of drugs in various solvents. The drug solubility in the generalization evaluation set has also been successfully predicted, which indicates the good prediction performance of the model. Three directions for improving the model were summarized as adding molecular descriptors of drug–solvent interactions in the water system and drug–drug interactions in the organic solvent system and expanding the dataset to adequately obtain the features of multiple drugs. These findings show that the proposed model has the capability of solubility prediction, which is expected to provide important information for drug development and drug solvent screening. Solubility ; Molecular descriptor ; Support vector machine ; Artificial neural network ; Machine learning ; Artificial intelligence ; Generalization ; Solvent ; Computer science ; Random forest ; Biological system ; Chemistry ; Quantitative structure–activity relationship ; Mathematics ; Organic chemistry Analytical Chemistry and Chromatography ; Crystallization and Solubility Studies ; Phase Equilibria and Thermodynamics
Authors: Aravind Krishnamoorthy | Ken‐ichi Nomura | Nitish Baradwaj | Kohei Shimamura | Pankaj Rajak | Ankit Mishra | Shogo Fukushima | Fuyuki Shimojo | Rajiv K. Kalia | Aiichiro Nakano | Priya Vashishta
Source: Physical Review Letters (article, 2021-05-25)
Field: Materials Science — Materials Chemistry

Dielectric Constant of Liquid Water Determined with Neural Network Quantum Molecular Dynamics The static dielectric constant ϵ_{0} and its temperature dependence for liquid water is investigated using neural network quantum molecular dynamics (NNQMD). We compute the exact dielectric constant in canonical ensemble from NNQMD trajectories using fluctuations in macroscopic polarization computed from maximally localized Wannier functions (MLWF). Two deep neural networks are constructed. The first, NNQMD, is trained on QMD configurations for liquid water under a variety of temperature and density conditions to learn potential energy surface and forces and then perform molecular dynamics simulations. The second network, NNMLWF, is trained to predict locations of MLWF of individual molecules using the atomic configurations from NNQMD. Training data for both the neural networks is produced using a highly accurate quantum-mechanical method, DFT-SCAN that yields an excellent description of liquid water. We produce 280×10^{6} configurations of water at 7 temperatures using NNQMD and predict MLWF centers using NNMLWF to compute the polarization fluctuations. The length of trajectories needed for a converged value of the dielectric constant at 0°C is found to be 20 ns (40×10^{6} configurations with 0.5 fs time step). The computed dielectric constants for 0, 15, 30, 45, 60, 75, and 90°C are in good agreement with experiments. Our scalable scheme to compute dielectric constants with quantum accuracy is also applicable to other polar molecular liquids. Dielectric ; Molecular dynamics ; Quantum ; Artificial neural network ; Polarization (electrochemistry) ; Physics ; Statistical physics ; Materials science ; Computational physics ; Quantum mechanics ; Physical chemistry ; Chemistry ; Computer science ; Machine learning Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Protein Structure and Dynamics
Authors: Pengcheng Xu | Tian Lu | Lifei Ju | Lumin Tian | Minjie Li | Wencong Lu
Source: The Journal of Physical Chemistry B (article, 2021-01-07)
Field: Materials Science — Polymers and Plastics

Machine Learning Aided Design of Polymer with Targeted Band Gap Based on DFT Computation Polymer band gap is one of the most important properties associated with electric conductivity. In this work, the machine learning model called support vector regression (SVR) was developed to predict the polymer band gap, where the training data of the polymer band gap were obtained from DFT computation while the descriptors were generated from Dragon. After feature selection with the maximum relevance minimum redundancy, the SVR model using 16 key features as inputs gave the optimal performance for predicting polymer band gaps. The determination coefficient (<i>R</i><sup>2</sup>) of the SVR model between the DFT computations and SVR predictions of polymer band gaps reached as high as 0.824 for the leave-one-out cross-validation and 0.925 for the independent test. Besides, the 16 key features were explored through correlation analysis and sensitivity analysis. The available model can be used to screen out the polymers with targeted band gaps before experiments, which is very helpful for rapid design of new polymers. Computation ; Support vector machine ; Band gap ; Feature selection ; Polymer ; Computer science ; Redundancy (engineering) ; Sensitivity (control systems) ; Relevance (law) ; Artificial intelligence ; Machine learning ; Materials science ; Electronic engineering ; Algorithm ; Engineering ; Optoelectronics ; Composite material Conducting polymers and applications ; Machine Learning in Materials Science ; Advanced Sensor and Energy Harvesting Materials
Authors: Tamara Husch | Jiace Sun | Lixue Cheng | Sebastian J. R. Lee | Thomas F. Miller
Source: The Journal of Chemical Physics (article, 2021-02-12)
Field: Materials Science — Materials Chemistry

Improved accuracy and transferability of molecular-orbital-based machine learning: Organics, transition-metal complexes, non-covalent interactions, and transition states Molecular-orbital-based machine learning (MOB-ML) provides a general framework for the prediction of accurate correlation energies at the cost of obtaining molecular orbitals. The application of Nesbet's theorem makes it possible to recast a typical extrapolation task, training on correlation energies for small molecules and predicting correlation energies for large molecules, into an interpolation task based on the properties of orbital pairs. We demonstrate the importance of preserving physical constraints, including invariance conditions and size consistency, when generating the input for the machine learning model. Numerical improvements are demonstrated for different datasets covering total and relative energies for thermally accessible organic and transition-metal containing molecules, non-covalent interactions, and transition-state energies. MOB-ML requires training data from only 1% of the QM7b-T dataset (i.e., only 70 organic molecules with seven and fewer heavy atoms) to predict the total energy of the remaining 99% of this dataset with sub-kcal/mol accuracy. This MOB-ML model is significantly more accurate than other methods when transferred to a dataset comprising of 13 heavy atom molecules, exhibiting no loss of accuracy on a size intensive (i.e., per-electron) basis. It is shown that MOB-ML also works well for extrapolating to transition-state structures, predicting the barrier region for malonaldehyde intramolecular proton-transfer to within 0.35 kcal/mol when only trained on reactant/product-like structures. Finally, the use of the Gaussian process variance enables an active learning strategy for extending the MOB-ML model to new regions of chemical space with minimal effort. We demonstrate this active learning strategy by extending a QM7b-T model to describe non-covalent interactions in the protein backbone-backbone interaction dataset to an accuracy of 0.28 kcal/mol. Transferability ; Transition metal ; Covalent bond ; Transition (genetics) ; Molecular orbital ; Chemical physics ; Chemistry ; Computational chemistry ; Computer science ; Molecule ; Machine learning ; Organic chemistry ; Biochemistry ; Catalysis Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Akash Arora | Tzyy‐Shyang Lin | Nathan Rebello | Sarah Av-Ron | Hidenobu Mochigase | Bradley D. Olsen
Source: ACS Macro Letters (article, 2021-10-14)
Field: Materials Science — Materials Chemistry

Random Forest Predictor for Diblock Copolymer Phase Behavior Physics-based models are the primary approach for modeling the phase behavior of block copolymers. However, the successful use of self-consistent field theory (SCFT) for designing new materials relies on the correct chemistry- and temperature-dependent Flory-Huggins interaction parameter <i>χ</i><sub>AB</sub> that quantifies the incompatibility between the two blocks A and B as well as accurate estimation of the ratio of Kuhn lengths (<i>b</i><sub>A</sub>/<i>b</i><sub>B</sub>) and block densities. This work uses machine learning to model the phase behavior of AB diblock copolymers by using the chemical identities of blocks directly, obviating the need for measurement of χ<sub>AB</sub> and <i>b</i><sub>A</sub>/<i>b</i><sub>B</sub>. The random forest approach employed predicts the phase behavior with almost 90% accuracy after training on a data set of 4768 data points, almost twice the accuracy obtained using SCFT employing χ<sub>AB</sub> from group contribution theory. The machine-learning model is notably sensitive toward the uncertainty in measuring molecular parameters; however, its accuracy still remains at least 60% even for highly uncertain experimental measurements. Accuracy is substantially reduced when extrapolating to chemistries outside the training set. This work demonstrates that a random forest phase predictor performs remarkably well in many scenarios, providing an opportunity to predict self-assembly without measurement of molecular parameters. Random forest ; Copolymer ; Block (permutation group theory) ; Phase (matter) ; Set (abstract data type) ; Random phase approximation ; Work (physics) ; Statistical physics ; Computer science ; Training set ; Experimental data ; Materials science ; Algorithm ; Biological system ; Artificial intelligence ; Machine learning ; Thermodynamics ; Chemistry ; Statistics ; Mathematics ; Physics ; Polymer ; Organic chemistry ; Condensed matter physics Block Copolymer Self-Assembly ; Machine Learning in Materials Science ; Advanced Polymer Synthesis and Characterization
Authors: Shenghong Ju | Ryo Yoshida | Chang Liu | Stephen Wu | Kenta Hongo | Terumasa Tadano | Junichiro Shiomi
Source: Physical Review Materials (article, 2021-05-10)
Field: Materials Science — Materials Chemistry

Exploring diamondlike lattice thermal conductivity crystals via feature-based transfer learning Ultrahigh lattice thermal conductivity materials hold great importance since they play a critical role in the thermal management of electronic and optical devices. Models using machine learning can search for materials with outstanding higher-order properties like thermal conductivity. However, the lack of sufficient data to train a model is a serious hurdle. Herein we show that big data can complement small data for accurate predictions when lower-order feature properties available in big data are selected properly and applied to transfer learning. The connection between the crystal information and thermal conductivity is directly built with a neural network by transferring descriptors acquired through a pretrained model for the feature property. Successful transfer learning shows the ability of extrapolative prediction and reveals descriptors for lattice anharmonicity. The resulting model is employed to screen over $60\phantom{\rule{0.16em}{0ex}}000$ compounds to identify novel crystals that can serve as alternatives to diamond. Even though most materials in the top list are superhard materials, we reveal that superhard property does not necessarily lead to high lattice thermal conductivity. Large hardness means high elastic constants and group velocity of phonons in the linear dispersion regime, but the lattice thermal conductivity is determined also by other important factors such as the phonon relaxation time. What is more, the average or maximum dipole polarizability and the van der Waals radius are revealed to be the leading descriptors among those that can also be qualitatively related to anharmonicity. Phonon ; Thermal conductivity ; Polarizability ; Lattice (music) ; Transfer of learning ; Thermal ; Dipole ; Lattice constant Thermal properties of materials ; Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices
Authors: Charles Le Losq | A. P. Valentine | Bjørn O. Mysen | Daniel R. Neuville
Source: Geochimica et Cosmochimica Acta (article, 2021-08-28)
Field: Materials Science — Ceramics and Composites

Structure and properties of alkali aluminosilicate glasses and melts: Insights from deep learning Aluminosilicate glasses and melts are of paramount importance for geo- and materials sciences. They include most magmas, and are used to produce a wide variety of everyday materials, from windows to smartphone displays. Despite this importance, no general model exists with which to predict the atomic structure, thermodynamic and viscous properties of aluminosilicate melts. To address this, we introduce a deep learning framework, ‘i-Melt’, which combines a deep artificial neural network with thermodynamic equations. It is trained to predict 18 different latent and observed properties of melts and glasses in the K2O-Na2O-Al2O3-SiO2 system, including configurational entropy, viscosity, optical refractive index, density, and Raman signals. Viscosity can be predicted in the 100–1015 log10 Pa·s range using five different theoretical frameworks (Adam-Gibbs, Free Volume, MYEGA, VFT, Avramov-Milchev), with a precision equal to, or better than, 0.4 log10 Pa·s on unseen data. Density and optical refractive index (through the Sellmeier equation) can be predicted with errors equal or lower than 0.02 and 0.006, respectively. Raman spectra for K2O-Na2O-Al2O3-SiO2 glasses are also predicted, with a relatively high mean error of ∼25% due to the limited data set available for training. Latent variables can also be predicted with good precisions. For example, the glass transition temperature, Tg, can be predicted to within 19 K, while the melt configurational entropy at the glass transition, Sconf(Tg), can be predicted to within 0.8 J mol−1 K−1. Applied to rhyolite compositions, i-Melt shows that the rheological threshold separating explosive and effusive eruptions correlates with an increase in the fraction of non-bridging oxygens in rhyolite melts as their alkali/Al ratio becomes larger than 1. Exploring further the effect of the K/(K + Na) ratio on the properties of alkali aluminosilicate melts with compositions varying along a simplified alkali magmatic series trend, we observe that K-rich melts have systematically different structures and higher viscosities compared to Na-rich melts. Combined with the effects of the K/(K + Na) ratio on other parameters, such as the solubility, solution mechanisms and speciation of volatile elements, this could ultimately influence the eruptive dynamics of volcanic systems emitting Na-rich or K-rich alkali magmas. Aluminosilicate ; Alkali metal ; Geology ; Mineralogy ; Geochemistry ; Chemistry ; Organic chemistry Glass properties and applications ; Geological and Geochemical Analysis ; Geochemistry and Geologic Mapping
Authors: Silvan Käser | Eric D. Boittier | Meenu Upadhyay | Markus Meuwly
Source: Journal of Chemical Theory and Computation (article, 2021-05-07)
Field: Materials Science — Materials Chemistry

Transfer Learning to CCSD(T): Accurate Anharmonic Frequencies from Machine Learning Models The calculation of the anharmonic modes of small- to medium-sized molecules for assigning experimentally measured frequencies to the corresponding type of molecular motions is computationally challenging at sufficiently high levels of quantum chemical theory. Here, a practical and affordable way to calculate coupled-cluster quality anharmonic frequencies using second-order vibrational perturbation theory (VPT2) from machine-learned models is presented. The approach, referenced as "NN + VPT2", uses a high-dimensional neural network (PhysNet) to learn potential energy surfaces (PESs) at different levels of theory from which harmonic and VPT2 frequencies can be efficiently determined. The NN + VPT2 approach is applied to eight small- to medium-sized molecules (H<sub>2</sub>CO, trans-HONO, HCOOH, CH<sub>3</sub>OH, CH<sub>3</sub>CHO, CH<sub>3</sub>NO<sub>2</sub>, CH<sub>3</sub>COOH, and CH<sub>3</sub>CONH<sub>2</sub>) and frequencies are reported from NN-learned models at the MP2/aug-cc-pVTZ, CCSD(T)/aug-cc-pVTZ, and CCSD(T)-F12/aug-cc-pVTZ-F12 levels of theory. For the largest molecules and at the highest levels of theory, transfer learning (TL) is used to determine the necessary full-dimensional, near-equilibrium PESs. Overall, NN + VPT2 yields anharmonic frequencies to within 20 cm<sup>-1</sup> of experimentally determined frequencies for close to 90% of the modes for the highest quality PES available and to within 10 cm<sup>-1</sup> for more than 60% of the modes. For the MP2 PESs only ∼60% of the NN + VPT2 frequencies were within 20 cm<sup>-1</sup> of the experiment, with outliers up to ∼150 cm<sup>-1</sup>, compared to the experiment. It is also demonstrated that the approach allows to provide correct assignments for strongly interacting modes such as the OH bending and the OH torsional modes in formic acid monomer and the CO-stretch and OH-bend mode in acetic acid. Anharmonicity ; Transfer of learning ; Computer science ; Transfer (computing) ; Machine learning ; Artificial intelligence ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Topic Modeling ; Nuclear Materials and Properties
Authors: Byeong-Hyo Cho | Shigenobu Koseki
Source: Scientia Horticulturae (article, 2021-07-05)
Field: Chemistry — Analytical Chemistry

Determination of banana quality indices during the ripening process at different temperatures using smartphone images and an artificial neural network Ripening ; Hue ; Mean squared error ; Mathematics ; Pulp (tooth) ; RGB color model ; Coefficient of determination ; Artificial neural network ; Artificial intelligence ; Horticulture ; Chemistry ; Statistics ; Food science ; Computer science ; Biology Spectroscopy and Chemometric Analyses ; Banana Cultivation and Research ; Smart Agriculture and AI