jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jingbai Li | Patrick Reiser | André Eberhard | Pascal Friederich | Steven R. López
Source: ChemRxiv (preprint, 2020-10-05)
Field: Materials Science — Materials Chemistry

Nanosecond Photodynamics Simulations of a Cis-Trans Isomerization Are Enabled by Machine Learning &lt;p&gt;Photochemical reactions are being increasingly used to construct complex molecular architectures with mild and straightforward reaction conditions. Computational techniques are increasingly important to understand the reactivities and chemoselectivities of photochemical isomerization reactions because they offer molecular bonding information along the excited-state(s) of photodynamics. These photodynamics simulations are resource-intensive and are typically limited to 1–10 picoseconds and 1,000 trajectories due to high computational cost. Most organic photochemical reactions have excited-state lifetimes exceeding 1 picosecond, which places them outside possible computational studies. Westermeyr &lt;i&gt;et al.&lt;/i&gt; demonstrated that a machine learning approach could significantly lengthen photodynamics simulation times for a model system, methylenimmonium cation (CH&lt;sub&gt;2&lt;/sub&gt;NH&lt;sub&gt;2&lt;/sub&gt;&lt;sup&gt;+&lt;/sup&gt;).&lt;/p&gt;&lt;p&gt;We have developed a Python-based code, Python Rapid Artificial Intelligence &lt;i&gt;Ab Initio&lt;/i&gt; Molecular Dynamics (PyRAI&lt;sup&gt;2&lt;/sup&gt;MD), to accomplish the unprecedented 10 ns &lt;i&gt;cis-trans&lt;/i&gt; photodynamics of &lt;i&gt;trans&lt;/i&gt;-hexafluoro-2-butene (CF&lt;sub&gt;3&lt;/sub&gt;–CH=CH–CF&lt;sub&gt;3&lt;/sub&gt;) in 3.5 days. The same simulation would take approximately 58 years with ground-truth multiconfigurational dynamics. We proposed an innovative scheme combining Wigner sampling, geometrical interpolations, and short-time quantum chemical trajectories to effectively sample the initial data, facilitating the adaptive sampling to generate an informative and data-efficient training set with 6,232 data points. Our neural networks achieved chemical accuracy (mean absolute error of 0.032 eV). Our 4,814 trajectories reproduced the S&lt;sub&gt;1&lt;/sub&gt; half-life (60.5 fs), the photochemical product ratio (&lt;i&gt;trans&lt;/i&gt;: &lt;i&gt;cis&lt;/i&gt; = 2.3: 1), and autonomously discovered a pathway towards a carbene. The neural networks have also shown the capability of generalizing the full potential energy surface with chemically incomplete data (&lt;i&gt;trans&lt;/i&gt; → &lt;i&gt;cis&lt;/i&gt; but not &lt;i&gt;cis&lt;/i&gt; → &lt;i&gt;trans&lt;/i&gt; pathways) that may offer future automated photochemical reaction discoveries.&lt;/p&gt; Isomerization ; Chemistry ; Picosecond ; Molecular dynamics ; Excited state ; Photochemistry ; Computational chemistry ; Physics ; Atomic physics ; Catalysis Machine Learning in Materials Science ; Scientific Computing and Data Management ; Catalysis and Oxidation Reactions
Authors: Philomena Schlexer | Tej S. Choksi | Verena Streibel | Frank Abild‐Pedersen
Source: arXiv (Cornell University) (preprint, 2020-05-06)
Field: Materials Science — Materials Chemistry

Artificial intelligence real-time prediction and physical interpretation of atomic binding energies in nano-scale metal clusters Single atomic sites often determine the functionality and performance of materials, such as catalysts, semi-conductors or enzymes. Computing and understanding the properties of such sites is therefore a crucial component of the rational materials design process. Because of complex electronic effects at the atomic level, atomic site properties are conventionally derived from computationally expensive first-principle calculations, as this level of theory is required to achieve relevant accuracy. In this study, we present a widely applicable machine learning (ML) approach to compute atomic site properties with high accuracy in real time. The approach works well for complex non-crystalline atomic structures and therefore opens up the possibility for high-throughput screenings of nano-materials, amorphous systems and materials interfaces. Our approach includes a robust featurization scheme to transform atomic structures into features which can be used by common machine learning models. Performing a genetic algorithm (GA) based feature selection, we show how to establish an intuitive physical interpretation of the structure-property relations implied by the ML models. With this approach, we compute atomic site stabilities of metal nanoparticles ranging from 3-55 atoms with mean absolute errors in the range of 0.11-0.14 eV in real time. We also establish the chemical identity of the site as most important factor in determining atomic site stabilities, followed by structural features like bond distances and angles. Both, the featurization and GA feature selection functionality are published in open-source python modules. With this method, we enable the efficient rational design of highly specialized real-world nano-catalysts through data-driven materials screening. Atomic units ; Computer science ; Python (programming language) ; Atomic model ; Artificial intelligence ; Materials science ; Nanotechnology ; Algorithm ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Quantum Dots Synthesis And Properties
Authors: AkshatKumar Nigam | Robert Pollice | Mario Krenn | Gabriel dos Passos Gomes | Alán Aspuru‐Guzik
Source: ChemRxiv (preprint, 2020-12-15)
Field: Materials Science — Materials Chemistry

Beyond Generative Models: Superfast Traversal, Optimization, Novelty, Exploration and Discovery (STONED) Algorithm for Molecules using SELFIES Inverse design allows the design of molecules with desirable properties using property optimization. Deep generative models have recently been applied to tackle inverse design, as they possess the ability to optimize molecular properties directly through structure modification using gradients. While the ability to carry out direct property optimizations is promising, the use of generative deep learning models to solve practical problems requires large amounts of data and is very time-consuming. In this work, we propose STONED – a simple and efficient algorithm to perform interpolation and exploration in the chemical space, comparable to deep generative models. STONED bypasses the need for large amounts of data and training times by using string modifications in the SELFIES molecular representation. We achieve comparable performance on typical benchmarks without any training. We demonstrate applications in high-throughput virtual screening for the design of drugs, photovoltaics, and the construction of chemical paths, allowing for both property and structure-based interpolation in the chemical space. We anticipate our results to be a stepping stone for developing more sophisticated inverse design models and benchmarking tools, ultimately helping generative models achieve wide adoption. Computer science ; Chemical space ; Generative grammar ; Deep learning ; Generative model ; Toolbox ; Artificial intelligence ; Machine learning ; Algorithm ; Theoretical computer science ; Drug discovery ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Anton Vladyka | Tim Albrecht
Source: Machine Learning Science and Technology (preprint, 2020-07-17)
Field: Materials Science — Materials Chemistry

Unsupervised classification of single-molecule data with autoencoders and transfer learning Abstract Datasets from single-molecule experiments often reflect a large variety of molecular behaviour. The exploration of such datasets can be challenging, especially if knowledge about the data is limited and a priori assumptions about expected data characteristics are to be avoided. Indeed, searching for pre-defined signal characteristics is sometimes useful, but it can also lead to information loss and the introduction of expectation bias. Here, we demonstrate how Transfer Learning-enhanced dimensionality reduction can be employed to identify and quantify hidden features in single-molecule charge transport data, in an unsupervised manner. Taking advantage of open-access neural networks trained on millions of seemingly unrelated image data, our results also show how Deep Learning methodologies can readily be employed, even if the amount of problem-specific, ‘own’ data is limited. Transfer of learning ; Computer science ; Dimensionality reduction ; Artificial intelligence ; Machine learning ; Unsupervised learning ; Curse of dimensionality ; A priori and a posteriori ; Variety (cybernetics) ; Artificial neural network ; Pattern recognition (psychology) ; Deep learning Machine Learning in Materials Science ; Molecular Junctions and Nanostructures ; Protein Structure and Dynamics
Authors: Tomáš Bučko | Monika Gešvandtnerová | Dario Rocca
Source: ChemRxiv (preprint, 2020-05-14)
Field: Materials Science — Materials Chemistry

Ab initio calculations of free energy of activation at multiple electronic structure levels made affordable: An effective combination of perturbation theory and machine learning &lt;div&gt;While free energies are fundamental thermodynamic quantities to characterize chemical reactions, their calculation based on ab initio theory is usually limited by the high computational cost. This is particularly true if multiple levels of theory have to be tested to establish their relative accuracy, if highly expensive quantum mechanical approximations are of interest, and also if several different temperatures have to be considered. We present an ab initio approach that effectively couples perturbation theory and machine learning to make ab initio free energy calculations more affordable. Starting from results based on a certain production ab initio theory, perturbation theory is applied to obtain free energies. The large number of single point calculations required by a brute force application of this approach are here significantly decreased by applying machine learning techniques. Importantly, the training of the machine learning model requires only a small amount of data and does not need to be performed again when the temperature is decreased. The accuracy and efficiency of this method is demonstrated by computing the free energy of activation of the proton exchange reaction in the zeolite chabazite. Starting from an ab initio calculation based on a semilocal approximation of density functional theory, free energies based on significantly more expensive non-local van der Waals and hybrid functionals are obtained with only a few tens of additional single point calculations. In this way this work paves the route to quick free energy calculations using different levels of theory or approximations that would be too computationally expensive to be directly employed in molecular dynamics or Monte Carlo simulations.&lt;/div&gt; Ab initio ; Perturbation theory (quantum mechanics) ; Density functional theory ; van der Waals force ; Ab initio quantum chemistry methods ; Statistical physics ; Chemistry ; Physics ; Computational chemistry ; Quantum mechanics ; Molecule Machine Learning in Materials Science ; Advanced Physical and Chemical Molecular Interactions ; Advanced Chemical Physics Studies
Authors: Maximilian Länge
Source: Soft Computing (article, 2020-01-22)
Field: Materials Science — Materials Chemistry

An artificial neural network model for the unary description of pure substances and its application on the thermodynamic modelling of pure iron Abstract The aim of this work is to introduce a novel approach for the universal description of the thermodynamic functions of pure substances on the basis of artificial neural networks. The proposed approximation method is able to describe the thermodynamic functions ( $$C_{p}(T), S(T), H(T)-H(T_{\mathrm{ref}}), G(T)$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:msub> <mml:mi>C</mml:mi> <mml:mi>p</mml:mi> </mml:msub> <mml:mrow> <mml:mo>(</mml:mo> <mml:mi>T</mml:mi> <mml:mo>)</mml:mo> </mml:mrow> <mml:mo>,</mml:mo> <mml:mi>S</mml:mi> <mml:mrow> <mml:mo>(</mml:mo> <mml:mi>T</mml:mi> <mml:mo>)</mml:mo> </mml:mrow> <mml:mo>,</mml:mo> <mml:mi>H</mml:mi> <mml:mrow> <mml:mo>(</mml:mo> <mml:mi>T</mml:mi> <mml:mo>)</mml:mo> </mml:mrow> <mml:mo>-</mml:mo> <mml:mi>H</mml:mi> <mml:mrow> <mml:mo>(</mml:mo> <mml:msub> <mml:mi>T</mml:mi> <mml:mi>ref</mml:mi> </mml:msub> <mml:mo>)</mml:mo> </mml:mrow> <mml:mo>,</mml:mo> <mml:mi>G</mml:mi> <mml:mrow> <mml:mo>(</mml:mo> <mml:mi>T</mml:mi> <mml:mo>)</mml:mo> </mml:mrow> </mml:mrow> </mml:math> ) of the different phases of unary material systems in a wide temperature range (between 0 and 6000 K). Phase transition temperatures and the respective enthalpies of transformation, which are computationally determined by the minimization of the Gibbs free energy, are also approximated. This is achieved by using artificial neural networks as models for the thermodynamic functions of the individual phases and by expressing the thermodynamic quantities in terms of the free network parameters. The resulting expressions are then optimized with machine learning algorithms on the basis of measurement data. A physical basis for the resulting approximation is given by the use of, among others, Planck–Einstein functions as activation function of the neurons of the network. This article provides a description of the method and as an example of a specific application the approximation of the thermodynamic functions of the different phases of pure iron. The article focuses on the problem of the representation of thermodynamic data and their practical application. Algorithm ; Materials science ; Computer science Material Properties and Failure Mechanisms ; Machine Learning in Materials Science ; Metallurgical Processes and Thermodynamics
Authors: Navid Shervani-Tabar | Nicholas Zabaras
Source: arXiv (Cornell University) (preprint, 2020-09-29)
Field: Materials Science — Materials Chemistry

Physics-Constrained Predictive Molecular Latent Space Discovery with Graph Scattering Variational Autoencoder Recent advances in artificial intelligence have propelled the development of innovative computational materials modeling and design techniques. Generative deep learning models have been used for molecular representation, discovery, and design. In this work, we assess the predictive capabilities of a molecular generative model developed based on variational inference and graph theory in the small data regime. Physical constraints that encourage energetically stable molecules are proposed. The encoding network is based on the scattering transform with adaptive spectral filters to allow for better generalization of the model. The decoding network is a one-shot graph generative model that conditions atom types on molecular topology. A Bayesian formalism is considered to capture uncertainties in the predictive estimates of molecular properties. The model's performance is evaluated by generating molecules with desired target properties. Autoencoder ; Generative model ; Molecular graph ; Inference ; Generative grammar ; Computer science ; Artificial intelligence ; Graph ; Machine learning ; Decoding methods ; Formalism (music) ; Theoretical computer science ; Algorithm ; Artificial neural network ; Topology (electrical circuits) ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Yang Deng | Simiao Ren | Kebin Fan | Jordan M. Malof | Willie J. Padilla
Source: arXiv (Cornell University) (preprint, 2020-12-09)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Neural-adjoint method for the inverse design of all-dielectric metasurfaces All-dielectric metasurfaces exhibit exotic electromagnetic responses, similar to those obtained with metal-based metamaterials. Research in all-dielectric metasurfaces currently uses relatively simple unit-cell designs, but increased geometrical complexity may yield even greater scattering states. Although machine learning has recently been applied to the design of metasurfaces with impressive results, the much more challenging task of finding a geometry that yields the desired spectra remains largely unsolved. We explore and adapt a recent deep learning approach -- termed neural-adjoint -- and find it is capable of accurately and efficiently estimating complex geometry needed to yield a targeted frequency-dependent scattering. We also show how the neural-adjoint method can intelligently grow the design search space to include designs that increasingly and accurately approximate the desired scattering response. The neural-adjoint method is not restricted to the case demonstrated and may be applied to plasmonics, photonic bandgap, and other structured material systems. Metamaterial ; Artificial neural network ; Plasmon ; Computer science ; Dielectric ; Simple (philosophy) ; Scattering ; Electronic engineering ; Physics ; Artificial intelligence ; Optics ; Engineering ; Optoelectronics Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Millimeter-Wave Propagation and Modeling
Authors: Vivek Sinha | Jochem Jan Laan | Evgeny A. Pidko
Source: ChemRxiv (preprint, 2020-10-14)
Field: Materials Science — Materials Chemistry

Accurate and Rapid Prediction of pKa of Transition Metal Complexes: Semiempirical Quantum Chemistry with a DataAugmented Approach &lt;div&gt; &lt;p&gt;Rapid and accurate prediction of reactivity descriptors of transition metal (TM) complexes is a major challenge for contemporary quantum chemistry. Recently developed GFN2-xTB method based on the density functional tight-binding theory (DFT-B) is suitable for high-throughput calculation of geometries and thermochemistry for TM complexes albeit with a moderate accuracy. Herein we present a data-augmented approach to improve substantially the accuracy of GFN2-xTB for the prediction of thermochemical properties using pK&lt;sub&gt;a&lt;/sub&gt; values of TM hydrides as a representative model example. We constructed a comprehensive database for ca. 200 TM hydride complexes featuring the experimentally measured pK&lt;sub&gt;a&lt;/sub&gt;’s as well as the GFN2-xTB optimized geometries and various computed electronic and energetic descriptors. The GFN2-xTB results were further refined and validated by DFT calculations with the hybrid PBE0 functional. Our results show that although the GFN2-xTB performs well in most cases, it fails to adequately desribe TM complexes featuring multicarbonyl and multihydride ligand environments. The dataset was analyzed with the partial least squares (OLS) fitting and was used to construct an automated machine learning (AutoML) approach for the rapid estimation of pK&lt;sub&gt;a&lt;/sub&gt; of TM hydride complexes. The results obtained show a high predictive power of the very fast AutoML model (RMSE ~ 2.7) comparable to that of the much slower DFT calculations (RMSE ~ 3). The presented data-augmented quantum chemistry-based approach is promising for high-throughput computational screening workflows of homogeneous TM-based catalysts.&lt;/p&gt; &lt;/div&gt; &lt;br&gt; Thermochemistry ; Hydride ; Density functional theory ; Chemistry ; Transition metal ; Reactivity (psychology) ; Quantum chemistry ; Mean squared error ; Quantum ; Computational chemistry ; Quantum chemical ; Metal ; Molecule ; Physical chemistry ; Mathematics ; Physics ; Electrochemistry ; Quantum mechanics ; Statistics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemistry and Chemical Engineering
Authors: Xing Tong | Tong Xue
Source: Materials science forum (article, 2020-07-10)
Field: Materials Science — Materials Chemistry

Modeling of Annealing Heat Treatment Parameters for Zr Alloy Tube by ANN-Ga In this paper, there are tow part of module for predicting the Annealing heat treatments of Zr tube. The artificial neural network (ANN) were used for relationship between mechanical property and annealing parameters. The genetic algorithm (GA) were used for Annealing heat treatments of Zr tube. The best ANN network architecture is 2-8-3, and the optimum values of momentum factor is 0.8 while the Crossover is also 0.8 by ANN-GA, which can be efficiently track the effect of annealing Heat treatment on properties for Zr-4 alloy. Keywords: Zr alloy, Heat Treatment, mechanical propert Annealing (glass) ; Materials science ; Alloy ; Simulated annealing ; Artificial neural network ; Crossover ; Composite material ; Metallurgy ; Computer science ; Artificial intelligence ; Algorithm Nuclear Materials and Properties ; Metallurgy and Material Forming ; Powder Metallurgy Techniques and Materials
Authors: Bing Huang | O. Anatole von Lilienfeld
Source: arXiv (Cornell University) (preprint, 2020-08-12)
Field: Materials Science — Materials Chemistry

Dictionary of 140k GDB and ZINC derived AMONs We present all {\bf A}mons for {\bf G}DB and {\bf Z}inc data-bases using no more than 7 non-hydrogen atoms (AGZ7)---a calculated organic chemistry building-block dictionary based on the AMON approach [Huang and von Lilienfeld, {\em Nature Chemistry} (2020)]. AGZ7 records Cartesian coordinates of compositional and constitutional isomers, as well as properties for $\sim$140k small organic molecules obtained by systematically fragmenting all molecules of Zinc and the majority of GDB17 into smaller entities, saturating with hydrogens, and containing no more than 7 heavy atoms (excluding hydrogen atoms). AGZ7 cover the elements \{H, B, C, N, O, F, Si, P, S, Cl, Br, Sn and I\} and includes optimized geometries, total energy and its decomposition, Mulliken atomic charges, dipole moment vectors, quadrupole tensors, electronic spatial extent, eigenvalues of all occupied orbitals, LUMO, gap, isotropic polarizability, harmonic frequencies, reduced masses, force constants, IR intensity, normal coordinates, rotational constants, zero-point energy, internal energy, enthalpy, entropy, free energy, and heat capacity (all at ambient conditions) using B3LYP/cc-pVTZ (pseudopotentials were used for Sn and I) level of theory. We exemplify the usefulness of this data set with AMON based machine learning models of total potential energy predictions of seven of the most rigid GDB-17 molecules. Chemistry ; Dipole ; Polarizability ; Atomic orbital ; Quadrupole ; Atomic physics ; Molecule ; Computational chemistry ; Physics ; Quantum mechanics ; Organic chemistry ; Electron Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Alessandro Lunghi | Stefano Sanvito
Source: The Journal of Physical Chemistry Letters (preprint, 2020-07-15)
Field: Materials Science — Materials Chemistry

The Limit of Spin Lifetime in Solid-State Electronic Spins The development of spin qubits for quantum technologies requires their protection from the main source of finite-temperature decoherence: atomic vibrations. Here we eliminate one of the main barriers to the progress in this field by providing a complete first-principles picture of spin relaxation that includes up to two-phonon processes. Our method is based on machine learning and electronic structure theory and makes the prediction of spin lifetime in realistic systems feasible. We study a prototypical vanadium-based molecular qubit and reveal that the spin lifetime at high temperature is limited by Raman processes due to a small number of THz intramolecular vibrations. These findings effectively change the conventional understanding of spin relaxation in this class of materials and open new avenues for the rational design of long-living spin systems. Quantum decoherence ; Spin engineering ; Spin (aerodynamics) ; Qubit ; Spins ; Relaxation (psychology) ; Condensed matter physics ; Physics ; Quantum mechanics ; Quantum ; Spin polarization Machine Learning in Materials Science ; Molecular Junctions and Nanostructures ; Organic and Molecular Conductors Research