jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ergün Şimşek
Source: Machine Learning Science and Technology (article, 2020-02-25)
Field: Materials Science — Materials Chemistry

Determining optical constants of 2D materials with neural networks from multi-angle reflectometry data Abstract Synthetically generated multi-angle reflectometry data is used to train a neural network based learning system to estimate the refractive index of atomically thin layered materials in the visible part of the electromagnetic spectrum. Unlike previously developed regression based optical characterization methods, the prediction is achieved via classification by using the probabilities of each input element belonging to a label as weighting coefficients in a simple analytical formula. Various types of activation functions and gradient descent optimizers are tested to determine the optimum combination yielding the best performance. For the verification of the proposed method’s accuracy, four different materials are studied. In all cases, the maximum error is calculated to be less than 0.3%. Considering the highly dispersive nature of the studied materials, this result is a substantial improvement in terms of accuracy and efficiency compared to traditional approaches. Reflectometry ; Weighting ; Artificial neural network ; Simple (philosophy) ; Refractive index ; Characterization (materials science) ; Computer science ; Gradient descent ; Biological system ; Algorithm ; Regression ; Materials science ; Optics ; Mathematics ; Artificial intelligence ; Statistics ; Physics ; Acoustics ; Optoelectronics ; Time domain 2D Materials and Applications ; Graphene research and applications ; Photonic and Optical Devices
Authors: Chaoyi Chang | Andrew J. Medford
Source: The Journal of Chemical Physics (article, 2020-07-28)
Field: Materials Science — Materials Chemistry

Classification of biomass reactions and predictions of reaction energies through machine learning Elementary steps and intermediate species of linearly structured biomass compounds are studied. Specifically, possible intermediates and elementary reactions of 15 key biomass compounds and 33 small molecules are obtained from a recursive bond-breaking algorithm. These are used as inputs to the unsupervised Mol2Vec algorithm to generate vector representations of all intermediates and elementary reactions. The vector descriptors are used to identify sub-classes of elementary steps, and linear discriminant analysis is used to accurately identify the reaction type and reduce the dimension of the vectors. The resulting descriptors are applied to predict gas-phase reaction energies using linear regression with accuracies that exceed the commonly employed group additivity approach. They are also applied to quantitatively assess model compound similarity, and the results are consistent with chemical intuition. This workflow for creating vector representations of complex molecular systems requires no input from electronic structure calculations, and it is expected to be applicable to other similar systems where vector representations are needed. Linear discriminant analysis ; Support vector machine ; Elementary reaction ; Intuition ; Molecule ; Additive function ; Biological system ; Chemistry ; Computer science ; Mathematics ; Pattern recognition (psychology) ; Artificial intelligence ; Physics ; Kinetics ; Organic chemistry ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Process Optimization and Integration
Authors: Upendra Kumar | Sanjay Nayak | Soubhik Chakrabarty | Satadeep Bhattacharjee | Seung-Cheol Lee
Source: Journal of Materials Science (article, 2020-05-07)
Field: Materials Science — Materials Chemistry

Gallium–Boron–Phosphide ($$\hbox {GaBP}_{2}$$): a new III–V semiconductor for photovoltaics Band gap ; Photovoltaics ; Semiconductor ; Wide-bandgap semiconductor ; Scaling ; Ab initio ; Electronic band structure ; Intrinsic semiconductor ; Crystal (programming language) Machine Learning in Materials Science ; Chemical and Physical Properties of Materials ; Ga2O3 and related materials
Authors: Nemilyn A. Fadchar | Jennifer C. Dela Cruz
Source:  (article, 2020-04-01)
Field: Chemistry — Inorganic Chemistry

Design and Development of a Neural Network — Based Coconut Maturity Detector Using Sound Signatures Revolutionizing the post-harvest process using low-cost non-destructive approaches such as acoustics is a promising frontier to uplift the conventional farming practices as well as adapt machine learning tools to further increase the accuracy of the system. This study attempts to develop a prototype that uses the sound signatures to classify the maturity level of the young coconut by using neural network. Specifically, it aims to: (1) extract the acoustic features from the sound signal; (2) train the data set and develop the classification model using neural network; (3) develop a program for the prototype; (4) design and fabricate the hardware for the prototype; and (5) test and evaluate the protype. The major parts of the prototype were the vibration motor, vibration sensor, rpi 3b+ microcontroller, battery and LCD. Results of the neural network model obtained an overall classification accuracy of 91.3 % and an R-value of 0.94229. This implied that the neural network model has a high prediction rate to accurately determine the maturity level of the coconut using the sound signatures. Lastly, final evaluation results showed that the prototype has a higher percentage of prediction accuracy as compared to the manual process. Artificial neural network ; Computer science ; Process (computing) ; Microcontroller ; Detector ; Benchmark (surveying) ; Set (abstract data type) ; Artificial intelligence ; Pattern recognition (psychology) ; Engineering ; Computer hardware ; Telecommunications Coconut Research and Applications ; Smart Agriculture and AI ; Advanced Chemical Sensor Technologies
Authors: Nicolae C. Iovanac | Brett M. Savoie
Source: Machine Learning Science and Technology (article, 2020-08-12)
Field: Materials Science — Materials Chemistry

Improving the generative performance of chemical autoencoders through transfer learning Abstract Generative models are a sub-class of machine learning models that are capable of generating new samples with a target set of properties. In chemical and materials applications, these new samples might be drug targets, novel semiconductors, or catalysts constrained to exhibit an application-specific set of properties. Given their potential to yield high-value targets from otherwise intractable design spaces, generative models are currently under intense study with respect to how predictions can be improved through changes in model architecture and data representation. Here we explore the potential of multi-task transfer learning as a complementary approach to improving the validity and property specificity of molecules generated by such models. We have compared baseline generative models trained on a single property prediction task against models trained on additional ancillary prediction tasks and observe a generic positive impact on the validity and specificity of the multi-task models. In particular, we observe that the validity of generated structures is strongly affected by whether or not the models have chemical property data, as opposed to only syntactic structural data, supplied during learning. We demonstrate this effect in both interpolative and extrapolative scenarios (i.e., where the generative targets are poorly represented in training data) for models trained to generate high energy structures and models trained to generated structures with targeted bandgaps within certain ranges. In both instances, the inclusion of additional chemical property data improves the ability of models to generate valid, unique structures with increased property specificity. This approach requires only minor alterations to existing generative models, in many cases leveraging prediction frameworks already native to these models. Additionally, the transfer learning strategy is complementary to ongoing efforts to improve model architectures and data representation and can foreseeably be stacked on top of these developments. Property (philosophy) ; Computer science ; Machine learning ; Artificial intelligence ; Generative grammar ; Transfer of learning ; Generative model ; Task (project management) ; Set (abstract data type) ; Representation (politics) ; Class (philosophy) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Ghanshyam Pilania | Prasanna V. Balachandran | J. E. Gubernatis | Turab Lookman
Source: Synthesis lectures on materials and optics (article, 2020-03-06)
Field: Materials Science — Materials Chemistry

Data-Based Methods for Materials Design and Discovery: Basic Ideas and General Methods Property (philosophy) ; Focus (optics) ; Fidelity ; Probabilistic logic ; Computer science ; Field (mathematics) ; Data science ; Management science ; Artificial intelligence ; Engineering ; Mathematics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Yanqiang Han | Zhilong Wang | Jinjin Li
Source: The Journal of Physical Chemistry Letters (article, 2020-12-14)
Field: Materials Science — Materials Chemistry

Neural Networks Accelerate the <i>Ab Initio</i> Prediction of Solid–Solid Phase Transitions at High Pressures High-level <i>ab initio</i> chemical calculations, such as second-order Møller-Plesset perturbation (MP2), are highly accurate but time-consuming, making it inefficient to apply to macromolecular systems. Here, we propose a newly efficient approach based on the neural network and fragment method to predict the Gibbs free energy, structural characteristics, and thus phase transition of solid crystal structures. The proposed approach has the same prediction accuracy as the MP2 calculation but is hundreds of times faster than the MP2. The predicted structures and phase transitions of two selected ice phases (IX and XV) under extreme conditions are in excellent agreement with the MP2 calculations and experimental results but with an extremely low computational cost. It not only predicts the high-pressure structures and phase diagrams of solid systems accurately and efficiently but also solves the problem of extreme calculation cost during a high-precision theoretical study on high-pressure molecular crystals with potentially essential applications. Ab initio ; Crystal structure prediction ; Phase transition ; Artificial neural network ; Perturbation theory (quantum mechanics) ; Statistical physics ; Computer science ; Perturbation (astronomy) ; Phase (matter) ; Phase diagram ; Materials science ; Crystal structure ; Thermodynamics ; Physics ; Chemistry ; Machine learning ; Crystallography ; Quantum mechanics Machine Learning in Materials Science ; Crystallography and molecular interactions ; Advanced Chemical Physics Studies
Authors: Xiaotong Liu | Tianfu Zhang | Tao Yang | Xiulei Liu | Xin Song | Yong Yang | Ning Li | Gian‐Marco Rignanese | Yongwang Li | Xiaodong Wen
Source: The Journal of Physical Chemistry A (article, 2020-10-13)
Field: Materials Science — Materials Chemistry

Solving Chemistry Problems via an End-to-End Approach: A Proof of Concept Traditionally, chemistry problems are solved by means of a deductive approach. The question to be addressed is typically related to the value of a property that is either measured experimentally, computed using quantum-chemistry software, or (more recently) predicted using a machine-learned model. In this paper, we demonstrate that an inductive approach can be adopted using End-to-End (E2E) machine learning. This approach is illustrated for tackling the following chemistry problems: (i) determine the fully coordinated (FC) and undercoordinated (UC) atoms in a molecule with one missing atom, (ii) identify the type of atom that is missing in such an incomplete molecule, and (iii) predict the direction of a reaction between two molecules according to an existing dataset. The E2E approach leads to accuracies higher than 99%, 98%, and 93% for these three problems, respectively. Finally, in order to achieve such accuracies, a descriptor for the molecules, called bag of clusters, is introduced and compared with a series previously proposed descriptors, highlighting a series of advantages. Quantum chemistry ; Series (stratigraphy) ; Atom (system on chip) ; Molecule ; Property (philosophy) ; Computer science ; Quantum ; Software ; Algorithm ; Chemistry ; Computational chemistry ; Theoretical computer science ; Quantum mechanics ; Physics ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Sumit Kumar | P. Sudhakar Rao | Deepam Goyal | Shankar Sehgal
Source: Metal Powder Report (article, 2020-07-01)
Field: Materials Science — Materials Chemistry

Process modeling for machining Inconel 825 using cryogenically treated carbide insert The ubiquity of artificial intelligence in the manufacturing domain draws inspiration for the present article. The implementation of a neural network technique is still a difficult and time-consuming effort for the industry. Prediction of machining variables is a considerable issue that needs to be explored for preventive maintenance of the machine structure and to optimize the surface quality. This work aims at predicting response parameters of the dry turning process for Inconel 825 alloy using deep-cryogenic treated tungsten-carbide insert through artificial neural network technique. Process parameters considered in this work were cutting speed, feed and depth of cut, whereas, surface-roughness, tool-wear, and material-removal-rate were taken as the three response parameters.14 types of training functions were compared based upon their error indices searching for the training function which best suits this work. Artificial Neural Network (ANN) model was developed by taking Bayesian regularization back propagation based training function. The response values predicted by the ANN were in very close approximation to the actual experimental value with the mean square error of only 0.0011 μm2, 39.0882 μm2 and 0.0520 cm6/min2in the prediction of surface-roughness, tool-wear, and material-removal-rate of dry turning process of Inconel 825 using treated carbide tool. Inconel ; Artificial neural network ; Machining ; Surface roughness ; Tungsten carbide ; Materials science ; Mechanical engineering ; Mean squared error ; Surface finish ; Carbide ; Computer science ; Process (computing) ; Machine learning ; Composite material ; Engineering ; Metallurgy ; Alloy ; Mathematics ; Statistics Metal Alloys Wear and Properties ; Advanced machining processes and optimization ; Advanced materials and composites
Authors: Alberto Acevedo | Michael J. Curry | Shantanu H. Joshi | Brett Leroux | Nicholas Malaya
Source:  (article, 2020-11-01)
Field: Materials Science — Materials Chemistry

Vandermonde Wave Function Ansatz for Improved Variational Monte Carlo Solutions to the Schrodinger equation can be used to predict the electronic structure of molecules and materials and therefore infer their complex physical and chemical properties. Variational Quantum Monte Carlo (VMC) is a technique that can be used to solve the weak form of the Schrodinger equation. Applying VMC to systems with N electrons involves evaluating the determinant of an N by N matrix. The evaluation of this determinant scales as O(N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> ) and is the main computational cost in the VMC process. In this work, we investigate an alternative VMC technique based on the Vandermonde determinant. The Vandermonde determinant is a product of pairwise differences and so evaluating it scales as O(N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ). Therefore, this approach reduces the computational cost by a factor of N. The Vandermonde determinant was implemented in PyTorch and the performance was assessed in approximating the ground state energy of various quantum systems against existing techniques. The performance is evaluated in a variety of systems, starting with the one-dimensional particle in a box, and then considering more complicated atomic systems with multiple particles. The Vandermonde determinant was also implemented in PauliNet, a deep-learning architecture for VMC. The new method is shown to be computationally efficient, and results in a speed-up as large as 5X. In these cases, the new ansatz obtains a reasonable approximation for wavefunctions of atomic systems, but does not reach the accuracy of the Hartree-Fock method that relies on the Slater determinant. It is observed that while the use of neural networks in VMC can result in highly accurate solutions, further work is necessary to determine an appropriate balance between computational time and accuracy. Vandermonde matrix ; Monte Carlo method ; Ansatz ; Wave function ; Schrödinger equation ; Quantum Monte Carlo ; Applied mathematics ; Mathematics ; Computer science ; Quantum mechanics ; Physics ; Mathematical analysis Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Catalysis and Oxidation Reactions
Authors: R. Matthias Geilhufe | Bart Olsthoorn
Source: Physical review. B./Physical review. B (article, 2020-11-30)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Identification of strongly interacting organic semimetals Dirac and Weyl point- and line-node semimetals are characterized by a zero band gap with simultaneously vanishing density of states. Given a sufficient interaction strength, such materials can undergo an interaction instability, e.g., into an excitonic insulator phase. Due to generically flatbands, organic crystals represent a promising materials class in this regard. We combine machine learning, density functional theory, and effective models to identify specific example materials. Without taking into account the effect of many-body interactions, we found the organic charge transfer salts [bis(3,4-diiodo-3\ensuremath{'},4\ensuremath{'}-ethyleneditio- tetrathiafulvalene), 2,3-dichloro-5,6-dicyanobenzoquinone, acetenitrile] $[{(\mathrm{EDT}\text{\ensuremath{-}}\mathrm{TTF}\text{\ensuremath{-}}{\mathrm{I}}_{2})}_{2}](\mathrm{DDQ})\ifmmode\cdot\else\textperiodcentered\fi{}({\mathrm{CH}}_{3}\mathrm{CN})$ and $2,{2}^{\ensuremath{'}},5,{5}^{\ensuremath{'}}\text{\ensuremath{-}}\mathrm{tetraselenafulvalene}--7,7,8,8\text{\ensuremath{-}}\mathrm{tetracyano}\text{\ensuremath{-}}p\text{\ensuremath{-}}\mathrm{quinodimethane}$ (TSeF-TCNQ) and a bis-1,2,3-dithiazolyl radical conductor to exhibit a semimetallic phase in our ab initio calculations. Adding the effect of strong particle-hole interactions for ${(\mathrm{EDT}\text{\ensuremath{-}}\mathrm{TTF}\text{\ensuremath{-}}{\mathrm{I}}_{2})}_{2}(\mathrm{DDQ})\ifmmode\cdot\else\textperiodcentered\fi{}({\mathrm{CH}}_{3}\mathrm{CN})$ and TSeF-TCNQ opens an excitonic gap on the order of 60 and 100 meV, which is in good agreement with previous experiments on these materials. Physics ; Condensed matter physics ; Charge (physics) ; Order (exchange) ; Crystallography ; Quantum mechanics ; Chemistry Organic and Molecular Conductors Research ; Topological Materials and Phenomena ; Machine Learning in Materials Science
Authors: Natalia Sizochenko | Markus Hofmann
Source: Molecules (article, 2020-12-22)
Field: Materials Science — Materials Chemistry

Predictive Modeling of Critical Temperatures in Superconducting Materials In this study, we have investigated quantitative relationships between critical temperatures of superconductive inorganic materials and the basic physicochemical attributes of these materials (also called quantitative structure-property relationships). We demonstrated that one of the most recent studies (titled "A data-driven statistical model for predicting the critical temperature of a superconductor" and published in Computational Materials Science by K. Hamidieh in 2018) reports on models that were based on the dataset that contains 27% of duplicate entries. We aimed to deliver stable models for a properly cleaned dataset using the same modeling techniques (multiple linear regression, MLR, and gradient boosting decision trees, XGBoost). The predictive ability of our best XGBoost model (R2 = 0.924, RMSE = 9.336 using 10-fold cross-validation) is comparable to the XGBoost model by the author of the initial dataset (R2 = 0.920 and RMSE = 9.5 K in ten-fold cross-validation). At the same time, our best model is based on less sophisticated parameters, which allows one to make more accurate interpretations while maintaining a generalizable model. In particular, we found that the highest relative influence is attributed to variables that represent the thermal conductivity of materials. In addition to MLR and XGBoost, we explored the potential of other machine learning techniques (NN, neural networks and RF, random forests). Random forest ; Gradient boosting ; Linear regression ; Mean squared error ; Artificial neural network ; Boosting (machine learning) ; Support vector machine ; Predictive modelling ; Decision tree ; Machine learning ; Computer science ; Artificial intelligence ; Regression analysis ; Linear model ; Regression ; Ensemble forecasting ; Data mining ; Statistics ; Mathematics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Nuclear Materials and Properties
Authors: Sayak Subhra Panda | Kirill Shmilovich | Andrew L. Ferguson | John D. Tovar
Source: Langmuir (article, 2020-06-03)
Field: Materials Science — Biomaterials

Computationally Guided Tuning of Amino Acid Configuration Influences the Chiroptical Properties of Supramolecular Peptide-π-Peptide Nanostructures Self-assembled supramolecular materials derived from peptidic macromolecules with π-conjugated building blocks are of enormous interest because of their aqueous solubility and biocompatibility. The design rules to achieve tailored optoelectronic properties from these types of materials can be guided by computation and virtual screening rather than intuition-based experimental trial-and-error. Using machine learning, we reported previously that the supramolecular chirality in self-assembled aggregates from VEVAG-π-GAVEV type peptidic materials was most strongly influenced by hydrogen bonding and hydrophobic packing of the alanine and valine residues. Herein, we build upon this idea to demonstrate through molecular-level experimental characterization and all-atom molecular modeling that varying the stereogenic centers of these residues has a profound impact on the optoelectronic properties of the supramolecular aggregates, whereas the variation of stereogenic centers of other residues has only nominal influence on these properties. This study highlights the synergy between computational and experimental insight relevant to the control of chiroptical or other electronic properties associated with supramolecular materials. Supramolecular chemistry ; Supramolecular chirality ; Stereocenter ; Chirality (physics) ; Peptide ; Hydrogen bond ; Macromolecule ; Materials science ; Nanotechnology ; Molecular recognition ; Nanostructure ; Chemistry ; Supramolecular assembly ; Combinatorial chemistry ; Molecule ; Organic chemistry ; Enantioselective synthesis ; Catalysis Supramolecular Self-Assembly in Materials ; Polydiacetylene-based materials and applications ; Chemical Synthesis and Analysis