jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Megan A. Lim | Yang Song | Huanghao Mai | Alan C. Cheng
Source: Journal of Chemical Information and Modeling (review, 2022-06-27)
Field: Materials Science — Materials Chemistry

Exploring Deep Learning of Quantum Chemical Properties for Absorption, Distribution, Metabolism, and Excretion Predictions Quantum mechanical (QM) descriptors of small molecules have wide applicability in understanding organic reactivity and molecular properties, but the substantial compute cost required for <i>ab initio</i> QM calculations limits their broad usage. Here, we investigate the use of deep learning for predicting QM descriptors, with the goal of enabling usage of near-QM accuracy electronic properties on large molecular data sets such as those seen in drug discovery. Several deep learning approaches have previously been benchmarked on a published data set called QM9, where 12 ground-state properties have been calculated for molecules with up to nine heavy atoms, limited to C, H, N, O, and F elements. To advance the work beyond the QM9 chemical space and enable application to molecules encountered in drug discovery, we extend the QM9 data set by creating a QM9-extended data set covering an additional ∼20,000 molecules containing S and Cl atoms. Using this extended set, we generate new deep learning models as well as leverage ANI-2x models to provide predictions on larger, more diverse molecules common in drug discovery, and we find the models estimate 11 of 12 ground-state properties reasonably. We use the predicted QM descriptors to augment graph convolutional neural network (GCNN) models for selected ADME end points (rat microsomal clearance, hepatic clearance, total clearance, and P-glycoprotein efflux) and found varying degrees of performance improvement compared to nonaugmented GCNN models, including pronounced improvement in P-glycoprotein efflux prediction. Chemical space ; ADME ; Leverage (statistics) ; Deep learning ; chEMBL ; Convolutional neural network ; Drug discovery ; Computer science ; Chemistry ; Virtual screening ; Artificial intelligence ; Biological system ; Computational chemistry ; Machine learning ; Molecular dynamics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Rongzhi Dong | Yong Zhao | Yuqi Song | Nihang Fu | Sadman Sadeed Omee | Sourin Dey | Qinyang Li | Lai Wei | Jianjun Hu
Source: ACS Applied Materials & Interfaces (article, 2022-08-26)
Field: Materials Science — Materials Chemistry

DeepXRD, a Deep Learning Model for Predicting XRD spectrum from Material Composition One of the long-standing problems in materials science is how to predict a material's structure and then its properties given only its composition. Experimental characterization of crystal structures has been widely used for structure determination, which is, however, too expensive for high-throughput screening. At the same time, directly predicting crystal structures from compositions remains a challenging unsolved problem. Herein we propose a deep learning algorithm for predicting the XRD spectrum given only the composition of a material, which can then be used to infer key structural features for downstream structural analysis such as crystal system or space group classification or crystal lattice parameter determination or materials property prediction. Benchmark studies on two data sets show that our DeepXRD algorithm can achieve good performance for XRD prediction as evaluated over our test sets. It can thus be used in high-throughput screening in the huge materials composition space for materials discovery. Materials science ; Crystal structure ; Characterization (materials science) ; Benchmark (surveying) ; Crystal structure prediction ; Crystal (programming language) ; Computer science ; Key (lock) ; Property (philosophy) ; Artificial intelligence ; Biological system ; Algorithm ; Machine learning ; Nanotechnology ; Crystallography Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Geochemistry and Geologic Mapping
Authors: William Bro‐Jørgensen | Joseph Hamill | Rasmus Bro | Gemma C. Solomon
Source: Chemical Society Reviews (review, 2022-01-01)
Field: Materials Science — Materials Chemistry

Trusting our machines: validating machine learning models for single-molecule transport experiments In this tutorial review, we will describe crucial aspects related to the application of machine learning to help users avoid the most common pitfalls. The examples we present will be based on data from the field of molecular electronics, specifically single-molecule electron transport experiments, but the concepts and problems we explore will be sufficiently general for application in other fields with similar data. In the first part of the tutorial review, we will introduce the field of single-molecule transport, and provide an overview of the most common machine learning algorithms employed. In the second part of the tutorial review, we will show, through examples grounded in single-molecule transport, that the promises of machine learning can only be fulfilled by careful application. We will end the tutorial review with a discussion of where we, as a field, could go from here. Computer science ; Machine learning ; Artificial intelligence ; Human–computer interaction Machine Learning in Materials Science ; Mass Spectrometry Techniques and Applications ; Advanced Electron Microscopy Techniques and Applications
Authors: Zahra Ghiasi | Faramarz Faghihi | Amir Abbas Shayegani Akmal
Source: Electric Power Systems Research (article, 2022-03-26)
Field: Materials Science — Materials Chemistry

Artificial Neural Network Approach for Prediction of Leakage Current of polymeric insulator under Non-Uniform Fan-shaped Contamination Insulator (electricity) ; Contamination ; Leakage (economics) ; Finite element method ; Pollution ; Materials science ; Artificial neural network ; Structural engineering ; Environmental science ; Composite material ; Engineering ; Computer science High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Infrastructure Maintenance and Monitoring
Authors: Zhenze Yang | Markus J. Buehler
Source: Small Methods (article, 2022-07-29)
Field: Materials Science — Materials Chemistry

High‐Throughput Generation of 3D Graphene Metamaterials and Property Quantification Using Machine Learning 3D graphene assemblies are proposed as solutions to meet the goal toward efficient utilization of 2D graphene sheets, showing excellent performances in applications such as mechanical support, energy storage, and electrochemical catalysis. However, given the diversity and complexity of possible graphene 3D structures, there does not yet exist a systematic approach that can generate target 3D shapes and also, evaluate their performance. Here high-throughput data generation is combined with artificial intelligence approaches to realize rapid structure formation and property quantification of 3D graphene foams with mathematically controlled topologies, driven by molecular dynamics simulations. More than 4000 different foam structures are created, which feature diverse topologies that contain potential pathways for small molecules and auxetic structures with negative Poisson's ratio. Empowered by machine learning (ML) algorithms including graph neural networks, not only global properties such as elastic moduli, but also local behaviors such as atomic stress can be predicted and optimized based on their atomic structure, bypassing expensive atomistic simulations. The key findings of the research reported in this paper include a high-throughput virtual framework of generating diverse 3D graphene assemblies with mechanical performances quantification, and highly efficient methods of evaluating physical properties based on ML. Graphene ; Materials science ; Throughput ; Computer science ; Nanotechnology ; Network topology ; Metamaterial ; Auxetics ; Topology (electrical circuits) ; Computational science ; Composite material Graphene research and applications ; Machine Learning in Materials Science ; Supercapacitor Materials and Fabrication
Authors: Ingrid von Glehn | James S. Spencer | David Pfau
Source: arXiv (Cornell University) (preprint, 2022-11-24)
Field: Materials Science — Materials Chemistry

A Self-Attention Ansatz for Ab-initio Quantum Chemistry We present a novel neural network architecture using self-attention, the Wavefunction Transformer (Psiformer), which can be used as an approximation (or Ansatz) for solving the many-electron Schrödinger equation, the fundamental equation for quantum chemistry and material science. This equation can be solved from first principles, requiring no external training data. In recent years, deep neural networks like the FermiNet and PauliNet have been used to significantly improve the accuracy of these first-principle calculations, but they lack an attention-like mechanism for gating interactions between electrons. Here we show that the Psiformer can be used as a drop-in replacement for these other neural networks, often dramatically improving the accuracy of the calculations. On larger molecules especially, the ground state energy can be improved by dozens of kcal/mol, a qualitative leap over previous methods. This demonstrates that self-attention networks can learn complex quantum mechanical correlations between electrons, and are a promising route to reaching unprecedented accuracy in chemical calculations on larger systems. Ansatz ; Wave function ; Quantum ; Artificial neural network ; Ab initio ; Quantum chemistry ; Electron ; Ground state ; Electronic structure ; Statistical physics ; Quantum mechanics ; Computer science ; Physics ; Molecule ; Artificial intelligence Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Advanced Chemical Physics Studies
Authors: Yunzhen Du | Zhaocang Meng | Qiang Yan | Canglong Wang | Yuan Tian | Wen-Shan Duan | Sheng Zhang | Ping Lin
Source: Physical Chemistry Chemical Physics (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Deep potential for a face-centered cubic Cu system at finite temperatures The state-of-the-art method generating potential functions used in molecular dynamics is based on machine learning with neural networks, which is critical for molecular dynamics simulation. This method provides an efficient way for fitting multi-variable nonlinear functions, attracting extensive attention in recent years. Generally, the quality of potentials fitted by neural networks is heavily affected by training datasets and the training process and could be ensured by comprehensively verificating the model accuracy. In this study, we obtained the neural network potential of face-centered cubic (FCC) Cu with the most accurate and adequate training datasets from first-principle calculations and the training process performed by Deep Potential Molecular Dynamics (DeePMD). This potential could not only succeed in reproductions of the variety of properties of Cu at 0 K, but also have a good performance at finite temperatures, such as predicting elastic constants and the melting point. Moreover, our potential has a better generalization capacity to predict the grain boundary energy without including extra datasets about grain boundary structures. These results support the applicability of the method under more practical conditions. Generalization ; Artificial neural network ; Nonlinear system ; Molecular dynamics ; Computer science ; Face (sociological concept) ; Process (computing) ; Potential energy ; Melting point ; Artificial intelligence ; Point (geometry) ; Variable (mathematics) ; Boundary (topology) ; Energy (signal processing) ; Cubic crystal system ; Statistical physics ; Materials science ; Mathematics ; Physics ; Chemistry ; Computational chemistry ; Condensed matter physics ; Mathematical analysis ; Geometry ; Classical mechanics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; nanoparticles nucleation surface interactions
Authors: David E. Farache | Juan C. Verduzco | Zachary D. McClure | Saaketh Desai | Alejandro Strachan
Source: Computational Materials Science (article, 2022-04-06)
Field: Materials Science — Materials Chemistry

Active learning and molecular dynamics simulations to find high melting temperature alloys Active learning (AL) can drastically accelerate materials discovery; its power has been shown in various classes of materials and target properties. Prior efforts have used machine learning models for the optimal selection of physical experiments or physics-based simulations. However, the latter efforts have been mostly limited to the use of electronic structure calculations and properties that can be obtained at the unit cell level and with negligible noise. We couple AL with molecular dynamics simulations to identify multiple principal component alloys (MPCAs) with high melting temperatures. Building on cloud computing services through nanoHUB, we present a fully autonomous workflow for the efficient exploration of the high dimensional compositional space of MPCAs. We characterize how uncertainties arising from the stochastic nature of the simulations and the acquisition functions used to select simulations affect the convergence of the approach. Interestingly, we find that relatively short simulations with significant uncertainties can be used to efficiently find the desired alloys as the random forest models used for AL average out fluctuations. Workflow ; Molecular dynamics ; Computer science ; Convergence (economics) ; Statistical physics ; Random forest ; Physics ; Chemistry ; Artificial intelligence ; Database ; Computational chemistry Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; Ion-surface interactions and analysis
Authors: G. Sakthi Balan | R. Balasundaram | K. Chellamuthu | S. Nandha Gopan | S. Dinesh | V. Vijayan | T. Sathish | S. Rajkumar
Source: Journal of Nanomaterials (article, 2022-01-01)
Field: Materials Science — Polymers and Plastics

Flame Resistance Characteristics of Woven Jute Fiber Reinforced Fly Ash Filled Polymer Composite Due to their unique characteristics, natural fiber reinforced polymer composites have recently been increasingly utilised to replace traditional materials. Fire retardant resins can increase flammability, and they have a deleterious influence on the mechanical properties of the material (Fan et al., 2020). As a result, this experiment examined the flammability of woven jute fiber reinforced with fly ash. The specimens were created by hand layup using a L9 orthogonal array and varying ratios of jute fiber, fly ash, and time for chemical fiber treatment. Vertical and horizontal flammability tests are conducted in accordance with ASTM D635 and ASTM D3801, respectively. According to the test results, the inclusion of fly ash significantly reduces flammability. In this work, 5 wt% inclusion of the jute fiber, 15 wt% addition of the fly ash, and with 10 hours, NaOH treatment produces a composite with minimum burning rates of 10.2 mm/min in horizontal UL‐94 tests. To determine the link between input and output characteristics, various regression models from machine learning are used. Multilayer perception produced a stronger association in both horizontal and vertical testing, according to the models. Materials science ; Composite material ; Fly ash ; Composite number ; Fiber ; Impact resistance Natural Fiber Reinforced Composites ; Flame retardant materials and properties ; Fire dynamics and safety research
Authors: T. Ando | Naoto Shimizu | Norihisa Yamamoto | Nobuyuki Matsuzawa | Hiroyuki Maeshima | Hiromasa Kaneko
Source: The Journal of Physical Chemistry A (article, 2022-09-02)
Field: Materials Science — Materials Chemistry

Design of Molecules with Low Hole and Electron Reorganization Energy Using DFT Calculations and Bayesian Optimization Materials exhibiting higher mobility than conventional organic semiconducting materials, such as fullerenes and fused thiophenes, are in high demand for applications in printed electronics. To discover new molecules that might show improved charge mobility, the adaptive design of experiments (DoE) to design molecules with low reorganization energy was performed by combining density functional theory (DFT) methods and machine learning techniques. DFT-calculated values of 165 molecules were used as an initial training dataset for a Gaussian process regression (GPR) model, and five rounds of molecular designs applying the GPR model and validation via DFT calculations were executed. As a result, new molecules whose reorganization energy is smaller than the lowest value in the initial training dataset were successfully discovered. Bayesian optimization ; Density functional theory ; Molecule ; Fullerene ; Computer science ; Gaussian process ; Kriging ; Materials science ; Gaussian ; Statistical physics ; Computational chemistry ; Artificial intelligence ; Physics ; Chemistry ; Machine learning ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Pascal Pernot
Source: The Journal of Chemical Physics (article, 2022-02-23)
Field: Materials Science — Materials Chemistry

The long road to calibrated prediction uncertainty in computational chemistry Uncertainty quantification (UQ) in computational chemistry (CC) is still in its infancy. Very few CC methods are designed to provide a confidence level on their predictions, and most users still rely improperly on the mean absolute error as an accuracy metric. The development of reliable UQ methods is essential, notably for CC to be used confidently in industrial processes. A review of the CC-UQ literature shows that there is no common standard procedure to report or validate prediction uncertainty. I consider here analysis tools using concepts (calibration and sharpness) developed in meteorology and machine learning for the validation of probabilistic forecasters. These tools are adapted to CC-UQ and applied to datasets of prediction uncertainties provided by composite methods, Bayesian ensembles methods, and machine learning and a posteriori statistical methods. Uncertainty quantification ; Calibration ; Probabilistic logic ; Metric (unit) ; A priori and a posteriori ; Computer science ; Machine learning ; Uncertainty analysis ; Bayesian probability ; Artificial intelligence ; Data mining ; Mathematics ; Statistics ; Simulation ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Analytical Chemistry and Chromatography
Authors: Eunsong Kim | Joonchul Kim | Kyoungmin Min
Source: Physical Chemistry Chemical Physics (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Prediction of dielectric constants of ABO<sub>3</sub>-type perovskites using machine learning and first-principles calculations In this study, the machine-learning method, combined with density functional perturbation theory (DFPT) calculations, was implemented to predict and validate the dielectric constants of ABO<sub>3</sub>-type perovskites. For the construction of the training database, the dielectric constants of 7113 inorganic materials were extracted from the Materials Project. The chemical, structural, and physical descriptors were generated and trained using the gradient-boosting-based regressor after feature engineering. The prediction accuracies were 0.83 and 0.67 (<i>R</i><sup>2</sup>) and 0.12 and 0.26 (root mean square error) for the electronic and ionic contributions to the dielectric constant, respectively. The constructed surrogate model was then employed to predict the dielectric constants of the ABO<sub>3</sub>-type perovskites (216 structures), whose thermodynamic stabilities were satisfactory. The predicted values were validated using DFPT calculations. The constructed database was further used to develop a surrogate model for the prediction of dielectric constants. The final <i>R</i><sup>2</sup> prediction accuracies reached 0.79 (electronic) and 0.67 (ionic). Dielectric ; Ionic bonding ; Perturbation theory (quantum mechanics) ; Materials science ; Mean squared error ; Density functional theory ; Thermodynamics ; Chemistry ; Computational chemistry ; Ion ; Mathematics ; Physics ; Statistics ; Quantum mechanics ; Optoelectronics ; Organic chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electronic and Structural Properties of Oxides
Authors: Michelangelo Domina | Matteo Cobelli | Stefano Sanvito
Source: Physical review. B./Physical review. B (article, 2022-06-30)
Field: Materials Science — Materials Chemistry

Spectral neighbor representation for vector fields: Machine learning potentials including spin We introduce a translational and rotational invariant local representation for vector fields, which can be employed in the construction of machine learning energy models of solids and molecules. This allows us to describe, on the same footing, the energy fluctuations due to the atomic motion, the longitudinal and transverse excitations of the vector field, and their mutual interplay. The formalism can then be applied to physical systems where the total energy is determined by a vector density, as in the case of magnetism. Our representation is constructed over the power spectrum of the combined angular momentum describing the local atomic positions and the vector field, and it can be used in conjunction with different machine learning schemes and data taken from accurate ab initio electronic structure theories. We demonstrate the descriptive power of our representation for a range of classical spin Hamiltonian and machine learning algorithms. In particular, we construct energy models based on both linear Ridge regression, as in conventional spectral neighbor analysis potentials, and the Gaussian approximation. These are both built to represent a Heisenberg-type Hamiltonian including a longitudinal energy term and spin-lattice coupling. Physics ; Hamiltonian (control theory) ; Statistical physics ; Gaussian ; Spectral density ; Quantum mechanics ; k-nearest neighbors algorithm ; Theoretical physics ; Computer science ; Mathematics ; Artificial intelligence Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Protein Structure and Dynamics
Authors: Somayeh Salam | Kamran Kheiralipour | Fuji Jian
Source: Agriculture (article, 2022-07-10)
Field: Chemistry — Analytical Chemistry

Detection of Unripe Kernels and Foreign Materials in Chickpea Mixtures Using Image Processing The existence of dockage, unripe kernels, and foreign materials in chickpea mixtures is one of the main concerns during chickpea storage and marketing. Novel algorithms based on image processing were developed to detect undesirable, foreign materials, and matured chickpea kernels in the chickpea mixture. Images of 270 objects including 54 sound samples and 36 samples of each undesired object were prepared and features of these acquired images were extracted. Different models based on linear discriminant analysis (LDA), support vector machine (SVM), and artificial neural networks (ANN) methods were developed by using MATLAB. Three classification algorithms based on LDA, SVM, and ANN methods were developed. The classification accuracy in training, testing, and overall detection showed the superiority of ANN (99.4, 92.6, and 94.4%, respectively) and LDA (91.1, 94.0, and 91.9%, respectively) over the SVM (100, 53.7, and 88.5%, respectively). The developed image processing technique can be incorporated with a vision-based real-time system. Linear discriminant analysis ; Support vector machine ; Artificial intelligence ; Pattern recognition (psychology) ; Image processing ; Artificial neural network ; MATLAB ; Computer science ; Kernel (algebra) ; Image (mathematics) ; Mathematics ; Machine learning Spectroscopy and Chemometric Analyses ; GABA and Rice Research ; Identification and Quantification in Food