jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Vaneet Saini | Aditya Sharma | Dhruv Nivatia
Source: Physical Chemistry Chemical Physics (article, 2021-12-06)
Field: Chemistry — Organic Chemistry

A machine learning approach for predicting the nucleophilicity of organic molecules Nucleophilicity provides important information about the chemical reactivity of organic molecules. Experimental determination of the nucleophilicity parameter is a tedious and resource-intensive approach. Herein, we present a novel machine learning protocol that uses key structural descriptors to predict the nucleophilicities of organic molecules, which agree well with the experimental values. A data driven approach was used where quantum mechanical molecular and thermodynamic descriptors from a wide range of structurally diverse nucleophiles and relevant solvents were extracted and modelled using advanced algorithms against the experimentally available nucleophilicity values. Despite the structural diversity of nucleophiles, we are able to achieve statistically robust models with a high predictive power using tree-based and neural network algorithms trained on an in-house developed unique dataset consisting of 752 nucleophilicity values and 27 molecular descriptors. Nucleophile ; Artificial neural network ; Molecule ; Chemistry ; Reactivity (psychology) ; Artificial intelligence ; Machine learning ; Computer science ; Computational chemistry ; Biological system ; Organic chemistry Organic Chemistry Cycloaddition Reactions ; Chemical Reaction Mechanisms ; Computational Drug Discovery Methods
Authors: Amrish Kumar | Jayendran Iyer | Fatima Jalid | Manojkumar Ramteke | Tuhin Suvra Khan | M. Ali Haider
Source: ChemCatChem (article, 2021-11-18)
Field: Materials Science — Materials Chemistry

Machine Learning Enabled Screening of Single Atom Alloys: Predicting Reactivity Trend for Ethanol Dehydrogenation Abstract A machine learning (ML) approach implementing the gradient boosting regressor (GBR) algorithm is applied to predict the binding energies of oxygen (E O ) and carbon (E C ) atoms on single atom alloys (SAAs) of Cu, Ag and Au. Readily available periodic properties of the transition metals are utilized as input features in the model. Their relative contribution in adsorbate‐metal interaction is assessed to develop a comprehensive descriptor. In test runs, the ML model is observed to predict E O and E C with significantly reduced errors (∼0.2 eV). Further, ML approach is augmented with an ab initio microkinetic model (MKM) for non‐oxidative dehydrogenation (NODH) of ethanol. The ML‐MKM is calculated to yield higher turnover frequency for ethanol conversion on NiAu, NiAg, and PtAg SAAs as compared to their monometallic counterparts Au and Ag catalysts. Overall, the ML model provides a rationale for feature selection to synthesize catalytically active SAAs of group 11 elements using fast‐track in silico screening. Dehydrogenation ; Gradient boosting ; Chemistry ; Catalysis ; Yield (engineering) ; Atom (system on chip) ; Ethanol ; Transition metal ; Reactivity (psychology) ; Boosting (machine learning) ; Ab initio ; Computational chemistry ; Materials science ; Computer science ; Machine learning ; Organic chemistry ; Metallurgy Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Electrocatalysts for Energy Conversion
Authors: Changjiao Li | Hua Hao | Ben Xu | Zhonghui Shen | E Zhou | Dongbing Jiang | Hanxing Liu
Source: Computational Materials Science (article, 2021-07-14)
Field: Materials Science — Materials Chemistry

Improved physics-based structural descriptors of perovskite materials enable higher accuracy of machine learning Support vector machine ; Artificial intelligence ; Random forest ; Machine learning ; Valence (chemistry) ; Computer science ; Regression ; Materials science ; Mathematics ; Chemistry ; Statistics Machine Learning in Materials Science ; Perovskite Materials and Applications ; X-ray Diffraction in Crystallography
Authors: Huaping Li | Yuliang Jin | Ying Jiang | Jeff Z. Y. Chen
Source: Proceedings of the National Academy of Sciences (article, 2021-03-08)
Field: Materials Science — Materials Chemistry

Determining the nonequilibrium criticality of a Gardner transition via a hybrid study of molecular simulations and machine learning Apparent critical phenomena, typically indicated by growing correlation lengths and dynamical slowing down, are ubiquitous in nonequilibrium systems such as supercooled liquids, amorphous solids, active matter, and spin glasses. It is often challenging to determine if such observations are related to a true second-order phase transition as in the equilibrium case or simply a crossover and even more so to measure the associated critical exponents. Here we show that the simulation results of a hard-sphere glass in three dimensions are consistent with the recent theoretical prediction of a Gardner transition, a continuous nonequilibrium phase transition. Using a hybrid molecular simulation-machine learning approach, we obtain scaling laws for both finite-size and aging effects and determine the critical exponents that traditional methods fail to estimate. Our study provides an approach that is useful to understand the nature of glass transitions and can be generalized to analyze other nonequilibrium phase transitions. Non-equilibrium thermodynamics ; Statistical physics ; Phase transition ; Supercooling ; Criticality ; Scaling ; Glass transition ; Directed percolation ; Crossover ; Critical exponent ; Physics ; Amorphous solid ; Spin glass ; Critical phenomena ; Condensed matter physics ; Thermodynamics ; Chemistry ; Computer science ; Mathematics ; Polymer ; Machine learning Material Dynamics and Properties ; Theoretical and Computational Physics ; Phase Equilibria and Thermodynamics
Authors: CORDEIRO FONSECA, Gregory | POLTAVSKYI, Igor | VASSILEV GALINDO, Valentin | TKATCHENKO, Alexandre
Source: Open Repository and Bibliography (University of Luxembourg) (article, 2021-03-22)
Field: Materials Science — Materials Chemistry

Improving molecular force fields across configurational space by combining supervised and unsupervised machine learning peer reviewed Unsupervised learning ; Space (punctuation) ; Supervised learning ; Field (mathematics) ; Pattern recognition (psychology) ; Feature (linguistics) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Masashi Tsubaki | Teruyasu Mizoguchi
Source: Journal of Chemical Theory and Computation (article, 2021-11-30)
Field: Materials Science — Materials Chemistry

Quantum Deep Descriptor: Physically Informed Transfer Learning from Small Molecules to Polymers In this study, we propose a physically informed transfer learning approach for materials informatics (MI) using a quantum deep descriptor (QDD) obtained from the quantum deep field (QDF). The QDF is a machine learning model based on density functional theory (DFT) and can be trained with a large database of molecular properties. The pre-trained QDF model can provide an effective molecular descriptor that encodes the fundamental quantum-chemical characteristics (i.e., the wave function or orbital, electron density, and energies of a molecule) learned from the large database; we refer to this descriptor as a QDD. We show that a QDD pre-trained with certain properties of small molecules can predict different properties (e.g., the band gap and dielectric constant) of polymers compared with some existing descriptors. We believe that our DFT-based, physically informed transfer learning approach will not only be useful for practical applications in MI but will also provide quantum-chemical insights into materials in the future. All codes used in this study are available at https://github.com/masashitsubaki. Computer science ; Quantum ; Density functional theory ; Transfer of learning ; Wave function ; Artificial intelligence ; Function (biology) ; Deep learning ; Machine learning ; Quantum mechanics ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Software Engineering Research
Authors: Viktor Zaverkin | Julia Netz | Fabian Zills | Andreas Köhn | Johannes Kästner
Source: Journal of Chemical Theory and Computation (article, 2021-12-09)
Field: Materials Science — Materials Chemistry

Thermally Averaged Magnetic Anisotropy Tensors via Machine Learning Based on Gaussian Moments We propose a machine learning method to model molecular tensorial quantities, namely, the magnetic anisotropy tensor, based on the Gaussian moment neural network approach. We demonstrate that the proposed methodology can achieve an accuracy of 0.3-0.4 cm<sup>-1</sup> and has excellent generalization capability for out-of-sample configurations. Moreover, in combination with machine-learned interatomic potential energies based on Gaussian moments, our approach can be applied to study the dynamic behavior of magnetic anisotropy tensors and provide a unique insight into spin-phonon relaxation. Anisotropy ; Gaussian ; Tensor (intrinsic definition) ; Magnetic moment ; Moment (physics) ; Generalization ; Artificial neural network ; Statistical physics ; Magnetic anisotropy ; Computer science ; Isotropy ; Phonon ; Relaxation (psychology) ; Physics ; Artificial intelligence ; Condensed matter physics ; Magnetic field ; Magnetization ; Mathematics ; Classical mechanics ; Quantum mechanics ; Mathematical analysis Machine Learning in Materials Science ; Quantum many-body systems ; Spectroscopy and Quantum Chemical Studies
Authors: Yajun Wei | Jing Yu | Yonglin Du | Hongxu Li | Chia‐Hung Su
Source: Journal of Molecular Liquids (article, 2021-09-27)
Field: Chemistry — Inorganic Chemistry

Artificial intelligence simulation of Pb(II) and Cd(II) adsorption using a novel metal organic framework-based nanocomposite adsorbent Adsorption ; Kinetics ; Artificial neural network ; Metal ions in aqueous solution ; Nanocomposite ; Aqueous solution ; Materials science ; Ion ; Chemistry ; Computer science ; Nanotechnology ; Artificial intelligence ; Physical chemistry ; Organic chemistry ; Physics Metal-Organic Frameworks: Synthesis and Applications ; Adsorption and biosorption for pollutant removal ; Chemical Synthesis and Characterization
Authors: Esther Heid | Jiannan Liu | Andrea Aude | William H. Green
Source: Journal of Chemical Information and Modeling (article, 2021-12-23)
Field: Materials Science — Materials Chemistry

Influence of Template Size, Canonicalization, and Exclusivity for Retrosynthesis and Reaction Prediction Applications Heuristic and machine learning models for rank-ordering reaction templates comprise an important basis for computer-aided organic synthesis regarding both product prediction and retrosynthetic pathway planning. Their viability relies heavily on the quality and characteristics of the underlying template database. With the advent of automated reaction and template extraction software and consequently the creation of template databases too large for manual curation, a data-driven approach to assess and improve the quality of template sets is needed. We therefore systematically studied the influence of template generality, canonicalization, and exclusivity on the performance of different template ranking models. We find that duplicate and nonexclusive templates, i.e., templates which describe the same chemical transformation on identical or overlapping sets of molecules, decrease both the accuracy of the ranking algorithm and the applicability of the respective top-ranked templates significantly. To remedy the negative effects of nonexclusivity, we developed a general and computationally efficient framework to deduplicate and hierarchically correct templates. As a result, performance improved considerably for both heuristic and machine learning template ranking models, as well as multistep retrosynthetic planning models. The canonicalization and correction code is made freely available. Template ; Computer science ; Generality ; Ranking (information retrieval) ; Retrosynthetic analysis ; Heuristic ; Data mining ; Quality (philosophy) ; Artificial intelligence ; Software ; Transformation (genetics) ; Machine learning ; Programming language Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemical Synthesis and Analysis
Authors: Yinan Wang | Linfeng Zhang | Ben Xu | Xiaoyang Wang | Handong Wang
Source: Modelling and Simulation in Materials Science and Engineering (article, 2021-12-07)
Field: Materials Science — Materials Chemistry

A generalizable machine learning potential of Ag–Au nanoalloys and its application to surface reconstruction, segregation and diffusion Abstract Owing to the excellent catalytic properties of Ag–Au binary nanoalloys, nanostructured Ag–Au, such as Ag–Au nanoparticles and nanopillars, has been under intense investigation. To achieve high accuracy in molecular simulations of Ag–Au nanoalloys, the surface properties must be modeled with first-principles precision. In this work, we constructed a generalizable machine learning interatomic potential for Ag–Au nanoalloys based on deep neural networks trained from a database constructed with first-principles calculations. This potential is highlighted by the accurate prediction of Au (111) surface reconstruction and the segregation of Au toward the Ag–Au nanoalloy surface, where the empirical force field (EFF) failed in both cases. Moreover, regarding the adsorption and diffusion of adatoms on surfaces, the overall performance of our potential is better than the EFFs. We stress that the reported surface properties are blind to the potential modeling in the sense that none of the surface configurations is explicitly included in the training database; therefore, the reported potential is expected to have a strong generalization ability to a wide range of properties and to play a key role in investigating nanostructured Ag–Au evolution, where accurate descriptions of free surfaces are necessary. Materials science ; Nanopillar ; Diffusion ; Adsorption ; Surface (topology) ; Nanotechnology ; Binary number ; Interatomic potential ; Nanoparticle ; Molecular dynamics ; Force field (fiction) ; Chemical physics ; Nanostructure ; Computer science ; Artificial intelligence ; Thermodynamics ; Computational chemistry ; Physical chemistry Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; nanoparticles nucleation surface interactions
Authors: Zachary L. Glick | Alexios Koutsoukas | Daniel L. Cheney | C. David Sherrill
Source: The Journal of Chemical Physics (article, 2021-06-09)
Field: Materials Science — Materials Chemistry

Cartesian message passing neural networks for directional properties: Fast and transferable atomic multipoles The message passing neural network (MPNN) framework is a promising tool for modeling atomic properties but is, until recently, incompatible with directional properties, such as Cartesian tensors. We propose a modified Cartesian MPNN (CMPNN) suitable for predicting atom-centered multipoles, an essential component of ab initio force fields. The efficacy of this model is demonstrated on a newly developed dataset consisting of 46 623 chemical structures and corresponding high-quality atomic multipoles, which was deposited into the publicly available Molecular Sciences Software Institute QCArchive server. We show that the CMPNN accurately predicts atom-centered charges, dipoles, and quadrupoles and that errors in the predicted atomic multipoles have a negligible effect on multipole–multipole electrostatic energies. The CMPNN is accurate enough to model conformational dependencies of a molecule’s electronic structure. This opens up the possibility of recomputing atomic multipoles on the fly throughout a simulation in which they might exhibit strong conformational dependence. Multipole expansion ; Cartesian coordinate system ; Dipole ; Atom (system on chip) ; Ab initio ; Physics ; Artificial neural network ; Energetic neutral atom ; Computer science ; Atomic physics ; Quantum mechanics ; Ion ; Artificial intelligence ; Geometry ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Maksim Beliaev | Dana Zöllner | Alexandra Pacureanu | Paul Zaslansky | Igor Zlotnikov
Source: Nature Physics (article, 2021-01-04)
Field: Materials Science — Biomaterials

Dynamics of topological defects and structural synchronization in a forming periodic tissue Dissipative system ; Topological defect ; Physics ; Topology (electrical circuits) ; Kuramoto model ; Coherence (philosophical gambling strategy) ; Distortion (music) ; Synchronization (alternating current) ; Condensed matter physics Calcium Carbonate Crystallization and Inhibition ; Geology and Paleoclimatology Research ; Paleontology and Stratigraphy of Fossils
Authors: Qunchao Tong | Xiaoshan Luo | Adebayo A. Adeleke | Pengyue Gao | Yu Xie | Hanyu Liu | Quan Li | Yanchao Wang | Jian Lv | Yansun Yao | Yanming Ma
Source: Physical review. B./Physical review. B (article, 2021-02-09)
Field: Materials Science — Materials Chemistry

Machine learning metadynamics simulation of reconstructive phase transition Simulating reconstructive phase transition requires an accurate description of potential energy surface (PES). Density-functional-theory (DFT) based molecular dynamics can achieve the desired accuracy but it is computationally unfeasible for large systems and/or long simulation times. Here we introduce an approach that combines the metadynamics simulation and machine learning representation of PES at the accuracy close to the DFT calculations, but with the computational cost several orders of magnitude less, and scaling with system size approximately linear. The high accuracy of the method is demonstrated in the simulation of pressure-induced $B4\text{\ensuremath{-}}B1$ phase transition in gallium nitride (GaN). The large-scale simulation using a 4096-atom simulation box reveals the phase transition with excellent detail, revealing different simulated transition paths under particular stress conditions. With well-trained machine learning potentials, this method can be easily applied to all types of systems for accurate scalable simulations of solid-solid reconstructive phase transition. Metadynamics ; Phase transition ; Molecular dynamics ; Computer science ; Statistical physics ; Embedded atom model ; Density functional theory ; Scaling ; Representation (politics) ; Computational science ; Algorithm ; Physics ; Mathematics ; Thermodynamics ; Quantum mechanics ; Geometry Machine Learning in Materials Science ; Thermal properties of materials ; Advanced Electron Microscopy Techniques and Applications
Authors: Namjung Kim | Kyoungmin Min
Source: The Journal of Physical Chemistry Letters (article, 2021-03-02)
Field: Materials Science — Materials Chemistry

Accelerated Discovery of Zeolite Structures with Superior Mechanical Properties via Active Learning A Bayesian active learning platform is developed for the accelerated discovery of mechanically superior zeolite structures from more than half a million hypothetical candidates. An initial database containing the mechanical properties of synthesizable zeolites is trained to develop the machine learning regression model. Then, a Bayesian optimization scheme is implemented to identify zeolites with potentially excellent mechanical properties. The newly accumulated database consists of 871 labeled structures, and the uncertainty of the predictive model is reduced by 40% and 58% for the bulk and shear moduli, respectively. The model convergence shows that no further improvement occurs after the 10th iteration of optimizations. The proposed platform is able to discover 23 new zeolite structures that have unprecedented shear moduli; in one case, the shear modulus (127.81 GPa) is 250% higher than the previous data set. The proposed platform accelerates the material discovery process while maximizing computational efficiency and enhancing the predictive accuracy. Bayesian optimization ; Moduli ; Computer science ; Zeolite ; Convergence (economics) ; Bayesian probability ; Shear (geology) ; Materials science ; Set (abstract data type) ; Shear modulus ; Algorithm ; Machine learning ; Artificial intelligence ; Composite material ; Physics ; Chemistry Machine Learning in Materials Science ; Zeolite Catalysis and Synthesis ; X-ray Diffraction in Crystallography
Authors: Danilo Tedesco | Bruno Rafael de Almeida Moreira | Marcelo Rodrigues Barbosa Júnior | João Paulo Papa | Rouverson Pereira da Silva
Source: Computers and Electronics in Agriculture (article, 2021-11-10)
Field: Chemistry — Analytical Chemistry

Predicting on multi-target regression for the yield of sweet potato by the market class of its roots upon vegetation indices Yield (engineering) ; Vegetation (pathology) ; Class (philosophy) ; Regression analysis ; Regression ; Mathematics ; Statistics ; Agronomy ; Artificial intelligence ; Botany ; Computer science ; Biology ; Materials science ; Medicine Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Leaf Properties and Growth Measurement
Authors: Tao Huang | Mengqi Long | Jun Xiao | Hao Liu | Guoxiu Wang
Source: Energy Materials (article, 2021-10-30)
Field: Materials Science — Polymers and Plastics

Recent research on emerging organic electrode materials for energy storage Due to the growth of the demand for rechargeable batteries in intelligent terminals, electric vehicles, energy storage, and other markets, electrode materials, as the essential of batteries, have attracted tremendous attention. The research of emerging organic electrode materials in batteries has been boosted recently to their advantages of low cost, environmental friendliness, biodegradability, and designability. This manuscript highlights and classifies several recent studies on organic electrode materials and lists their potential applications in various battery systems. Finally, the challenge and perspective of organic electrode materials are also summarized. Energy storage ; Battery (electricity) ; Nanotechnology ; Electrode ; Computer science ; Process engineering ; Materials science ; Environmental science ; Biochemical engineering ; Engineering ; Chemistry Conducting polymers and applications ; Polyoxometalates: Synthesis and Applications ; Supercapacitor Materials and Fabrication