jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Gabriel Marques | Karl Leswing | Tim Robertson | David J. Giesen | Mathew D. Halls | Alexander Goldberg | Kyle Marshall | Joshua Staker | Tsuguo Morisato | Hiroyuki Maeshima | Hideyuki Arai | Masaru Sasago | Eiji Fujii | Nobuyuki Matsuzawa
Source: The Journal of Physical Chemistry A (article, 2021-08-03)
Field: Materials Science — Materials Chemistry

<i>De Novo</i> Design of Molecules with Low Hole Reorganization Energy Based on a Quarter-Million Molecule DFT Screen Materials exhibiting higher mobilities than conventional organic semiconducting materials such as fullerenes and fused thiophenes are in high demand for applications in printed electronics. To discover new molecules in the heteroacene family that might show improved hole mobility, three <i>de novo</i> design methods were applied. Machine learning (ML) models were generated based on previously calculated hole reorganization energies of a quarter million examples of heteroacenes, where the energies were calculated by applying density functional theory (DFT) and a massive cloud computing environment. The three generative methods applied were (1) the continuous space method, where molecular structures are converted into continuous variables by applying the variational autoencoder/decoder technique; (2) the method based on reinforcement learning of SMILES strings (the REINVENT method); and (3) the junction tree variational autoencoder method that directly generates molecular graphs. Among the three methods, the second and third methods succeeded in obtaining chemical structures whose DFT-calculated hole reorganization energy was lower than the lowest energy in the training dataset. This suggests that an extrapolative materials design protocol can be developed by applying generative modeling to a quantitative structure-property relationship (QSPR) utility function. Autoencoder ; Density functional theory ; Molecule ; Computer science ; HOMO/LUMO ; Fullerene ; Function (biology) ; Materials science ; Computational chemistry ; Statistical physics ; Topology (electrical circuits) ; Algorithm ; Deep learning ; Artificial intelligence ; Chemistry ; Physics ; Quantum mechanics ; Mathematics ; Combinatorics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Molecular Junctions and Nanostructures
Authors: Yuzhi Xu | Cheng‐Wei Ju | Bo Li | Qiu-Shi Ma | Zhenyu Chen | Lianjie Zhang | Junwu Chen
Source: ACS Applied Materials & Interfaces (article, 2021-07-16)
Field: Materials Science — Materials Chemistry

Hydrogen Evolution Prediction for Alternating Conjugated Copolymers Enabled by Machine Learning with Multidimension Fragmentation Descriptors Hydrogen evolution by alternating conjugated copolymers has attracted much attention in recent years. To study alternating copolymers with data-driven strategies, two types of multidimension fragmentation descriptors (MDFD), structure-based MDFD (SMDFD), and electronic property-based MDFD (EPMDFD), have been developed with machine learning (ML) algorithms for the first time. The superiority of SMDFD-based models has been demonstrated by the highly accurate and universal predictions of electronic properties. Moreover, EPMDFD-based, experimental-parameter-free ML models were developed for the prediction of the hydrogen evolution reaction, displaying excellent accuracy (real-test accuracy = 0.91). The combination of explainable ML approaches and first-principles calculations was employed to explore photocatalytic dynamics, revealing the importance of electron delocalization in the excited state. Virtual designing of high-performance candidates can also be achieved. Our work illustrates the huge potential of ML-based material design in the field of polymeric photocatalysts toward high-performance photocatalysis. Materials science ; Conjugated system ; Copolymer ; Photocatalysis ; Fragmentation (computing) ; Delocalized electron ; Computer science ; Excited state ; Artificial intelligence ; Machine learning ; Polymer ; Biological system ; Nanotechnology ; Catalysis ; Organic chemistry ; Physics Machine Learning in Materials Science ; Advanced Photocatalysis Techniques ; Electrocatalysts for Energy Conversion
Authors: Omri Abarbanel | Geoffrey Hutchison
Source: The Journal of Chemical Physics (article, 2021-08-03)
Field: Materials Science — Materials Chemistry

Machine learning to accelerate screening for Marcus reorganization energies Understanding and predicting the charge transport properties of π-conjugated materials is an important challenge for designing new organic electronic devices, such as solar cells, plastic transistors, light-emitting devices, and chemical sensors. A key component of the hopping mechanism of charge transfer in these materials is the Marcus reorganization energy which serves as an activation barrier to hole or electron transfer. While modern density functional methods have proven to accurately predict trends in intramolecular reorganization energy, such calculations are computationally expensive. In this work, we outline active machine learning methods to predict computed intramolecular reorganization energies of a wide range of polythiophenes and their use toward screening new compounds with low internal reorganization energies. Our models have an overall root mean square error (RMSE) of ±0.113 eV, but a much smaller RMSE of only ±0.036 eV on the new screening set. Since the larger error derives from high-reorganization energy compounds, the new method is highly effective to screen for compounds with potentially efficient charge transport parameters. Intramolecular force ; Marcus theory ; Charge (physics) ; Density functional theory ; Work (physics) ; Mean squared error ; Computer science ; Transistor ; Materials science ; Chemical physics ; Chemistry ; Computational chemistry ; Physics ; Voltage ; Thermodynamics ; Mathematics Machine Learning in Materials Science ; Molecular Junctions and Nanostructures ; Organic Electronics and Photovoltaics
Authors: Alan Bilsland | Kirsten McAulay | Ryan West | Angelo Pugliese | Justin Bower
Source: Journal of Chemical Information and Modeling (article, 2021-05-24)
Field: Materials Science — Materials Chemistry

Automated Generation of Novel Fragments Using Screening Data, a Dual SMILES Autoencoder, Transfer Learning and Syntax Correction Fragment-based hit identification (FBHI) allows proportionately greater coverage of chemical space using fewer molecules than traditional high-throughput screening approaches. However, effectively exploiting this advantage is highly dependent on the library design. Solubility, stability, chemical complexity, chemical/shape diversity, and synthetic tractability for fragment elaboration are all critical aspects, and molecule design remains a time-consuming task for computational and medicinal chemists. Artificial neural networks have attracted considerable attention in automated <i>de novo</i> design applications and could also prove useful for fragment library design. Chemical autoencoders are neural networks consisting of encoder and decoder parts, which respectively compress and decompress molecular representations. The decoder is applied to samples drawn from the space of compressed representations to generate novel molecules that can be scored for properties of interest. Here, we report an autoencoder model using a recurrent neural network architecture, which was trained using 486,565 fragments curated from commercial sources, to simultaneously reconstruct both SMILES and chemical fingerprints. To explore its utility in fragment design, we applied transfer learning to the fingerprint decoder layers to train a classifier using 66 frequent hitter fragments identified from our screening campaigns. Using a particle swarm optimization sampling approach, we compare the performance of this "dual" model to an architecture encoding SMILES only. The dual model produced valid SMILES with improved features, considering a range of properties including aromatic ring counts, heavy atom count, synthetic accessibility, and a new fragment complexity score we term Feature Complexity (FeCo). Additionally, we demonstrate that generative performance is further enhanced by use of a simple syntax-correction procedure during training, in which invalid and undesirable SMILES are spiked into the training set. Finally, we used the syntax-corrected model to generate a library of novel candidate privileged fragments. Autoencoder ; Syntax ; Computer science ; Dual (grammatical number) ; Artificial intelligence ; Natural language processing ; Transfer of learning ; Transfer (computing) ; Pattern recognition (psychology) ; Machine learning ; Deep learning ; Linguistics Machine Learning in Materials Science ; Topic Modeling ; Scientific Computing and Data Management
Authors: Reeju Pokharel | Anup Pandey | Alexander Scheinker
Source: JOM (article, 2021-09-29)
Field: Materials Science — Materials Chemistry

Physics-Informed Data-Driven Surrogate Modeling for Full-Field 3D Microstructure and Micromechanical Field Evolution of Polycrystalline Materials Deformation (meteorology) ; Microstructure ; Plasticity ; Nonlinear system ; Field (mathematics) ; Crystallite ; Representation (politics) ; Materials science ; Crystal plasticity ; Statistical physics ; Mechanics ; Physics ; Composite material ; Mathematics ; Metallurgy Microstructure and mechanical properties ; Model Reduction and Neural Networks ; Metallurgy and Material Forming
Authors: S. Alireza Ghasemi | Thomas D. Kühne
Source: The Journal of Chemical Physics (article, 2021-02-17)
Field: Materials Science — Materials Chemistry

Artificial neural networks for the kinetic energy functional of non-interacting fermions A novel approach to find the fermionic non-interacting kinetic energy functional with chemical accuracy using machine learning techniques is presented. To that extent, we apply machine learning to an intermediate quantity rather than targeting the kinetic energy directly. We demonstrate the performance of the method for three model systems containing three and four electrons. The resulting kinetic energy functional remarkably accurately reproduces self-consistently the ground state electron density and total energy of reference Kohn-Sham calculations with an error of less than 5 mHa. This development opens a new avenue to advance orbital-free density functional theory by means of machine learning. Kinetic energy ; Density functional theory ; Orbital-free density functional theory ; Fermion ; Energy (signal processing) ; Physics ; Statistical physics ; Artificial neural network ; Electron ; Ground state ; Kinetic theory ; Hybrid functional ; Computer science ; Artificial intelligence ; Quantum mechanics ; Theoretical physics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Catalysis and Oxidation Reactions
Authors: Oliver T. Unke | Mihail Bogojeski | Michael Gastegger | Mario Geiger | Tess Smidt | Klaus‐Robert Müller
Source: arXiv (Cornell University) (preprint, 2021-06-04)
Field: Materials Science — Materials Chemistry

SE(3)-equivariant prediction of molecular wavefunctions and electronic\n densities Machine learning has enabled the prediction of quantum chemical properties\nwith high accuracy and efficiency, allowing to bypass computationally costly ab\ninitio calculations. Instead of training on a fixed set of properties, more\nrecent approaches attempt to learn the electronic wavefunction (or density) as\na central quantity of atomistic systems, from which all other observables can\nbe derived. This is complicated by the fact that wavefunctions transform\nnon-trivially under molecular rotations, which makes them a challenging\nprediction target. To solve this issue, we introduce general SE(3)-equivariant\noperations and building blocks for constructing deep learning architectures for\ngeometric point cloud data and apply them to reconstruct wavefunctions of\natomistic systems with unprecedented accuracy. Our model achieves speedups of\nover three orders of magnitude compared to ab initio methods and reduces\nprediction errors by up to two orders of magnitude compared to the previous\nstate-of-the-art. This accuracy makes it possible to derive properties such as\nenergies and forces directly from the wavefunction in an end-to-end manner. We\ndemonstrate the potential of our approach in a transfer learning application,\nwhere a model trained on low accuracy reference wavefunctions implicitly learns\nto correct for electronic many-body interactions from observables computed at a\nhigher level of theory. Such machine-learned wavefunction surrogates pave the\nway towards novel semi-empirical methods, offering resolution at an electronic\nlevel while drastically decreasing computational cost. Additionally, the\npredicted wavefunctions can serve as initial guess in conventional ab initio\nmethods, decreasing the number of iterations required to arrive at a converged\nsolution, thus leading to significant speedups without any loss of accuracy or\nrobustness.\n Wave function ; Observable ; Ab initio ; Electronic structure ; Computer science ; Function (biology) ; Artificial intelligence ; Statistical physics ; Algorithm ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Various Chemistry Research Topics
Authors: P. Nirmala | G. Ramkumar | Satyajeet Sahoo | G Anitha | S. Ramesh | S. Agnes Shifani | Agegnehu Shara Shata
Source: Advances in Materials Science and Engineering (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Artificial Intelligence to Analyze the Performance of the Ceramic‐Coated Diesel Engine Using Digital Filter Optimization The completeness of oil goods activates the barriers of lack of goods, inequality in the society, and surroundings impoverishment. Avoiding their use overnight and switching to clean electric motors are a challenge. Under all these conditions, researchers can launch their research on alternative fuels for a preeminent solution. Oxygenated fuel additives and thermal barrier coating (TBC) applications are essential to decrease the emission levels of exhaust and improve the performance of the vehicle. The main objective of this research is to analyze the performance of the ceramic‐coated diesel engine. The ceramic particles use polymer coating to enhance the functionality and durability. Optimum outcomes are determined using Taguchi method. The impacts of various casting parameters of composites have been examined in detail. PSO‐GA (Particle Swarm Optimization and Genetic Algorithm) is utilized to analyze the performance. Using an artificial neural network (ANN), the performance of diesel engine is examined to reduce time, cost, and experimental repetition. Thus, by using the artificial intelligence, the performance of the ceramic‐coated diesel engine is analyzed and the polymeric substance and condition in coating ceramic engine is discussed. Materials science ; Ceramic ; Diesel engine ; Filter (signal processing) ; Process engineering ; Diesel fuel ; Composite material ; Automotive engineering ; Mechanical engineering ; Computer science ; Computer vision Catalytic Processes in Materials Science ; Lubricants and Their Additives ; Tribology and Lubrication Engineering
Authors: Alireza Sanaeifar | Wenkai Zhang | Haitian Chen | Dongyi Zhang | Xiaoli Li | Yong He
Source: Ecotoxicology and Environmental Safety (article, 2021-12-06)
Field: Chemistry — Analytical Chemistry

Study on effects of airborne Pb pollution on quality indicators and accumulation in tea plants using Vis-NIR spectroscopy coupled with radial basis function neural network Tea plants that have a large leaf area mainly suffer from heavy metal accumulation in the above-ground parts through foliar uptake. With the world rapid industrialization, this pollution in tea is considered a crucial challenge due to its potential health risks. The present study proposes an innovative approach based on visible and near-infrared (Vis-NIR) spectroscopy coupled with chemometrics for the characterization of tea chemical indicators under airborne lead stress, which can be performed fast and in situ. The effects of lead stress on chemical indicators and accumulation in leaves of the two tea varieties at different time intervals and levels of treatment were investigated. In addition, changes in cell structure and leaf stomata were monitored during foliar uptake of aerosol particles by transmission electron microscopy (TEM) and scanning electron microscopy (SEM). The spectral variation was able to classify the tea samples into the Pb treatment groups through the linear discriminant analysis (LDA) model. Two machine learning techniques, namely, partial least squares (PLS) and radial basis function neural network (RBFNN), were evaluated and compared for building the quantitative determination models. The RBFNN models combined with correlation-based feature selection (CFS) and PLS data compression methods were used to optimize the prediction performance. The results demonstrated that the PLS-RBFNN as a non-linear model outperformed the PLS model and provided the R-value of 0.944, 0.952, 0.881, 0.937, and 0.930 for prediction of MDA, starch, sucrose, fructose, glucose, respectively. It can be concluded that the proposed approach has strong application potential in monitoring the quality and safety of plants under airborne heavy metal stress. Partial least squares regression ; Chemometrics ; Biological system ; Sugar ; Pollution ; Chemistry ; Environmental science ; Mathematics ; Food science ; Statistics ; Chromatography ; Biology Heavy Metals in Plants ; Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies
Authors: R. B. Vieira | John Lambros
Source: Experimental Mechanics (article, 2021-01-20)
Field: Materials Science — Materials Chemistry

Machine Learning Neural-Network Predictions for Grain-Boundary Strain Accumulation in a Polycrystalline Metal Electron backscatter diffraction ; Materials science ; Grain boundary ; Plasticity ; Digital image correlation ; Microstructure ; Crystallite ; Solid mechanics ; Grain boundary strengthening ; Deformation (meteorology) ; Composite material ; Metallurgy Microstructure and mechanical properties ; Microstructure and Mechanical Properties of Steels ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Akshay Krishna Ammothum Kandy | Eddie Wadbro | Bálint Aradi | Peter Broqvist | Jolla Kullgren
Source: Journal of Chemical Theory and Computation (article, 2021-02-19)
Field: Materials Science — Materials Chemistry

Curvature Constrained Splines for DFTB Repulsive Potential Parametrization The Curvature Constrained Splines (CCS) methodology has been used for fitting repulsive potentials to be used in SCC-DFTB calculations. The benefit of using CCS is that the actual fitting of the repulsive potential is performed through quadratic programming on a convex objective function. This guarantees a unique (for strictly convex) and optimum two-body repulsive potential in a single shot, thereby making the parametrization process robust, and with minimal human effort. Furthermore, the constraints in CCS give the user control to tune the shape of the repulsive potential based on prior knowledge about the system in question. Herein, we developed the method further with new constraints and the capability to handle sparse data. We used the method to generate accurate repulsive potentials for bulk Si polymorphs and demonstrate that for a given Slater-Koster table, which reproduces the experimental band structure for bulk Si in its ground state, we are unable to find one single two-body repulsive potential that can accurately describe the various bulk polymorphs of silicon in our training set. We further demonstrate that to increase transferability, the repulsive potential needs to be adjusted to account for changes in the chemical environment, here expressed in the form of a coordination number. By training a near-sighted Atomistic Neural Network potential, which includes many-body effects but still essentially within the first-neighbor shell, we can obtain full transferability for SCC-DFTB in terms of describing the energetics of different Si polymorphs. Parametrization (atmospheric modeling) ; Curvature ; Transferability ; Statistical physics ; Quadratic programming ; Computer science ; Potential energy ; Quadratic equation ; Physics ; Algorithm ; Mathematical optimization ; Mathematics ; Quantum mechanics ; Machine learning ; Geometry Machine Learning in Materials Science ; Thermal properties of materials ; Ion-surface interactions and analysis
Authors: Siroos Shojaei | Mashaallah Rahmani | Mostafa Khajeh | Ahmad Reza Abbasian
Source: ChemistrySelect (article, 2021-05-20)
Field: Chemistry — Analytical Chemistry

Magnetic‐Nanoparticle‐Based Dispersive Micro‐Solid Phase Extraction for the Determination of Crystal Violet in Environmental Water Samples Abstract In present study, a rapid and simple method based on ultrasonic‐assisted magnetic dispersive micro‐solid phase extraction (MD‐μ‐SPE) using magnetic ZnFe 2 O 4 ‐ZnS nanocomposites has been proposed for the determination of crystal violet (CV) in environmental water samples. To increase the extraction efficiency, the affecting factors on the ultrasonic‐assisted magnetic dispersive micro‐solid phase extraction method were investigated and optimized by Taguchi method, artificial neural network (ANN), and response surface methodology (RSM). For proposed method, the crystal violet was selected as target dye. Under optimal conditions, the limits of detection were obtained 25 ng mL −1 and the calibration curve showed linearity in the range of 100–600 ng mL −1 . The good extraction efficiency obtained indicates that the proposed MD‐μ‐SPE possesses a good potential for the determination of CV in environmental water samples. Crystal violet ; Materials science ; Extraction (chemistry) ; Solid phase extraction ; Calibration curve ; Nanocomposite ; Analytical Chemistry (journal) ; Taguchi methods ; Magnetic nanoparticles ; Crystal (programming language) ; Phase (matter) ; Detection limit ; Nanoparticle ; Chromatography ; Chemistry ; Nanotechnology ; Composite material Analytical chemistry methods development ; Electrochemical Analysis and Applications ; Dye analysis and toxicity