jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Maxime Ducamp | François‐Xavier Coudert
Source: ChemRxiv (preprint, 2022-01-05)
Field: Chemistry — Inorganic Chemistry

Prediction of Thermal Properties of Zeolites through Machine Learning The use of machine learning for the prediction of physical and chemical properties of crystals based on their structure alone is currently an area of intense research in computational materials science. In this work, we studied the possibility of using machine learning-trained algorithms in order to calculate the thermal properties of siliceous zeolite frameworks. We used as training data the thermal properties of 120 zeolites, calculated at the DFT level, in the quasi-harmonic approximation. We compared the statistical accuracy of trained models (based on the gradient boosting regression technique) using different types of descriptors, including ad hoc geometrical features, topology, pore space, and general geometric descriptors. While geometric descriptors were found to perform best, we also identified limitations on the accuracy of the predictions, especially for a small group of materials with very highly negative thermal expansion coefficients. We then studied the generalizability of the technique, demonstrating that the predictions were not sensitive to the refinement of framework structures at a high level of theory. Therefore, the models are suitable for the exploration and screening of large-scale databases of hypothetical frameworks, which we illustrate on the PCOD2 database of zeolites containing around 600,000 hypothetical structures. Generalizability theory ; Machine learning ; Computer science ; Artificial intelligence ; Harmonic mean ; Gradient boosting ; Boosting (machine learning) ; Thermal ; Algorithm ; Mathematics ; Geometry ; Thermodynamics ; Physics ; Statistics Zeolite Catalysis and Synthesis ; Machine Learning in Materials Science ; Catalysis and Oxidation Reactions
Authors: Rahul Upadhya | Matthew Tamasi | Elena Di Mare | Sanjeeva Murthy | Adam J. Gormley
Source: ChemRxiv (preprint, 2022-09-13)
Field: Materials Science — Materials Chemistry

Data-Driven Design of Protein-Like Single-Chain Polymer Nanoparticles The functional structure of proteins is heavily influenced by their folding behavior. AlphaFold, a powerful artificial intelligence (AI) program trained on information from the Protein Data Bank (PDB), was developed to predict the 3D structure of proteins from its amino acid sequence. Inspired by this, we aim to elucidate structural features of synthetic single-chain polymer nanoparticles (SCNPs) based on compositional information (monomers, chain length, molecular weight, charge, and valency) by machine learning (ML). Specifically, we demonstrate the effectiveness of ML to improve the efficiency of SCNP design and uncover important polymer design attributes to mimic protein-like structural features. To start, we randomly screened over 1000 synthesized SCNPs through a combination of high-throughput dynamic light scattering (DLS) and small-angle X-ray scattering (SAXS) and compared these results to simulated protein data from the PDB. Then, utilizing evidential neural networks (ENets), we predicted, synthesized, and characterized 30 novel compact SCNPs. Incredibly, this data-driven approach yielded 58% of the predicted SCNPs with Porod exponent ≥ 3.5 as opposed to 5% of SCNPs from the random screen. Using Shapely additive explanation (SHAP) values, we further uncovered interesting contributions of monomer content on Porod exponent and radius of gyration. From this work, we have shown that an ML-guided approach proves effective for the challenging, unintuitive problem of nanoparticle design. Radius of gyration ; Monomer ; Polymer ; Intrinsically disordered proteins ; Nanoparticle ; Small-angle X-ray scattering ; Single chain ; Protein Data Bank (RCSB PDB) ; Computer science ; Hydrodynamic radius ; Biological system ; Exponent ; Materials science ; Scattering ; Chemistry ; Dynamic light scattering ; Physics ; Nanotechnology ; Optics ; Stereochemistry Machine Learning in Materials Science ; Advanced Polymer Synthesis and Characterization ; Computational Drug Discovery Methods
Authors: David F. Nippa | Kenneth Atz | Remo Hohler | Alex T. Müller | Andreas Marx | Christian Bartelmus | Georg Wuitschik | Irene Marzuoli | Vera Jost | Jens Wolfard | Martin Binder | Antonia F. Stepan | David B. Konrad | Uwe Grether | Rainer E. Martin | Gisbert Schneider
Source: ChemRxiv (preprint, 2022-11-15)
Field: Materials Science — Materials Chemistry

Enabling late-stage drug diversification by high-throughput experimentation with geometric deep learning Late-stage functionalization (LSF) is an economical approach to optimize the properties of drug candidates. However, the chemical complexity of drug molecules often makes late-stage diversification challenging. To address this problem, an LSF platform based on geometric deep learning and high-throughput reaction screening was developed. Considering borylation as a critical step in LSF, the computational model predicted reaction yields for diverse reaction conditions with a mean absolute error margin of 4–5%, while the reactivity of novel reactions with known and unknown substrates were classified with a balanced accuracy of 92% and 67%, respectively. The regioselectivity of the major products was accurately captured in up to 90% of the cases studied. When applied to 23 diverse commercial drug molecules, the platform successfully identified numerous opportunities for structural diversification. The influence of steric and quantum mechanical information on model performance was quantified and a new comprehensive simple user-friendly reaction format (SURF) is introduced which proved to be a key enabler for seamlessly integrating deep learning and high-throughput experimentation (HTE) for LSF. Computer science ; Diversification (marketing strategy) ; Throughput ; Artificial intelligence ; Deep learning ; Machine learning Machine Learning in Materials Science ; Chemical Synthesis and Analysis ; Computational Drug Discovery Methods
Authors: Mario Barbatti | Mattia Bondanza | Rachel Crespo‐Otero | Baptiste Démoulin | Pavlo O. Dral | Giovanni Granucci | Fábris Kossoski | Hans Lischka | Benedetta Mennucci | Saikat Mukherjee | Marek Pederzoli | Maurizio Persico | Max Pinheiro | Jiřı́ Pittner | Felix Plasser | Eduarda Sangiogo Gil | Lijljana Stojanovic
Source: ChemRxiv (preprint, 2022-08-11)
Field: Materials Science — Materials Chemistry

The Newton-X platform: new software developments for surface hopping and nuclear ensembles Newton-X is an open-source computational platform to perform nonadiabatic molecular dynamics based on surface hopping and spectrum simulations using the nuclear ensemble approach. Both are among the most common methodologies in computational chemistry for photophysical and photochemical investigations. This paper describes the main features of these methods and how they are implemented in Newton-X. It emphasizes the newest developments, including zero-point-energy leakage correction, dynamics on complex-valued potential energy surfaces, dynamics induced by incoherent light, dynamics based on machine-learning potentials, exciton dynamics of multiple chromophores, and supervised and unsupervised machine learning techniques. Newton-X is interfaced with several third-party quantum-chemistry programs, spanning a broad spectrum of electronic structure methods. Surface hopping ; Exciton ; Chromophore ; Molecular dynamics ; Computer science ; Statistical physics ; Quantum ; Broad spectrum ; Quantum chemistry ; Dynamics (music) ; Physics ; Chemistry ; Quantum mechanics ; Molecule Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Molecular spectroscopy and chirality
Authors: Marko Melander | Tongwei Wu | Karoliina Honkala
Source: ChemRxiv (preprint, 2022-11-07)
Field: Materials Science — Materials Chemistry

Constant inner potential DFT for modelling electrochemical systems under constant potential and bias Electrochemical interfaces and reactions play a decisive role in \textit{e.g.} clean energy conversion but understanding their complex chemistry remains an outstanding challenge. Constant potential or grand canonical ensemble (GCE) simulations are indispensable for unraveling the properties of electrochemical processes as a function of the electrode potential. Currently, GCE calculations at the density functional theory (DFT) level are carried out by fixing the Fermi level of the simulation cell. Here we show that this method is inadequate for modelling semiconductor electrodes, outer sphere reactions, and a biased two-electrode cell; for these systems the Fermi level obtained from DFT calculations does not reflect the experimentally controlled electrode potential or describe the thermodynamic independent variable in GCE-DFT. To remedy this deficiency, we developed and implemented a constant inner potential (CIP) method as a more robust and general approach to carry out GCE-DFT simulations of electrochemical systems under constant potential or bias conditions. In CIP-DFT the electrode inner potential and hence the thermodynamically relevant electron bath electrochemical are directly controlled which makes the method widely applicable in simulating electrochemical interfaces. We demonstrate that the CIP and Fermi level GCE-DFT approaches are equivalent for metallic electrodes and inner-sphere reactions but CIP is also applicable to systems for which the constant Fermi level approach fails. A key advantage of CIP is that, unlike the Fermi level method, it does not require any electronic structure information. This is because only the inner potential of the systems is needed, CIP is also more compatible with classical force field or machine learning potentials. Altogether, the CIP approach emerges as a general and efficient GCE-DFT method to simulate (photo)electrochemical interfaces from first principles. Electrochemistry ; Density functional theory ; Electrode ; Fermi level ; Constant (computer programming) ; Electrode potential ; Standard electrode potential ; Fermi energy ; Fermi Gamma-ray Space Telescope ; Electrochemical potential ; Chemistry ; Chemical physics ; Materials science ; Physics ; Condensed matter physics ; Electron ; Physical chemistry ; Computational chemistry ; Quantum mechanics ; Computer science Machine Learning in Materials Science ; Electrochemical Analysis and Applications ; Surface and Thin Film Phenomena
Authors: Kenechi Nwosu‐Obieogu | Goziya W. Dzarma | Goodnews Christian | Ude Callistus Nonso | Allen Maureen Awele | Okoye Obuora Anozie
Source: Cleaner Waste Systems (article, 2022-07-01)
Field: Materials Science — Polymers and Plastics

Physico-chemical/mechanical properties of treated groundnut shell fibre; Response surface methodology and artificial neural network performance evaluation and optimisation The difficulties associated with forecasting mechanical behaviours of fibres based on the theoretical method is a big threat to precise design when dealing with complex variables; hence, the need to evaluate the fibres' properties using artificial intelligence techniques to minimise error. This study predicted the effect of acetylation process parameters (treatment time and concentration) on the mechanical properties (elongation at break and tensile strength) of groundnut shell fibre (GSF) using Response surface methodology (RSM) and Artificial neural network (ANN). The GSF was processed, dried and treated with NaOH varying the time and concentration of the treatment and then acetylated. The characterisation results from the physicochemical analysis, SEM and FTIR indicated that acetylation treatment removed excess lignin, hemicellulose and cellulose from GSF and improve its bonding characteristics. Assessment of the models through the coefficient of determination (R2) and mean square error (MSE) indicates that ANN for elongation at break (R2 =0.9999, MSE=2.769E-13) and tensile strength (R2 =0.99997, MSE=3.5146E-18) is superior to the RSM model for elongation at break (R2 =0.9264, MSE 1.59) and for tensile strength (R2 =0.9326, MSE=7.71) in predicting mechanical properties of GSF. Numerical optimisation revealed that optimal values of 3 h and treatment concentration of 3% gave a maximum percentage elongation at break of 10.583% and 135.045 MPa for tensile strength. This result shows that acetylation treatment enhanced the physicochemical/mechanical properties of GSF thereby making it suitable for composite processing. Ultimate tensile strength ; Response surface methodology ; Hemicellulose ; Elongation ; Mean squared error ; Cellulose ; Materials science ; Artificial neural network ; Composite number ; Lignin ; Central composite design ; Coefficient of determination ; Composite material ; Biological system ; Mathematics ; Computer science ; Chemical engineering ; Chemistry ; Machine learning ; Engineering ; Statistics Natural Fiber Reinforced Composites ; Advanced Cellulose Research Studies ; Agricultural Engineering and Mechanization
Authors: Cheng Xie | Ziwen Pan | Chao Shu
Source: Crystals (article, 2022-03-27)
Field: Materials Science — Materials Chemistry

Microstructure Representation Knowledge Graph to Explore the Twinning Formation Deformation twinning is an important mechanism of the plastic deformation of materials. The density of twins also affects the properties of the material. At present, the research methods of deformation twinning mainly depend on in situ EBSD, numerically investigated analysis and the finite element method. The application of machine learning methods to material microstructure research can shorten the time taken for material analysis. Machine learning methods are faced with the problem of the effective representation of the microstructure. We present a deformation twinning research method based on the representation of grain morphology features in a knowledge graph. We construct an autoencoder to extract grain morphology characteristics for building a grain knowledge graph. Then, a graph convolutional network (GCN) and fully connected network are developed to extract grain knowledge graph features and predict the twin density of materials subjected to specific tensile deformation. We use Mg-2Zn-3Li alloy as an experimental example to predict the twin density on three indexes of average grain size, twin boundaries density and average grain surface. The R2 score of the prediction result on the twin boundaries density is up to 0.510, and the R2 score of the average grain size and average grain surface is over 0.750. Therefore, the proposed method for deformation twinning research is effective and feasible. Crystal twinning ; Materials science ; Microstructure ; Graph ; Electron backscatter diffraction ; Deformation (meteorology) ; Grain boundary ; Computer science ; Composite material ; Theoretical computer science Machine Learning in Materials Science ; Corrosion Behavior and Inhibition ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Andrea Di Gioacchino | Jonah Procyk | Marco Molari | John S. Schreck | Yu Zhou | Yan Liu | Rémi Monasson | Simona Cocco | Petr Šulc
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2022-03-14)
Field: Materials Science — Materials Chemistry

Generative and interpretable machine learning for aptamer design and analysis of in vitro sequence selection Abstract Selection protocols such as SELEX, where molecules are selected over multiple rounds for their ability to bind to a target molecule of interest, are popular methods for obtaining binders for diagnostic and therapeutic purposes. With the increasing amount of such high-throughput experimental data available, machine learning techniques have become increasingly popular for molecular datasets analysis. Here, we show that Restricted Boltzmann Machines (RBMs), a two-layer neural network architecture, can successfully be trained on sequence ensembles from SELEX experiments for thrombin aptamers, and used to estimate the fitness of the sequences obtained through the experimental protocol. As a direct consequence, we show that trained RBMs can be exploited to classify as well as generate novel molecules. To confirm our findings, we experimentally verify the generated sequences from RBM. Aptamer ; Selection (genetic algorithm) ; Systematic evolution of ligands by exponential enrichment ; Artificial intelligence ; Computer science ; Machine learning ; Sequence (biology) ; Artificial neural network ; Biology ; Genetics ; Gene Machine Learning in Materials Science ; Protein Structure and Dynamics ; Nanopore and Nanochannel Transport Studies
Authors: Thomas Linker | Ken‐ichi Nomura | Shogo Fukushima | Rajiv K. Kalia | Aravind Krishnamoorthy | Aiichiro Nakano | Kohei Shimamura | Fuyuki Shimojo | Priya Vashishta
Source: The Journal of Physical Chemistry Letters (article, 2022-12-01)
Field: Materials Science — Materials Chemistry

Squishing Skyrmions: Symmetry-Guided Dynamic Transformation of Polar Topologies Under Compression Mechanical controllability of recently discovered topological defects (<i>e.g</i>., skyrmions) in ferroelectric materials is of interest for the development of ultralow-power mechano-electronics that are protected against thermal noise. However, fundamental understanding is hindered by the "multiscale quantum challenge" to describe topological switching encompassing large spatiotemporal scales with quantum mechanical accuracy. Here, we overcome this challenge by developing a machine-learning-based multiscale simulation framework─a hybrid neural network quantum molecular dynamics (NNQMD) and molecular mechanics (MM) method. For nanostructures composed of SrTiO<sub>3</sub> and PbTiO<sub>3</sub>, we find how the symmetry of mechanical loading essentially controls polar topological switching. We find under <i>symmetry-breaking</i> uniaxial compression a squishing-to-annihilation pathway versus formation of a topological composite named skyrmionium under <i>symmetry-preserving</i> isotropic compression. The distinct pathways are explained in terms of the underlying materials' elasticity and symmetry, as well as the Landau-Lifshitz-Kittel scaling law. Such rational control of ferroelectric topologies will likely facilitate exploration of the rich ferroelectric "topotronics" design space. Ferroelectricity ; Topology (electrical circuits) ; Skyrmion ; Topological defect ; Controllability ; Quantum ; Symmetry breaking ; Materials science ; Isotropy ; Symmetry (geometry) ; Physics ; Condensed matter physics ; Quantum mechanics ; Mathematics ; Geometry Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Advanced Electron Microscopy Techniques and Applications
Authors: Magnus Kløve | Sanna Sommer | Bo B. Iversen | Bjørk Hammer | Wilke Dononelli
Source: arXiv (Cornell University) (preprint, 2022-09-03)
Field: Materials Science — Materials Chemistry

Machine learning based approach for solving atomic structures of nanomaterials combining pair distribution functions with density functional theory Determination of crystal structures of nanocrystalline or amorphous compounds is a great challenge in solid states chemistry and physics. Pair distribution function (PDF) analysis of X-Ray or neutron total scattering data has proven to be a key element in tackling this challenge. However, in most cases a reliable structural motif is needed as starting configuration for structure refinements. Here, we present an algorithm that is able to determine the crystal structure of an unknown compound by means of an on-the-fly trained machine learning model that combines density functional theory (DFT) calculations with comparison of calculated and measured PDFs for global optimization in an artificial landscape. Due to the nature of this landscape, even metastable configurations can be determined. Pair distribution function ; Density functional theory ; Metastability ; Nanocrystalline material ; Crystal structure ; Crystal structure prediction ; Nanomaterials ; Amorphous solid ; Neutron scattering ; Statistical physics ; Materials science ; Physics ; Scattering ; Computer science ; Computational chemistry ; Chemistry ; Nanotechnology ; Crystallography ; Quantum mechanics X-ray Diffraction in Crystallography ; Machine Learning in Materials Science ; Crystal Structures and Properties
Authors: Lei Tao | Tom Arbaugh | John Byrnes | Vikas Varshney | Ying Li
Source: STAR Protocols (article, 2022-11-22)
Field: Materials Science — Materials Chemistry

Unified machine learning protocol for copolymer structure-property predictions Structure-property relationships are extremely valuable when predicting the properties of polymers. This protocol demonstrates a step-by-step approach, based on multiple machine learning (ML) architectures, which is capable of processing copolymer types such as alternating, random, block, and gradient copolymers. We detail steps for necessary software installation and construction of datasets. We further describe training and optimization steps for four neural network models and subsequent model visualization and comparison using training and test values. For complete details on the use and execution of this protocol, please refer to Tao et al. (2022).<sup>1</sup>. Computer science ; Protocol (science) ; Property (philosophy) ; Copolymer ; Artificial neural network ; Visualization ; Block (permutation group theory) ; Software ; Artificial intelligence ; Machine learning ; Polymer ; Materials science ; Mathematics ; Programming language Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Fuel Cells and Related Materials
Authors: Chong Li | Chao Liang | Yilimiranmu Rouzhahong | Biao Wang | Huashan Li
Source: Science China Materials (article, 2022-09-06)
Field: Materials Science — Materials Chemistry

Transferable prediction of intermolecular coupling achieved by hierarchical material representation Nanoporous ; Materials science ; Representation (politics) ; Intermolecular force ; Transferability ; Robustness (evolution) ; Ab initio ; Chemical physics ; Molecule ; Statistical physics ; Coupling (piping) ; Inverse ; Computer science ; Biological system ; Molecular physics ; Machine learning ; Nanotechnology ; Physics ; Quantum mechanics ; Chemistry ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions