jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Zilin Song | Hong-Yu Zhou | Hao Tian | Xinlei Wang | Peng Tao
Source: Communications Chemistry (article, 2020-10-08)
Field: Materials Science — Materials Chemistry

Unraveling the energetic significance of chemical events in enzyme catalysis via machine-learning based regression approach The bacterial enzyme class of β-lactamases are involved in benzylpenicillin acylation reactions, which are currently being revisited using hybrid quantum mechanical molecular mechanical (QM/MM) chain-of-states pathway optimizations. Minimum energy pathways are sampled by reoptimizing pathway geometry under different representative protein environments obtained through constrained molecular dynamics simulations. Predictive potential energy surface models in the reaction space are trained with machine-learning regression techniques. Herein, using TEM-1/benzylpenicillin acylation reaction as the model system, we introduce two model-independent criteria for delineating the energetic contributions and correlations in the predicted reaction space. Both methods are demonstrated to effectively quantify the energetic contribution of each chemical process and identify the rate limiting step of enzymatic reaction with high degrees of freedom. The consistency of the current workflow is tested under seven levels of quantum chemistry theory and three non-linear machine-learning regression models. The proposed approaches are validated to provide qualitative compliance with experimental mutagenesis studies. Regression ; Regression analysis ; Catalysis ; Machine learning ; Artificial intelligence ; Chemistry ; Computer science ; Biochemistry ; Mathematics ; Statistics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Zhongyu Wan | Quan‐De Wang | Jinhu Liang
Source: International Journal of Quantum Chemistry (article, 2020-08-29)
Field: Chemistry — Organic Chemistry

Accurate prediction of standard enthalpy of formation based on semiempirical quantum chemistry methods with artificial neural network and molecular descriptors Abstract This work investigates possible improvements in the accuracy of semiempirical quantum chemistry (SQC) methods for the prediction of standard enthalpy of formation ( Δ f H o ) through the use of an artificial neural network (ANN) with molecular descriptors. A total of 142 organic compounds with enough structural diversity has been considered in the training set. Standard enthalpy of formation for the selected compounds at the semiempirical PM3 and PM6 quantum chemistry methods is collected from literature and is calculated by using the semiempirical PM7 method in this work. The multiple stepwise regression is first used to screen effective molecular descriptors, which are highly correlated with the error terms of the standard enthalpy of formation compared with experimental values. The obtained seven effective molecular descriptors are then used as input set to establish three 7‐11‐1 neural network‐based correction models to improve the accuracy of SQC methods. By using the developed correction models, the mean absolute errors for Δ f H o of PM3, PM6, and PM7 methods are reduced from 22.36, 18.60, and 17.27 to 9.86, 9.83, and 8.95, respectively, in kJ/mol. Meanwhile, the results of the test set show that the neural network does not have the problem of overfitting. Detailed analysis of the seven effective molecular descriptors indicates that the major source of the correction models is the electron‐withdrawing effect. The developed ANN models for the three selected SQC methods provide an efficient method for the quick and accurate prediction of thermodynamic properties. Overfitting ; Artificial neural network ; Chemistry ; Enthalpy ; Standard enthalpy of formation ; Molecular descriptor ; Thermodynamics ; Biological system ; Quantitative structure–activity relationship ; Artificial intelligence ; Computer science ; Physical chemistry ; Physics ; Stereochemistry Chemical Thermodynamics and Molecular Structure ; Computational Drug Discovery Methods ; Thermal and Kinetic Analysis
Authors: Dhvaneel Visaria | Ankit Jain
Source: Applied Physics Letters (article, 2020-11-16)
Field: Materials Science — Materials Chemistry

Machine-learning-assisted space-transformation accelerates discovery of high thermal conductivity alloys We study the thermal conductivity distribution of hypothetical graphene-like materials composed of carbon and heavy carbon atoms. These materials are representative of alloys and disordered materials, which are relatively unexplored for thermal properties owing to their large configuration spaces. Since the full thermal conductivity calculations using the Boltzmann transport equation based solutions are computationally prohibitive for each of the 232 considered configurations, we employ regularized autoencoders, a class of generative machine learning models that transform the configuration space to the latent space in which materials are clustered according to the target property. Such conditioning allows selective sampling of high thermal conductivity materials from the latent space. We find that the model is able to learn the underlying thermal transport physics of the system under study and is able to predict superlattice-like configurations with high thermal conductivity despite their higher mass. Thermal conductivity ; Boltzmann equation ; Graphene ; Materials science ; Superlattice ; Space (punctuation) ; Thermal ; Carbon fibers ; Transformation (genetics) ; Statistical physics ; Thermodynamics ; Physics ; Computer science ; Nanotechnology ; Chemistry ; Composite material ; Optoelectronics ; Composite number Thermal properties of materials ; Machine Learning in Materials Science ; Model Reduction and Neural Networks
Authors: P. Nguyen | Brandon Hong | Shimon Rubin | Yeshaiahu Fainman
Source: Biomedical Optics Express (article, 2020-07-29)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine learning for composition analysis of ssDNA using chemical enhancement in SERS Surface-enhanced Raman spectroscopy (SERS) is an attractive method for bio-chemical sensing due to its potential for single molecule sensitivity and the prospect of DNA composition analysis. In this manuscript we leverage metal specific chemical enhancement effect to detect differences in SERS spectra of 200-base length single-stranded DNA (ssDNA) molecules adsorbed on gold or silver nanorod substrates, and then develop and train a linear regression as well as neural network models to predict the composition of ssDNA. Our results indicate that employing substrates of different metals that host a given adsorbed molecule leads to distinct SERS spectra, allowing to probe metal-molecule interactions under distinct chemical enhancement regimes. Leveraging this difference and combining spectra from different metals as an input for PCA (Principal Component Analysis) and NN (Neural Network) models, allows to significantly lower the detection errors compared to manual feature-choosing analysis as well as compared to the case where data from single metal is used. Furthermore, we show that NN model provides superior performance in the presence of complex noise and data dispersion factors that affect SERS signals collected from metal substrates fabricated on different days. Principal component analysis ; Molecule ; Biological system ; Raman spectroscopy ; Raman scattering ; Materials science ; Surface-enhanced Raman spectroscopy ; Analyte ; Metal ; Adsorption ; Nanotechnology ; Spectroscopy ; Analytical Chemistry (journal) ; Chemistry ; Computer science ; Optics ; Artificial intelligence ; Physics ; Physical chemistry ; Chromatography Gold and Silver Nanoparticles Synthesis and Applications ; Advanced biosensing and bioanalysis techniques ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Beatriz G. del Rio | Christopher Kuenneth | Tran Doan Huan | Rampi Ramprasad
Source: The Journal of Physical Chemistry A (article, 2020-11-03)
Field: Materials Science — Materials Chemistry

An Efficient Deep Learning Scheme To Predict the Electronic Structure of Materials and Molecules: The Example of Graphene-Derived Allotropes Computations based on density functional theory (DFT) are transforming various aspects of materials research and discovery. However, the effort required to solve the central equation of DFT, namely the Kohn-Sham equation, which remains a major obstacle for studying large systems with hundreds of atoms in a practical amount of time with routine computational resources. Here, we propose a deep learning architecture that systematically learns the input-output behavior of the Kohn-Sham equation and predicts the electronic density of states, a primary output of DFT calculations, with unprecedented speed and chemical accuracy. The algorithm also adapts and progressively improves in predictive power and versatility as it is exposed to new diverse atomic configurations. We demonstrate this capability for a diverse set of carbon allotropes spanning a large configurational and phase space. The electronic density of states, along with the electronic charge density, may be used downstream to predict a variety of materials properties, bypassing the Kohn-Sham equation, leading to an ultrafast and high-fidelity DFT emulator. Density functional theory ; Kohn–Sham equations ; Graphene ; Fidelity ; Computer science ; Computation ; High fidelity ; Electronic structure ; Materials science ; Statistical physics ; Nanotechnology ; Computational science ; Physics ; Algorithm ; Quantum mechanics Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Electronic and Structural Properties of Oxides
Authors: Kjell Jorner | Tore Brinck | Per‐Ola Norrby | David Buttar
Source: ChemRxiv (preprint, 2020-08-05)
Field: Materials Science — Materials Chemistry

Machine Learning Meets Mechanistic Modelling for Accurate Prediction of Experimental Activation Energies Accurate prediction of chemical reactions in solution is challenging for current state-of-the-art approaches based on transition state modelling with density functional theory. Models based on machine learning have emerged as a promising alternative to address these problems, but these models currently lack the precision to give crucial information on the magnitude of barrier heights, influence of solvents and catalysts and extent of regio- and chemoselectivity. Here, we construct hybrid models which combine the traditional transition state modelling and machine learning to accurately predict reaction barriers. We train a Gaussian Process Regression model to reproduce high-quality experimental kinetic data for the nucleophilic aromatic substitution reaction and use it to predict barriers with a mean absolute error of 0.77 kcal/mol for an external test set. The model was further validated on regio- and chemoselectivity prediction on patent reaction data and achieved a competitive top-1 accuracy of 86%, despite not being trained explicitly for this task. Importantly, the model gives error bars for its predictions that can be used for risk assessment by the end user. Hybrid models emerge as the preferred alternative for accurate reaction prediction in the very common low-data situation where only 100–150 rate constants are available for a reaction class. With recent advances in deep learning for quickly predicting barriers and transition state geometries from density functional theory, we envision that hybrid models will soon become a standard alternative to complement current machine learning approaches based on ground-state physical organic descriptors or structural information such as molecular graphs or fingerprints. Chemoselectivity ; Computer science ; Machine learning ; Artificial intelligence ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemistry and Chemical Engineering
Authors: Josh Payne | M. Sammer Srouji | Dian Ang Yap | Vineet Kosaraju
Source: arXiv (Cornell University) (preprint, 2020-07-11)
Field: Materials Science — Materials Chemistry

BERT Learns (and Teaches) Chemistry Modern computational organic chemistry is becoming increasingly data-driven. There remain a large number of important unsolved problems in this area such as product prediction given reactants, drug discovery, and metric-optimized molecule synthesis, but efforts to solve these problems using machine learning have also increased in recent years. In this work, we propose the use of attention to study functional groups and other property-impacting molecular substructures from a data-driven perspective, using a transformer-based model (BERT) on datasets of string representations of molecules and analyzing the behavior of its attention heads. We then apply the representations of functional groups and atoms learned by the model to tackle problems of toxicity, solubility, drug-likeness, and synthesis accessibility on smaller datasets using the learned representations as features for graph convolution and attention models on the graph structure of molecules, as well as fine-tuning of BERT. Finally, we propose the use of attention visualization as a helpful tool for chemistry practitioners and students to quickly identify important substructures in various chemical properties. Computer science ; Graph ; Artificial intelligence ; Theoretical computer science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Yi Zhang | Aize Li | Binghui Deng | Kelleen K. Hughes
Source: npj Materials Degradation (article, 2020-05-26)
Field: Materials Science — Materials Chemistry

Data-driven predictive models for chemical durability of oxide glass under different chemical conditions Abstract We conducted a comprehensive study to investigate the performance of various machine-learning models in predicting the chemical durability of oxide glasses under different chemical conditions with glass composition as input features, by taking advantage of the large dataset (~1400 datapoints) we have collected. Two typical machine-learning tasks, weight loss regression, and surface appearance change rating classification, were conducted in the study. We successfully made Neural Networks delivered an excellent performance in predicting the weight loss, while Random Forest in classifying the surface appearance change rating. Additionally, feature importance analysis showed that SiO 2 , Na 2 O, P 2 O 5 were the most dominate features for predicting the weight loss, while SiO 2 , ZrO 2 , CaO were the topmost features for classifying the surface appearance change rating, under acid, HF, and base testing conditions, respectively. We also realized that the trained models fall short of extrapolating data far from the training dataset space even though they exhibit outstanding interpolation performance in some cases. Topology constrained theory fed by structural information from molecular dynamics simulations seems to be a promising approach to address the challenge. Durability ; Machine learning ; Regression ; Artificial intelligence ; Artificial neural network ; Interpolation (computer graphics) ; Feature (linguistics) ; Computer science ; Weight change ; Surface (topology) ; Oxide ; Random forest ; Base (topology) ; Materials science ; Weight loss ; Mathematics ; Statistics ; Database ; Motion (physics) Catalytic Processes in Materials Science ; Pigment Synthesis and Properties ; Machine Learning in Materials Science
Authors: Fuzhu Liu | Shengchun Yang | Andrew J. Medford
Source: ChemCatChem (article, 2020-05-20)
Field: Materials Science — Materials Chemistry

Scalable approach to high coverages on oxides via iterative training of a machine‐learning algorithm Abstract Understanding the interaction of multiple types of adsorbate molecules on solid surfaces is crucial to establishing the stability of catalysts under various chemical environments. Computational studies on the high coverage and mixed coverages of reaction intermediates are still challenging, especially for transition‐metal compounds. In this work, we present a framework to predict differential adsorption energies and identify low‐energy structures under high‐ and mixed‐adsorbate coverages on oxide materials. The approach uses Gaussian process machine‐learning models with quantified uncertainty in conjunction with an iterative training algorithm to actively identify the training set. The framework is demonstrated for the mixed adsorption of CH x , NH x and OH x species on the oxygen vacancy and pristine rutile TiO 2 (110) surface sites. The results indicate that the proposed algorithm is highly efficient at identifying the most valuable training data, and is able to predict differential adsorption energies with a mean absolute error of ∼0.3 eV based on &lt;25 % of the total DFT data. The algorithm is also used to identify 76 % of the low‐energy structures based on &lt;30 % of the total DFT data, enabling construction of surface phase diagrams that account for high and mixed coverage as a function of the chemical potential of C, H, O, and N. Furthermore, the computational scaling indicates the algorithm scales nearly linearly ( N 1.12 ) as the number of adsorbates increases. This framework can be directly extended to metals, metal oxides, and other materials, providing a practical route toward the investigation of the behavior of catalysts under high‐coverage conditions. Scalability ; Adsorption ; Algorithm ; Materials science ; Scaling ; Stability (learning theory) ; Computer science ; Molecule ; Transition metal ; Chemistry ; Catalysis ; Physical chemistry ; Machine learning ; Mathematics Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Advanced Photocatalysis Techniques
Authors: Zhaohan Zhang | Mu Li | Katharine Flores | Rohan Mishra
Source: Journal of Applied Physics (article, 2020-09-08)
Field: Materials Science — Materials Chemistry

Machine learning formation enthalpies of intermetallics Developing fast and accurate methods to discover intermetallic compounds is relevant for alloy design. While density-functional-theory (DFT)-based methods have accelerated design of binary and ternary alloys by providing rapid access to the energy and properties of the stable intermetallics, they are not amenable for rapidly screening the vast combinatorial space of multi-principal element alloys (MPEAs). Here, a machine-learning model is presented for predicting the formation enthalpy of binary intermetallics and is used to identify new ones. The model uses easily accessible elemental properties as descriptors and has a mean absolute error of 0.025 eV/atom in predicting the formation enthalpy of stable binary intermetallics reported in the Materials Project database. The model further predicts stable intermetallics to form in 112 binary alloy systems that do not have any stable intermetallics reported in the Materials Project database. DFT calculations confirm one such stable intermetallic identified by the model, NbV2, to be on the convex hull. Furthermore, an adaptive transfer learning method is used to generalize the model to predict ternary intermetallics with a similar accuracy as DFT, which suggests that it could be extended to identify compositionally complex intermetallics that may form in MPEAs. Intermetallic ; Ternary operation ; Enthalpy ; Binary number ; Alloy Machine Learning in Materials Science ; Heusler alloys: electronic and magnetic properties ; Rare-earth and actinide compounds
Authors: Youyong Li | Zhili Li | Quanying Ma | Shiyang He | Qidong Zhang | Feng Wang | Guoqiang Wu | Qingyi Li | Pengfei Luo | Jiye Zhang | Jun Luo
Source: Research (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

High Thermoelectric Performance of Cu-Doped PbSe-PbS System Enabled by High-Throughput Experimental Screening Recent advances in high-throughput (HTP) computational power and machine learning have led to great achievements in exploration of new thermoelectric materials. However, experimental discovery and optimization of thermoelectric materials have long relied on the traditional Edisonian trial and error approach. Herein, we demonstrate that ultrahigh thermoelectric performance in a Cu-doped PbSe-PbS system can be realized by HTP experimental screening and precise property modulation. Combining the HTP experimental technique with transport model analysis, an optimal Se/S ratio showing high thermoelectric performance has been efficiently screened out. Subsequently, based on the screened Se/S ratio, the doping content of Cu has been subtly adjusted to reach the optimum carrier concentration. As a result, an outstanding peak zT~1.6 is achieved at 873 K for a 1.8 at% Cu-doped PbSe<sub>0.6</sub>S<sub>0.4</sub> sample, which is the superior value among the <i>n</i>-type Te-free lead chalcogenides. We anticipate that current work will stimulate large-scale unitization of the HTP experimental technique in the thermoelectric field, which can greatly accelerate the research and development of new high-performance thermoelectric materials. Throughput ; Thermoelectric effect ; Doping ; Materials science ; Optoelectronics ; Nanotechnology ; Engineering physics ; Computer science ; Telecommunications ; Engineering ; Physics ; Wireless Advanced Thermoelectric Materials and Devices ; Chalcogenide Semiconductor Thin Films ; Advanced Thermodynamics and Statistical Mechanics
Authors: Mohamadreza Shariati | Wolfgang E. J. Weber | Jan Bohlen | Gerrit Kurz | Dietmar Letzig | Daniel Höche
Source: Materials Science and Engineering A (article, 2020-07-17)
Field: Materials Science — Biomaterials

Enabling intelligent Mg-sheet processing utilizing efficient machine-learning algorithm Context (archaeology) ; Algorithm ; Computer science ; Interpolation (computer graphics) ; Property (philosophy) ; Process (computing) ; Set (abstract data type) ; Machine learning ; Interdependence ; Artificial intelligence ; Deformation (meteorology) ; Data mining ; Data processing ; Sensitivity (control systems) ; Engineering ; Database ; Materials science ; Image (mathematics) Magnesium Alloys: Properties and Applications ; Aluminum Alloys Composites Properties ; Aluminum Alloy Microstructure Properties
Authors: Kyoungmin Min | Eunseog Cho
Source: The Journal of Physical Chemistry C (article, 2020-06-28)
Field: Materials Science — Materials Chemistry

Accelerated Discovery of Novel Inorganic Materials with Desired Properties Using Active Learning Construction of prediction models using machine learning algorithms on existing databases expands the search limit of undiscovered structures, in principle, to the entire materials space. However, because of uncertainties in machine learning prediction, the suggested properties are not always promising; thus, improving the database quality is mandatory for validation as well as improvement in prediction accuracy. To achieve this, we herein implement an active learning process, beginning with a limited number of databases, to find materials satisfying target properties (band gap and refractive index) with minimized trials and errors. The regression model is initially trained with only around 2% of the entire search space, and 20 new databases, suggested from the optimization schemes, are added at each optimization process. Between exploration, exploitation, random selection, and the Bayesian optimization method, the Bayesian method exhibits the best performance in finding the number of materials that satisfies the criteria within limited trials In addition, the structure with the maximum target property values is found after searching only around 7.0% and 7.7% of the entire database for band gap and refractive index, respectively. Current results clearly confirm that the active learning process can be accelerated to find ideal materials satisfying target properties with minimized resources. Bayesian optimization ; Computer science ; Property (philosophy) ; Machine learning ; Process (computing) ; Artificial intelligence ; Active learning (machine learning) ; Limit (mathematics) ; Bayesian probability ; Data mining ; Space (punctuation) ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography