jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Chunhui Li | Steven Farrell | B. J. Gilbert | Piotr Zarzycki
Source: Goldschmidt2022 abstracts (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Rapid Prediction of Liquid Properties from Molecular Dynamics Simulations using Deep Learning Molecular dynamics simulations are indispensable tools in understanding collective in nature atom/molecule behavior in gas, solid and liquid phases on the molecular level if the quantum effects can be neglected. In order to accurately predict the structural and dynamic properties of complex solutions, tens of nanosecond-long simulation trajectories have to be analyzed. Molecular dynamics ; Computer science ; Dynamics (music) ; Deep learning ; Artificial intelligence ; Biological system ; Chemistry ; Physics ; Computational chemistry Machine Learning in Materials Science ; Phase Equilibria and Thermodynamics
Authors: Rémi Servien | Éric Latrille | Patureau, Dominique, Steyer | Arnaud Hélias
Source: HAL (Le Centre pour la Communication Scientifique Directe) (preprint, 2022-05-15)
Field: Chemistry — Analytical Chemistry

Machine learning models based on molecular descriptors to predict toxicological and ecotoxicological characterization factors International audience Computer science ; Machine learning ; Artificial intelligence ; Characterization (materials science) ; Materials science ; Nanotechnology Spectroscopy and Chemometric Analyses
Authors: F. T. Latypov | A. E. Mayer | E. V. Fomin | Vasiliy S. Krasnikov
Source: Physical Mesomechanics of Materials. Physical Principles of Multi-Layer Structure Forming and Mechanisms of Non-Linear Behavior: Conference Proceedings (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Dynamic Deformation of Porous Metals: Molecular Dynamics Study, Theoretical Models and Machine Learning ДИНАМИЧЕСКАЯ ДЕФОРМАЦИЯ ПОРИСТЫХ МЕТАЛЛОВ: МОЛЕКУЛЯРНО-ДИНАМИЧЕСКОЕ ИССЛЕДОВАНИЕ, ТЕОРЕТИЧЕСКИЕ МОДЕЛИ И МАШИННОЕ ОБУЧЕНИЕ Латыпов Ф Deformation (meteorology) ; Molecular dynamics ; Dynamics (music) ; Porosity ; Computer science ; Materials science ; Porous medium ; Artificial intelligence ; Composite material ; Chemistry ; Physics ; Computational chemistry Machine Learning in Materials Science
Authors: Rose K. Cersonsky | Maria Pakhnova | Edgar A. Engel | Michele Ceriotti
Source: arXiv (Cornell University) (preprint, 2022-09-21)
Field: Materials Science — Materials Chemistry

A data-driven interpretation of the stability of molecular crystals Due to the subtle balance of intermolecular interactions that govern structure-property relations, predicting the stability of crystal structures formed from molecular building blocks is a highly non-trivial scientific problem. A particularly active and fruitful approach involves classifying the different combinations of interacting chemical moieties, as understanding the relative energetics of different interactions enables the design of molecular crystals and fine-tuning their stabilities. While this is usually performed based on the empirical observation of the most commonly encountered motifs in known crystal structures, we propose to apply a combination of supervised and unsupervised machine-learning techniques to automate the construction of an extensive library of molecular building blocks. We introduce a structural descriptor tailored to the prediction of the binding (lattice) energy and apply it to a curated dataset of organic crystals and exploit its atom-centered nature to obtain a data-driven assessment of the contribution of different chemical groups to the lattice energy of the crystal. We then interpret this library using a low-dimensional representation of the structure-energy landscape and discuss selected examples of the insights into crystal engineering that can be extracted from this analysis, providing a complete database to guide the design of molecular materials. Crystal structure prediction ; Computer science ; Stability (learning theory) ; Lattice (music) ; Exploit ; Intermolecular force ; Representation (politics) ; Crystal (programming language) ; Interpretation (philosophy) ; Artificial intelligence ; Crystal structure ; Lattice energy ; Theoretical computer science ; Chemical physics ; Machine learning ; Molecule ; Chemistry ; Physics ; Crystallography Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Guido Ori
Source: SPIRE - Sciences Po Institutional REpository (article, 2022-09-19)
Field: Materials Science — Materials Chemistry

Co-Org. Symposium ""From predictive modelling to machine learning as versatile toolsfor materials design." International audience Predictive modelling ; Support vector machine ; Key (lock) ; Artificial neural network ; Field (mathematics) Machine Learning in Materials Science ; Material Selection and Properties ; Catalysis and Oxidation Reactions
Authors: Daniel Hewitt | Tom Pope | Misbah Sarwar | Alessandro Turrina | Ben Slater
Source: ChemRxiv (preprint, 2022-05-26)
Field: Chemistry — Inorganic Chemistry

Machine learning assisted high-throughput screening of zeolites for the selective adsorption of xylene isomers The production of widely used polymers such as polyester currently relies upon the chemical separation of and transformation of xylene isomers. The least valuable but most prevalent isomer is meta-xylene which can be selectively transformed into the more useful and expensive para-xylene isomer using a zeolite catalyst but at a high energy cost. In this work, high-throughput screening of existing and hypothetical zeolite databases containing more than two million structures was performed, using a combination of classical simulation and deep neural network methods to identify promising materials for selective adsorption of meta-xylene. Novel anomaly detection techniques were applied to the heavily biased classification task of identifying structures with a selectivity greater than that of the best performing existing zeolite, ZSM-5 (MFI topology). Four hypothetical zeolite topologies are found to be several orders of magnitude more selective towards meta-xylene than ZSM-5 which may provide an impetus for synthetic efforts to realise these promising materials. Moreover, the leading hypothetical frameworks identified from the screening procedure require a markedly lower operating temperature to achieve the diffusion seen in existing materials, suggesting significant energetic savings if the frameworks can be realised. Zeolite ; Selectivity ; Xylene ; Adsorption ; Throughput ; p-Xylene ; Materials science ; Topology (electrical circuits) ; Computer science ; Catalysis ; Chemistry ; Chemical engineering ; Process engineering ; Toluene ; Organic chemistry ; Mathematics ; Engineering Zeolite Catalysis and Synthesis ; Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Daniel Hewitt | Tom Pope | Misbah Sarwar | Alessandro Turrina | Ben Slater
Source: ChemRxiv (preprint, 2022-06-07)
Field: Chemistry — Inorganic Chemistry

Machine learning assisted high-throughput screening of zeolites for the selective adsorption of xylene isomers The production of widely used polymers such as polyester currently relies upon the chemical separation of and transformation of xylene isomers. The least valuable but most prevalent isomer is meta-xylene which can be selectively transformed into the more useful and expensive para-xylene isomer using a zeolite catalyst but at a high energy cost. In this work, high-throughput screening of existing and hypothetical zeolite databases containing more than two million structures was performed, using a combination of classical simulation and deep neural network methods to identify promising materials for selective adsorption of meta-xylene. Novel anomaly detection techniques were applied to the heavily biased classification task of identifying structures with a selectivity greater than that of the best performing existing zeolite, ZSM-5 (MFI topology). Eight hypothetical zeolite topologies are found to be several orders of magnitude more selective towards meta-xylene than ZSM-5 which may provide an impetus for synthetic efforts to realise these promising materials. Moreover, the leading hypothetical frameworks identified from the screening procedure require a markedly lower operating temperature to achieve the diffusion seen in existing materials, suggesting significant energetic savings if the frameworks can be realised. Zeolite ; Selectivity ; Xylene ; Adsorption ; p-Xylene ; Materials science ; Throughput ; Topology (electrical circuits) ; Computer science ; Catalysis ; Chemistry ; Chemical engineering ; Process engineering ; Toluene ; Organic chemistry ; Mathematics ; Engineering Zeolite Catalysis and Synthesis ; Machine Learning in Materials Science ; Catalysis for Biomass Conversion
Authors: Carlos Manuel de Armas-Morejón | Luís A. Montero | Ángel Rubio | Joaquim Jornet-Somoza
Source: arXiv (Cornell University) (preprint, 2022-05-24)
Field: Chemistry — Physical and Theoretical Chemistry

Electronic Descriptors for Supervised Spectroscopic Predictions Spectroscopic properties of molecules holds great importance for the description of the molecular response under the effect of an UV/Vis electromagnetic radiation. Computationally expensive ab initio (e.g. MultiConfigurational SCF, Coupled Cluster) or TDDFT methods are commonly used by the quantum chemistry community to compute these properties. In this work, we propose a (supervised) Machine Learning approach to model the absorption spectra of organic molecules. Several supervised ML methods have been tested such as Kernel Ridge Regression (KRR), Multiperceptron Neural Networs (MLP) and Convolutional Neural Networks. The use of only geometrical descriptors (e.g. Coulomb Matrix) proved to be insufficient for an accurate training. Inspired on the TDDFT theory, we propose to use a set of electronic descriptors obtained from low-cost DFT methods: orbital energy differences, transition dipole moment between occupied and unoccupied Kohn-Sham orbitals and charge-transfer character of mono-excitations. We demonstrate that with this electronic descriptors and the use of Neural Networks we can predict not only a density of excited states, but also getting very good estimation of the absorption spectrum and charge-transfer character of the electronic excited states, reaching results close to the chemical accuracy (~2 kcal/mol or ~0.1eV). Time-dependent density functional theory ; Excited state ; Coupled cluster ; Density functional theory ; Ab initio ; Electronic structure ; Chemistry ; Atomic orbital ; Dipole ; Artificial neural network ; Artificial intelligence ; Physics ; Computer science ; Molecular physics ; Computational chemistry ; Atomic physics ; Molecule ; Quantum mechanics Various Chemistry Research Topics ; Water Quality Monitoring and Analysis ; Free Radicals and Antioxidants
Authors: C. Edwards
Source: Engineering & Technology (article, 2022-06-01)
Field: Materials Science — Materials Chemistry

Metal Gurus [Materials science - platinum] Platinum, palladium have multiple and critical uses as catalysts, these metals play a key role in breaking down nitric oxide and other pollutants in car exhausts and tend to deliver the best results across a broad range of chemical reactions. They are not needed in enormous quantities for any single reaction, but more applications for these materials are being found. Even today, recycling accounts for a quarter of the platinum and palladium supply, because mining simply cannot deliver enough of these metals. The search is on for substitute materials that can do the jobs of the platinum-group metals, but which use far more abundant and readily available raw materials. The traditional tool was trial and error, which is an expensive, time-consuming process. Recently the process has shifted into the virtual domain, with chemists building digital twins of potential catalysts and target molecules. Another method that chemists employ in catalyst research is density functional theory (DFT). Despite its far greater simplicity compared to more accurate methods, DFT still takes hours to compute on today&apos;s hardware in catalytic scenarios like these. Other researchers are looking at using machine learning for a faster approximation of DFT and similar quantum-behaviour calculations. Platinum ; Metal ; Materials science ; Chemistry ; Metallurgy ; Organic chemistry Machine Learning in Materials Science
Authors: James P. Darby | Dávid Péter Kovács | Ilyes Batatia | Miguel A. Caro | Gus L. W. Hart | Christoph Ortner | Gábor Csányi
Source: arXiv (Cornell University) (preprint, 2022-10-02)
Field: Materials Science — Materials Chemistry

Tensor-reduced atomic density representations Density based representations of atomic environments that are invariant under Euclidean symmetries have become a widely used tool in the machine learning of interatomic potentials, broader data-driven atomistic modelling and the visualisation and analysis of materials datasets.The standard mechanism used to incorporate chemical element information is to create separate densities for each element and form tensor products between them. This leads to a steep scaling in the size of the representation as the number of elements increases. Graph neural networks, which do not explicitly use density representations, escape this scaling by mapping the chemical element information into a fixed dimensional space in a learnable way. We recast this approach as tensor factorisation by exploiting the tensor structure of standard neighbour density based descriptors. In doing so, we form compact tensor-reduced representations whose size does not depend on the number of chemical elements, but remain systematically convergeable and are therefore applicable to a wide range of data analysis and regression tasks. Scaling ; Tensor (intrinsic definition) ; Invariant (physics) ; Representation (politics) ; Homogeneous space ; Multidimensional scaling ; Visualization ; Computer science ; Theoretical computer science ; Mathematics ; Artificial intelligence ; Physics ; Machine learning ; Pure mathematics ; Geometry ; Quantum mechanics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Advanced NMR Techniques and Applications
Authors: Bowen Li
Source: Lehigh Preserve (article, 2022-05-01)
Field: Materials Science — Materials Chemistry

Obtain high accuracy data-driven molecular property estimation from sparse data In many problems in chemistry, chemical engineering, and material science, including the design of material and high throughput screening for drug discovery, accurate property estimation is required to identify the potential candidates (chemicals, intermediates in the reactions) for further validation. While conventional methods such as experiments or quantum chemical calculations allow for calculating these properties reliably, they are not fast enough for computational tractability. When the target property of a large number of candidates has to be computed, machine learningoffers relative computation scalability; after training a surrogate model with reference data from quantum mechanical calculations or experiments, the properties of new/unknown candidates can be predicted in a fast and accurate manner. However, data-driven models are often trained with tens of thousands of data, the data acquisition of which can be resource-intensive. In this thesis, we address the problem of building accurate data-driven molecular property models from sparse data sources using different strategies and models. Specifically, this thesis concerns (1) an algorithm to perform active learning for a sparse group activity model, (2) an active learning study on a graph deep learning network (3) a conceptual study to analyze when transfer learning benefits a sparse model. Finally, as a foray towards using data science and allied tools for reactions, we present a reaction network optimization formulation wherein obtaining property information from sparse data would be beneficial. We show how this formulation enables an unprecedented understanding of a complex reaction network. Ultimately, our objective is to infuse the methods into practical applications such as annotating a reaction network system or screening a potential chemical candidate with minimum computational cost. Property (philosophy) ; Computation ; Data modeling ; Sparse matrix ; Graph ; Sparse approximation ; Active learning (machine learning) ; Quantum chemical Machine Learning in Materials Science ; Machine Learning and Algorithms ; Advanced Graph Neural Networks
Authors: Mohamed A. Abba | Jonathan P. Williams | Brian J. Reich
Source: arXiv (Cornell University) (preprint, 2022-01-28)
Field: Materials Science — Materials Chemistry

A penalized complexity prior for deep Bayesian transfer learning with application to materials informatics A key task in the emerging field of materials informatics is to use machine learning to predict a material's properties and functions. A fast and accurate predictive model allows researchers to more efficiently identify or construct a material with desirable properties. As in many fields, deep learning is one of the state-of-the art approaches, but fully training a deep learning model is not always feasible in materials informatics due to limitations on data availability, computational resources, and time. Accordingly, there is a critical need in the application of deep learning to materials informatics problems to develop efficient transfer learning algorithms. The Bayesian framework is natural for transfer learning because the model trained from the source data can be encoded in the prior distribution for the target task of interest. However, the Bayesian perspective on transfer learning is relatively unaccounted for in the literature, and is complicated for deep learning because the parameter space is large and the interpretations of individual parameters are unclear. Therefore, rather than subjective prior distributions for individual parameters, we propose a new Bayesian transfer learning approach based on the penalized complexity prior on the Kullback-Leibler divergence between the predictive models of the source and target tasks. We show via simulations that the proposed method outperforms other transfer learning methods across a variety of settings. The new method is then applied to a predictive materials science problem where we show improved precision for estimating the band gap of a material based on its structural properties. Artificial intelligence ; Computer science ; Machine learning ; Transfer of learning ; Deep learning ; Bayesian probability ; Materials informatics ; Field (mathematics) ; Inductive transfer ; Divergence (linguistics) ; Task (project management) ; Health informatics ; Robot learning ; Mathematics ; Engineering informatics Machine Learning in Materials Science ; Non-Destructive Testing Techniques ; Domain Adaptation and Few-Shot Learning
Authors: Thorren Kirschbaum | Börries von Seggern | Joachim Dzubiella | Annika Bande | Frank Noé
Source: arXiv (Cornell University) (preprint, 2022-09-30)
Field: Materials Science — Materials Chemistry

Machine learning frontier orbital energies of nanodiamonds Nanodiamonds have a wide range of applications including catalysis, sensing, tribology and biomedicine. To leverage nanodiamond design via machine learning, we introduce the new dataset ND5k, consisting of 5,089 diamondoid and nanodiamond structures and their frontier orbital energies. ND5k structures are optimized via tight-binding density functional theory (DFTB) and their frontier orbital energies are computed using density functional theory (DFT) with the PBE0 hybrid functional. We also compare recent machine learning models for predicting frontier orbital energies for similar structures as they have been trained on (interpolation on ND5k), and we test their abilities to extrapolate predictions to larger structures. For both the interpolation and extrapolation task, we find best performance using the equivariant graph neural network PaiNN. The second best results are achieved with a message passing neural network using a tailored set of atomic descriptors proposed here. Density functional theory ; Leverage (statistics) ; Extrapolation ; Computer science ; Artificial neural network ; Machine learning ; Artificial intelligence ; Scalability ; Physics ; Quantum mechanics ; Mathematics Diamond and Carbon-based Materials Research ; Machine Learning in Materials Science ; Advanced Materials Characterization Techniques
Authors: Pei Ge | Linfeng Zhang | Huan Lei
Source: arXiv (Cornell University) (preprint, 2022-10-22)
Field: Materials Science — Materials Chemistry

Machine learning assisted coarse-grained molecular dynamics modeling of meso-scale interfacial fluids A hallmark of meso-scale interfacial fluids is the multi-faceted, scale-dependent interfacial energy, which often manifests different characteristics across the molecular and continuum scale. The multi-scale nature imposes a challenge to construct reliable coarse-grained (CG) models, where the CG potential function needs to faithfully encode the many-body interactions arising from the unresolved atomistic interactions and account for the heterogeneous density distributions across the interface. We construct the CG models of both single- and two-component of polymeric fluid systems based on the recently developed deep coarse-grained potential (DeePCG) scheme, where each polymer molecule is modeled as a CG particle. By only using the training samples of the instantaneous force under the thermal equilibrium state, the constructed CG models can accurately reproduce both the probability density function of the void formation in bulk and the spectrum of the capillary wave across the fluid interface. More importantly, the CG models accurately predict the volume-to-area scaling transition for the apolar solvation energy, illustrating the effectiveness to probe the meso-scale collective behaviors encoded with molecular-level fidelity. Statistical physics ; Molecular dynamics ; Scaling ; Scale (ratio) ; Solvation ; Length scale ; Construct (python library) ; Chemical physics ; Materials science ; Computer science ; Physics ; Mechanics ; Molecule ; Chemistry ; Computational chemistry ; Mathematics Block Copolymer Self-Assembly ; Machine Learning in Materials Science ; Enhanced Oil Recovery Techniques
Authors: Changming Ke | Zijing Lin | Shi Liu
Source: arXiv (Cornell University) (preprint, 2022-02-23)
Field: Materials Science — Materials Chemistry

Three Dimensional Activity Volcano Plot under External Electric Field An external electric field (EEF) can impact a broad range of catalytic processes beyond redox systems. Computational design of catalysts under EEFs targeting specific operation conditions essentially requires accurate predictions of the response of a complex physicochemical system to collective parameters such as EEF strength/direction and temperature. Here, we develop a first-principles-based multiscale approach that enables efficient EEF-dependent kinetic modeling of heterogeneous catalysis. Taking steam reforming of methanol as an example, we find that the methanol conversion rate exhibits strong nonlinear response to temperature and EEF, and the optimal field line and constant carbon concentration line defined in the temperature--EEF parameter space serve as powerful metrics for catalyst design. Assisted with a deep neural network, we establish a three-dimensional activity volcano plot under EEFs for thousands of metallic alloys. Catalysis ; Nonlinear system ; Methanol ; Electric field ; Field (mathematics) ; Parameter space ; Kinetic energy ; Thermal ; Line (geometry) ; Plot (graphics) ; Materials science ; Nanotechnology ; Physics ; Chemistry ; Thermodynamics ; Mathematics ; Classical mechanics Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Catalysts for Methane Reforming