jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jonas Verhellen | Jeriek Van den Abeele
Source: ChemRxiv (preprint, 2020-07-06)
Field: Materials Science — Materials Chemistry

Illuminating Elite Patches of Chemical Space In the past few years, there has been considerable activity in both academic and industrial research to develop innovative machine learning approaches to locate novel, high-performing molecules in chemical space. Here we describe a new and fundamentally different type of approach that provides a holistic overview of how high-performing molecules are distributed throughout a search space. Based on an open-source, graph-based implementation [Jensen, Chem. Sci., 2019, 12, 3567-3572] of a traditional genetic algorithm for molecular optimisation, and influenced by state-of-the-art concepts from soft robot design [Mouret et al., IEEE Trans. Evolut. Comput., 2016, 22, 623-630], we provide an algorithm that (i) produces a large diversity of high-performing, yet qualitatively different molecules, (ii) illuminates the distribution of optimal solutions, and (iii) improves search efficiency compared to both machine learning and traditional genetic algorithm approaches. Space (punctuation) ; Chemical space ; Artificial intelligence ; Computer science ; Elite ; Genetic algorithm ; Graph ; Robot ; Molecule ; Machine learning ; Theoretical computer science ; Chemistry ; Biology ; Bioinformatics ; Drug discovery Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation ; Computational Drug Discovery Methods
Authors: Seiji Takeda | Toshiyuki Hama | Hsiang-Han Hsu | Toshiyuki Yamane | Koji Masuda | Victoria A. Piunova | Dmitry Yu. Zubarev | Jed W. Pitera | Daniel P. Sanders | Daiju Nakano
Source: arXiv (Cornell University) (preprint, 2020-01-20)
Field: Materials Science — Materials Chemistry

AI-driven Inverse Design System for Organic Molecules Designing novel materials that possess desired properties is a central need across many manufacturing industries. Driven by that industrial need, a variety of algorithms and tools have been developed that combine AI (machine learning and analytics) with domain knowledge in physics, chemistry, and materials science. AI-driven materials design can be divided to mainly two stages; the first one is the modeling stage, where the goal is to build an accurate regression or classification model to predict material properties (e.g. glass transition temperature) or attributes (e.g. toxic/non-toxic). The next stage is design, where the goal is to assemble or tune material structures so that they can achieve user-demanded target property values based on a prediction model that is trained in the modeling stage. For maximum benefit, these two stages should be architected to form a coherent workflow. Today there are several emerging services and tools for AI-driven material design, however, most of them provide only partial technical components (e.g. data analyzer, regression model, structure generator, etc.), that are useful for specific purposes, but for comprehensive material design, those components need to be orchestrated appropriately. Our material design system provides an end-to-end solution to this problem, with a workflow that consists of data input, feature encoding, prediction modeling, solution search, and structure generation. The system builds a regression model to predict properties, solves an inverse problem on the trained model, and generates novel chemical structure candidates that satisfy the target properties. In this paper we will introduce the methodology of our system, and demonstrate a simple example of inverse design generating new chemical structures that satisfy targeted physical property values. Workflow ; Computer science ; Inverse ; Property (philosophy) ; Analytics ; Domain (mathematical analysis) ; Generator (circuit theory) ; Artificial intelligence ; Machine learning ; Data mining ; Database Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemistry and Chemical Engineering
Authors: Gerardo Raggi | Christian L. Ritterhoff | Ignacio Fdez. Galván | Morgane Vacher | Roland Lindh
Source: ChemRxiv (preprint, 2020-03-09)
Field: Materials Science — Materials Chemistry

Restricted-Variance Molecular Geometry Optimization Based on Gradient-Enhanced Kriging Machine learning techniques, specifically Gradient-Enhanced Kriging (GEK), has been implemented for molecular geometry optimization.GEK has many advantages as compared to conventional -- step-restricted second-order truncated -- molecular optimization methods.In particular, the surrogate model associated with GEK can have multiple stationary points, will smoothly converge to theexact model as the size of the data set increases, and contains an explicit expression for the expected average error of the model functionat an arbitrary point in space.In this respect GEK can be of interest for methods used in molecular geometry optimizations.GEK is usually, however, associated with abundance of data, contrary to the situation desired forefficient geometry optimizations.In the paper we will demonstrate how the GEK procedure can be utilized in a fashion such that in the presence of few data points, thesurrogate surface will in a robust way guide the optimization to a minimum of a molecular structure.In this respect the GEK procedure will be used to mimic the behavior of a conventional second-order scheme, but retaining theflexibility of the superior machine learning approach -- GEK is an exact interpolator.Moreover, the expected variance will be used in the optimization to facilitate restricted-variance rational function optimizations (RV-RFO).A procedure which relates the eigenvalues of the Hessian-model-function Hessian with the individual characteristiclengths, used in the GEK, reduces the number of empirical parameters to two -- the value of the trend function and themaximum allowed variance. These parameters are determined using the extended Baker (e-Baker) test suite, at the Hartree-Fock level of approximation,and a single reaction of the Baker transition-state (Baker-TS) test suite as a training set. The so-created optimizationprocedure -- RV-RFO-GEK -- is tested using the e-Baker, the full Baker-TS, and the S22 test suites, at the density-functional-theory level for the two Baker test suitesand at the second order Møller-Plesset level of approximation for the S22 test suite, respectively.The tests show that the new method is generally on par with a state-of-the-art conventional method, while for difficult cases it exhibits a definite advantage. Hessian matrix ; Mathematics ; Variance (accounting) ; Function (biology) ; Mathematical optimization ; Kriging ; Optimization problem ; Flexibility (engineering) ; Algorithm ; Surrogate model ; Applied mathematics ; Statistics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Chemical Physics Studies
Authors: Maximilian Topel | Andrew L. Ferguson
Source: The Journal of Chemical Physics (article, 2020-11-16)
Field: Materials Science — Materials Chemistry

Reconstruction of protein structures from single-molecule time series Single-molecule experimental techniques track the real-time dynamics of molecules by recording a small number of experimental observables. Following these observables provides a coarse-grained, low-dimensional representation of the conformational dynamics but does not furnish an atomistic representation of the instantaneous molecular structure. Takens's delay embedding theorem asserts that, under quite general conditions, these low-dimensional time series can contain sufficient information to reconstruct the full molecular configuration of the system up to an a priori unknown transformation. By combining Takens's theorem with tools from statistical thermodynamics, manifold learning, artificial neural networks, and rigid graph theory, we establish an approach, Single-molecule TAkens Reconstruction, to learn this transformation and reconstruct molecular configurations from time series in experimentally measurable observables such as intramolecular distances accessible to single molecule Förster resonance energy transfer. We demonstrate the approach in applications to molecular dynamics simulations of a C<sub>24</sub>H<sub>50</sub> polymer chain and the artificial mini-protein chignolin. The trained models reconstruct molecular configurations from synthetic time series data in the head-to-tail molecular distances with atomistic root mean squared deviation accuracies better than 0.2 nm. This work demonstrates that it is possible to accurately reconstruct protein structures from time series in experimentally measurable observables and establishes the theoretical and algorithmic foundations to do so in applications to real experimental data. Observable ; Series (stratigraphy) ; A priori and a posteriori ; Representation (politics) ; Molecular dynamics ; Embedding ; Transformation (genetics) Machine Learning in Materials Science ; Quantum many-body systems ; Protein Structure and Dynamics
Authors: Justin S. Smith | Nicholas Lubbers | Aidan P. Thompson | Kipton Barros
Source: arXiv (Cornell University) (preprint, 2020-06-09)
Field: Materials Science — Materials Chemistry

Simple and efficient algorithms for training machine learning potentials to force data Abstract Machine learning models, trained on data from ab initio quantum simulations, are yielding molecular dynamics potentials with unprecedented accuracy. One limiting factor is the quantity of available training data, which can be expensive to obtain. A quantum simulation often provides all atomic forces, in addition to the total energy of the system. These forces provide much more information than the energy alone. It may appear that training a model to this large quantity of force data would introduce significant computational costs. Actually, training to all available force data should only be a few times more expensive than training to energies alone. Here, we present a new algorithm for efficient force training, and benchmark its accuracy by training to forces from real-world datasets for organic chemistry and bulk aluminum. Benchmark (surveying) ; Computer science ; Limiting ; Simple (philosophy) ; Training (meteorology) ; Quantum ; Training set ; Energy (signal processing) ; Machine learning ; Molecular dynamics ; Algorithm ; Artificial intelligence ; Physics ; Computational chemistry ; Engineering ; Chemistry ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Shaymaa A. Qenawy | Loai Nasrat | Hanafy M. Ismail | J. N. Asaad
Source: IET Nanodielectrics (article, 2020-05-21)
Field: Materials Science — Materials Chemistry

Evaluation of dielectric strength of SiR/TiO <sub>2</sub> composites using feed‐forward neural network Among the recently insulating materials broadly utilized in high voltage outdoor insulation, silicone rubber (SiR) has gotten the foremost consideration. Actually, SiR is becoming an efficient countermeasure to insulator contamination issues. To enhance different properties of polymeric materials, micro‐ and nanofillers have been used for dielectric applications. In this study, micron‐sized titanium dioxide (TiO 2 ) and nano‐sized TiO 2 fillers were added to the SiR matrix to improve electrical and mechanical properties. Dielectric strength, tensile strength, and elongation at break tests were monitored. Also, a scanning electron microscope was carried out. The samples were prepared by mixing micro‐TiO 2 into SiR with the content of 0, 10, 20, 30, and 40 wt% and also mixing nano‐TiO 2 into SiR with the content of 0, 1, 3, 5, and 7 wt%. A feed‐forward neural network technique was used to estimate the dielectric strength in different conditions and different percentages of fillers. Adding nano TiO 2 filler enhances the electrical and mechanical properties of SiR composites. SiR with 5 wt% nano TiO 2 showed the best improvement in electrical and mechanical properties. Materials science ; Composite material ; Ultimate tensile strength ; Dielectric ; Silicone rubber ; Scanning electron microscope ; Dielectric strength ; Titanium dioxide ; Izod impact strength test ; Tear resistance High voltage insulation and dielectric phenomena ; Dielectric materials and actuators ; Polymer Nanocomposites and Properties
Authors: Hiroshi Sakiyama | Takaaki Abiko | Kosuke Yoshida | Kaoru Shomura | Ryoji Mitsuhashi | Yoshiki Koyama | Masahiro Mikuriya | Masayuki Koikawa | Minoru Mitsumi
Source: RSC Advances (article, 2020-01-01)
Field: Chemistry — Spectroscopy

Detailed magnetic analysis and successful deep-neural-network-based conformational prediction for [VO(dmso)<sub>5</sub>][BPh<sub>4</sub>]<sub>2</sub> Pentakis(dimethylsulfoxide-κ<i>O</i>)oxidovanadium(iv) bis(tetraphenylborate), [VO(dmso)<sub>5</sub>][BPh<sub>4</sub>]<sub>2</sub> (dmso: dimethylsulfoxide), was synthesized, and its pseudo-<i>C</i> <sub>4</sub> VO<sub>6</sub> coordination geometry was revealed by a single-crystal X-ray method. A novel equation set was obtained for magnetic susceptibility and magnetization of the d<sup>1</sup> complexes, considering the axial distortion and the spin-orbit coupling for the <sup>2</sup>D free-ion term. The equation set enabled magnetic simulation for significantly symmetry-lowered d<sup>1</sup> complexes to obtain the anisotropic <i>g</i>-values and also the excitation energies. In addition, conformational prediction was conducted, using the enumeration results on the basis of the group theory. The dominant conformers were predicted on the basis of the density functional theory (DFT) method, and especially, the conformer in the crystal was successfully predicted by a deep neural network method. Artificial neural network ; Chemistry ; Computer science ; Artificial intelligence Molecular spectroscopy and chirality ; Computational Drug Discovery Methods ; Catalysis and Oxidation Reactions
Authors: Yunfan Jiang | Jingjing Si | Rui Zhang | Godwin Enemali | Bin Zhou | H. McCann | Chang Liu
Source: arXiv (Cornell University) (preprint, 2020-10-08)
Field: Chemistry — Spectroscopy

CSTNet: A Dual-Branch Convolutional Network for Imaging of Reactive Flows using Chemical Species Tomography Chemical Species Tomography (CST) has been widely used for in situ imaging of critical parameters, e.g. species concentration and temperature, in reactive flows. However, even with state-of-the-art computational algorithms the method is limited due to the inherently ill-posed and rank-deficient tomographic data inversion, and by high computational cost. These issues hinder its application for real-time flow diagnosis. To address them, we present here a novel CST-based convolutional neural Network (CSTNet) for high-fidelity, rapid, and simultaneous imaging of species concentration and temperature. CSTNet introduces a shared feature extractor that incorporates the CST measurement and sensor layout into the learning network. In addition, a dual-branch architecture is proposed for image reconstruction with crosstalk decoders that automatically learn the naturally correlated distributions of species concentration and temperature. The proposed CSTNet is validated both with simulated datasets, and with measured data from real flames in experiments using an industry-oriented sensor. Superior performance is found relative to previous approaches, in terms of robustness to measurement noise and millisecond-level computing time. This is the first time, to the best of our knowledge, that a deep learning-based algorithm for CST has been experimentally validated for simultaneous imaging of multiple critical parameters in reactive flows using a low-complexity optical sensor with severely limited number of laser beams. Robustness (evolution) ; Convolutional neural network ; Computer science ; Tomography ; Inverse problem ; Deep learning ; Artificial intelligence ; Algorithm ; Synthetic data ; Network architecture ; Optics ; Physics ; Mathematics ; Chemistry Spectroscopy and Laser Applications ; Atmospheric and Environmental Gas Dynamics ; Air Quality Monitoring and Forecasting
Authors: Iman Ahadi Akhlaghi | Saeed Kahrobaee | Kian Karami Nezhad
Source: Nondestructive Testing And Evaluation (article, 2020-03-19)
Field: Materials Science — Electronic, Optical and Magnetic Materials

An accurate non-destructive method for determining mechanical properties of plain carbon steel parts using MHL and GRNN The sensitivity of magnetic non-destructive methods to both chemical composition and microstructure has limited their potential application for determination of mechanical properties in plain carbon steels under conditions of varying carbon content and microstructure. The present paper investigates advantages of applying an artificial neural network (ANN) method to magnetic hysteresis loop (MHL) method for non-destructively measuring mechanical properties of plain carbon steels with unknown carbon and microstructure (resulting from various heat-treating processes). Artificial neural network used in this study is a generalised regression neural network (GRNN), since it has reportedly high performance in estimation and function approximation and could be trained very fast. After it is appropriately trained, the neural network takes one of the four magnetic parameters (or any combination of them) extracted from the measured hysteresis loop to estimate the desired mechanical parameters (hardness, tensile strength, yield strength, and elongation) of the sample under test. The results revealed that the proposed methodology can be a very effective tool to estimate the mechanical properties of the hypoeutectic plain carbon steel sample with unknown carbon content and heat treatment background if appropriate combination of magnetic properties is used as the GRNN inputs. Microstructure ; Materials science ; Artificial neural network ; Hysteresis ; Ultimate tensile strength ; Carbon fibers ; Carbon steel ; Eutectic system ; Composite material ; Computer science ; Artificial intelligence Magnetic Properties and Applications ; Non-Destructive Testing Techniques ; Microstructure and Mechanical Properties of Steels
Authors: Olga Jakšić | Marko Spasenović | Zoran Jakšić | Dana Vasiljević-Radović
Source: Surfaces (article, 2020-08-24)
Field: Materials Science — Materials Chemistry

Monolayer Gas Adsorption on Graphene-Based Materials: Surface Density of Adsorption Sites and Adsorption Capacity Surface density of adsorption sites on an adsorbent (including affinity-based sensors) is one of the basic input parameters in modeling of process kinetics in adsorption based devices. Yet, there is no simple expression suitable for fast calculations in current multiscale models. The published experimental data are often application-specific and related to the equilibrium surface density of adsorbate molecules. Based on the known density of adsorbed gas molecules and the surface coverage, both of these in equilibrium, we obtained an equation for the surface density of adsorption sites. We applied our analysis to the case of pristine graphene and thus estimated molecular dynamics of adsorption on it. The monolayer coverage was determined for various pressures and temperatures. The results are verified by comparison with literature data. The results may be applicable to modeling of the surface density of adsorption sites for gas adsorption on other homogeneous crystallographic surfaces. In addition to it, the obtained analytical expressions are suitable for training artificial neural networks determining the surface density of adsorption sites on a graphene surface based on the known binding energy, temperature, mass of adsorbate molecules and their affinity towards graphene. The latter is of interest for multiscale modelling. Adsorption ; Monolayer ; Graphene ; Molecule ; Chemical physics ; Materials science ; Density functional theory ; Thermodynamics ; Physical chemistry ; Chemistry ; Computational chemistry ; Nanotechnology ; Organic chemistry ; Physics Graphene research and applications ; Thermal properties of materials ; Gas Sensing Nanomaterials and Sensors
Authors: James S. Spencer | David Pfau | Aleksandar Botev | W. M. C. Foulkes
Source: arXiv (Cornell University) (preprint, 2020-11-13)
Field: Materials Science — Materials Chemistry

Better, Faster Fermionic Neural Networks The Fermionic Neural Network (FermiNet) is a recently-developed neural network architecture that can be used as a wavefunction Ansatz for many-electron systems, and has already demonstrated high accuracy on small systems. Here we present several improvements to the FermiNet that allow us to set new records for speed and accuracy on challenging systems. We find that increasing the size of the network is sufficient to reach chemical accuracy on atoms as large as argon. Through a combination of implementing FermiNet in JAX and simplifying several parts of the network, we are able to reduce the number of GPU hours needed to train the FermiNet on large systems by an order of magnitude. This enables us to run the FermiNet on the challenging transition of bicyclobutane to butadiene and compare against the PauliNet on the automerization of cyclobutadiene, and we achieve results near the state of the art for both. Ansatz ; Artificial neural network ; Computer science ; Set (abstract data type) ; Cyclobutadiene ; Wave function ; Artificial intelligence ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Advanced Physical and Chemical Molecular Interactions ; Catalysis and Oxidation Reactions
Authors: Akira Yada | Kazuhiko Sato | Tarojiro Matsumura | Yasunobu Ando | Kenji Nagata | Sakina Ichinoseki
Source: Synlett (article, 2020-11-05)
Field: Materials Science — Materials Chemistry

Ensemble Learning Approach with LASSO for Predicting Catalytic Reaction Rates Abstract The prediction of the initial reaction rate in the tungsten-catalyzed epoxidation of alkenes by using a machine learning approach is demonstrated. The ensemble learning framework used in this study consists of random sampling with replacement from the training dataset, the construction of several predictive models (weak learners), and the combination of their outputs. This approach enables us to obtain a reasonable prediction model that avoids the problem of overfitting, even when analyzing a small dataset. Overfitting ; Ensemble learning ; Random forest ; Lasso (programming language) ; Chemistry ; Machine learning ; Artificial intelligence ; Ensemble forecasting ; Catalysis ; Artificial neural network ; Computer science ; Organic chemistry Machine Learning in Materials Science ; Polyoxometalates: Synthesis and Applications ; Computational Drug Discovery Methods