jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Justin K. Kirkland
Source:  (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Small Molecule Activation by Transition Metal Complexes: Studies with Quantum Mechanical and Machine Learning Methodologies Quantum ; Transition metal ; Transition (genetics) ; Chemistry ; Materials science ; Nanotechnology ; Computer science ; Physics ; Quantum mechanics ; Catalysis ; Organic chemistry Machine Learning in Materials Science ; Inorganic and Organometallic Chemistry ; Computational Drug Discovery Methods
Authors: Dávid Péter Kovács | Cas van der Oord | Jiří Kučera | Alice E. A. Allen | Daniel J. Cole | Christoph Ortner | Gábor Csányi
Source: ChemRxiv (preprint, 2021-07-19)
Field: Materials Science — Materials Chemistry

Linear Atomic Cluster Expansion Force Fields forOrganic Molecules: beyond RMSE We demonstrate that accurate linear force fields can be built using the Atomic Cluster Ex- pansion (ACE) framework for molecules. Our model is built from body ordered symmetric polynomials which makes it a natural exten- sion of traditional molecular mechanics force fields, and the large number of free parameters allows sufficient flexibility that it reaches the accuracy typical of recently proposed machine learning based approaches. We test our model on the MD17 and ISO17 data sets and also on a larger, more flexible molecule, and compare to leading machine learning models as well as re- fitted empirical force fields. We show that the linear body ordered ACE model has excellent transferability for properties beyond raw energy and force RMSE, both for molecular dynamics at different temperatures and for configurations very far from the training set including dihedral scans and even bond breaking. Force field (fiction) ; Transferability ; Flexibility (engineering) ; Dihedral angle ; Cluster (spacecraft) ; Molecular dynamics ; Statistical physics ; Mean squared error ; Molecule ; Computer science ; Physics ; Chemistry ; Computational chemistry ; Mathematics ; Artificial intelligence ; Machine learning ; Quantum mechanics ; Statistics Machine Learning in Materials Science ; Mass Spectrometry Techniques and Applications ; Force Microscopy Techniques and Applications
Authors: Dávid Péter Kovács | Cas van der Oord | Jiří Kučera | Alice E. A. Allen | Daniel J. Cole | Christoph Ortner | Gábor Csányi
Source:  (preprint, 2021-07-01)
Field: Materials Science — Materials Chemistry

Linear Atomic Cluster Expansion Force Fields for Organic Molecules: beyond RMSE We demonstrate that accurate linear force fields can be built using the Atomic Cluster Ex- pansion (ACE) framework for molecules. Our model is built from body ordered symmetric polynomials which makes it a natural exten- sion of traditional molecular mechanics force fields, and the large number of free parameters allows sufficient flexibility that it reaches the accuracy typical of recently proposed machine learning based approaches. We test our model on the MD17 and ISO17 data sets and also on a larger, more flexible molecule, and compare to leading machine learning models as well as re- fitted empirical force fields. We show that the linear body ordered ACE model has excellent transferability for properties beyond raw energy and force RMSE, both for molecular dynamics at different temperatures and for configurations very far from the training set including dihedral scans and even bond breaking. Transferability ; Force field (fiction) ; Flexibility (engineering) ; Dihedral angle ; Cluster (spacecraft) ; Molecular dynamics ; Mean squared error ; Raw data ; Statistical physics ; Computer science ; Molecule ; Mathematics ; Physics ; Computational chemistry ; Chemistry ; Artificial intelligence ; Machine learning ; Quantum mechanics ; Statistics Machine Learning in Materials Science ; Mass Spectrometry Techniques and Applications ; Force Microscopy Techniques and Applications
Authors: M. Muraguchi | Ryuho Nakaya | Souma Kawahara | Yoshitaka Itoh | Tota Suko
Source: Japanese Journal of Applied Physics (article, 2021-12-22)
Field: Materials Science — Materials Chemistry

Investigation of features for prediction modeling of nanoscale conduction with time-dependent calculation of electron wave packet Abstract A model to predict the electron transmission probability from the random impurity distribution in a two-dimensional nanowire system by combining the time evolution of the electron wave function and machine learning is proposed. We have shown that the intermediate state of the time evolution calculation is advantageous for efficient modeling by machine learning. The features for machine learning are extracted by analyzing the time variation of the electron density distribution using time evolution calculations. Consequently, the prediction error of the model is improved by performing machine learning based on the features. The proposed method provides a useful perspective for analyzing the motion of electrons in nanoscale semiconductors. Computer science ; Electron ; Wave function ; Nanoscopic scale ; Time evolution ; Statistical physics ; Wave packet ; Artificial intelligence ; Machine learning ; Algorithm ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Advancements in Semiconductor Devices and Circuit Design
Authors: Zhao Yao
Source: CUNY Academic Works (City University of New York) (article, 2021-01-01)
Field: Chemistry — Spectroscopy

Automated Code Engine for Tensor HyperContraction: Derivation, Optimization and Implementation of Rank-reduced Coupled Cluster Theories The ultimate goal of electronic structure theory is solving the electronic Schr¨odinger Equation. However, even accurate approximations of solving Schr¨odinger Equation, such as high order coupled cluster theories, require computational efforts that are too demanding to be applied on large chemical systems. This thesis tackles the problem of curse of dimensionality: how to reduce the time complexity of high-accuracy coupled cluster methods in order to accelerate computations of molecular energy. On one hand, we believe that low-rank approximation (i.e. Tensor HyperContraction) of high-order tensors appearing in coupled cluster theory is a promising way to achieve rank-reduced coupled cluster theory. On the other hand, we think that the work of deriving, optimizing and implementing low-rank approximated coupled cluster theory is both tedious and repetitious, and shall be performed by an automatic code engine other than chemists. In this thesis, we introduce our chemistry equation compiler, Autom: a code engine that is designed for automatically deriving, optimizing and implementing low-rank approximated coupled cluster theories. With Autom, the development of electronic theories is greatly simplified. Autom can easily generate runnable codes in minutes for tensor-algebra based chemistry equations with customized low-rank approximation rules; conventionally, such procedure may take an expert weeks or even months to manually program. This thesis roughly consists of two parts. In the first part, we aim to cover the lowrank approximation techniques for high-accuracy coupled cluster theories, emphasizing on two new factorization techniques we developed especially for several coupled cluster theories including triple contributions. In the second part, we demonstrate the algorithms of our automatic code engine. We also summarize recent advances of domain specific compilers in Machine Learning community and discuss future directions for compute graph optimization. We believe that this new software infrastructure holds great promise for future chemistry package designs. Tensor (intrinsic definition) ; Rank (graph theory) ; Programming language ; Algebra over a field ; Code (set theory) ; Computer science ; Mathematics ; Algorithm ; Applied mathematics ; Mathematical optimization ; Pure mathematics ; Combinatorics ; Set (abstract data type) Advanced NMR Techniques and Applications ; Scientific Computing and Data Management ; Distributed and Parallel Computing Systems
Authors: Anjana Samarakoon | D. M. Tennant
Source: Journal of Physics Condensed Matter (preprint, 2021-02-19)
Field: Materials Science — Materials Chemistry

Machine learning for magnetic phase diagrams and inverse scattering problems Machine learning promises to deliver powerful new approaches to neutron scattering from magnetic materials. Large scale simulations provide the means to realise this with approaches including spin-wave, Landau Lifshitz, and Monte Carlo methods. These approaches are shown to be effective at simulating magnetic structures and dynamics in a wide range of materials. Using large numbers of simulations the effectiveness of machine learning approaches are assessed. Principal component analysis and nonlinear autoencoders are considered with the latter found to provide a high degree of compression and to be highly suited to neutron scattering problems. Agglomerative heirarchical clustering in the latent space is shown to be effective at extracting phase diagrams of behavior and features in an automated way that aid understanding and interpretation. The autoencoders are also well suited to optimizing model parameters and were found to be highly advantageous over conventional fitting approaches including being tolerant of artifacts in untreated data. The potential of machine learning to automate complex data analysis tasks including the inversion of neutron scattering data into models and the processing of large volumes of multidimensional data is assessed. Directions for future developments are considered and machine learning argued to have high potential for impact on neutron science generally. Neutron scattering ; Computer science ; Machine learning ; Artificial intelligence ; Artificial neural network ; Monte Carlo method ; Scattering ; Cluster analysis ; Statistical physics ; Physics ; Mathematics ; Optics Machine Learning in Materials Science ; Advanced Condensed Matter Physics ; High-pressure geophysics and materials
Authors: Paolo Restuccia | E. Ahmad | N. M. Harrison
Source: arXiv (Cornell University) (preprint, 2021-08-20)
Field: Materials Science — Materials Chemistry

A transferable prediction model of molecular adsorption on metals based on adsorbate and substrate properties Surface adsorption is one of the fundamental processes in numerous fields, including catalysis, environment, energy and medicine. The development of an adsorption model which provides an effective prediction of binding energy in minutes has been a long term goal in surface and interface science. The solution has been elusive as identifying the intrinsic determinants of the adsorption energy for various compositions, structures and environments is non-trivial. We introduce a new and flexible model for predicting adsorption energies to metal substrates. The model is based on easily computed, intrinsic properties of the substrate and adsorbate. It is parameterised using machine learning based on first-principles calculations of probe molecules (e.g., H$_2$O, CO$_2$, O$_2$, N$_2$) adsorbed to a range of pure metal substrates. The model predicts the computed dissociative adsorption energy to metal surfaces with a correlation coefficient of 0.93 and a mean absolute error of 0.77 eV for the large database of molecular adsorption energies provided by \textit{Catalysis-Hub.org} which have a range of 15 eV. As the model is based on pre-computed quantities it provides near-instantaneous estimates of adsorption energies and it is sufficiently accurate to eliminate around 90\% of candidates in screening study of new adsorbates. The model, therefore, significantly enhances current efforts to identify new molecular coatings in many applied research fields. Adsorption ; Range (aeronautics) ; Catalysis ; Substrate (aquarium) ; Molecule ; Binding energy ; Materials science ; Metal ; Chemical physics ; Chemistry ; Thermodynamics ; Physical chemistry ; Computational chemistry ; Physics ; Atomic physics ; Organic chemistry Machine Learning in Materials Science
Authors: Kiran H. Kanekal
Source: MPG.PuRe (Max Planck Society) (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Investigating the Effect of Coarse-Graining on Chemical Compound Space Chemical structure-property relationships are essential for the development of new materials used in all facets of life. Practically, this process amounts to projecting regions of the chemical compound space (CCS) onto certain descriptors related to the property of interest, allowing the structure-property relationship to be inferred. The challenge in constructing these relationships usually stems from a lack of data, as their accuracy and transferability will depend on how well-sampled CCS is with respect to the chosen descriptors. High-throughput screening, in which the properties of compounds are determined in an automated fashion, is one strategy used to overcome this problem. However, for many properties of soft-matter systems, this approach is difficult to implement computationally. This difficulty arises due to the large costs associated with adequately sampling complex free energy landscapes at atomistic resolutions using established tools such as molecular dynamics (MD) simulations. Coarse-grained (CG) models, parameterized at lower resolutions compared to their atomistic counterparts, provide a means to circumvent these costs. However, many of these models are constructed in order to specifically reproduce the properties of a small number of compounds, making it difficult to generalize across CCS. In this work, we demonstrate that the coarse-grained Martini model reduces the size of CCS, and can be used in computational high-throughput screening methods to efficiently construct chemical structure-property relationships over wide ranges of CCS. We find that this reduction of CCS is due to a limited number of Martini interaction types, with multiple atomistic chemical fragments mapping to the same CG interaction type. We then investigate the relationship between unsupervised machine learning and coarse-graining, yielding strategies for parameterizing chemically transferable CG models from both a top-down and bottom-up perspective. We employ these data-driven techniques to parameterize new top-down CG models and quantify their transferability and accuracy as a function of the number of CG interaction types for each model. Finally, we develop a method that uses unsupervised machine learning in combination with the bottom-up multiscale coarse-graining technique to generate chemically-transferable CG models with high structural accuracy. We examine the limitations of both top-down and bottom-up approaches and make recommendations for the future development of these methodologies. Overall, our work demonstrates the means by which chemically-transferable CG models can be both constructed and utilized to efficiently infer chemical structure-property relationships for materials discovery. Space (punctuation) ; Granularity ; Computer science ; Mathematics ; Physics X-ray Diffraction in Crystallography
Authors: Ruizhi Zhang | Sohan Seth | James Cumby
Source: Acta Crystallographica Section A Foundations and Advances (article, 2021-08-14)
Field: Materials Science — Materials Chemistry

New generalized crystallographic descriptors for structural machine learning The ever-growing amount of crystallographic data offers the potential to uncover a range of scientific discoveries, from rapidly predicting physical properties to suggesting new materials with desirable functional behaviours. This is further enhanced by the current growth in machine learning (ML) algorithm development and implementation. There is, however, a significant obstacle to this goal; standard crystallographic information are not suitable inputs for ML algorithms. This arises due to the inherent flexibility of crystallography, such as non-unique unit cell definitions and symmetry. To overcome this problem, significant progress has been made in devising 'descriptors' for crystallographic ML, compressing and standardising crystallographic information into a smaller feature space. Much of the existing focus has been on molecular crystals, where the finite extent of individual molecules imposes a limit on the size of feature vector required. A large number of approaches have been proposed but do not easily extrapolate to extended (i.e. inorganic) materials. [1] The descriptors that are suitable for extended solids tend to be either hand-crafted for a specific problem, or have so many dimensions that extremely large datasets must be used to train reliable ML models. In addition, many do not scale well with variable numbers of atomic species. Computer science ; Reciprocal lattice ; Feature (linguistics) ; Algorithm ; Flexibility (engineering) ; Invariant (physics) ; Artificial intelligence ; Crystallography ; Diffraction ; Machine learning ; Mathematics ; Chemistry ; Physics X-ray Diffraction in Crystallography ; Machine Learning in Materials Science ; Crystallography and molecular interactions
Authors: Kai Xu | Lei Yan | Bingran You
Source: ChemRxiv (preprint, 2021-12-23)
Field: Materials Science — Materials Chemistry

Bayesian active learning of interatomic force field for molecular dynamics simulation of Pt/Ag(111) Force field is a central requirement in molecular dynamics (MD) simulation for accurate description of the potential energy landscape and the time evolution of individual atomic motions. Most energy models are limited by a fundamental tradeoff between accuracy and speed. Although ab initio MD based on density functional theory (DFT) has high accuracy, its high computational cost prevents its use for large-scale and long-timescale simulations. Here, we use Bayesian active learning to construct a Gaussian process model of interatomic forces to describe Pt deposited on Ag(111). An accurate model is obtained within one day of wall time after selecting only 126 atomic environments based on two- and three-body interactions, providing mean absolute errors of 52 and 142 meV/Å for Ag and Pt, respectively. Our work highlights automated and minimalistic training of machine-learning force fields with high fidelity to DFT, which would enable large-scale and long-timescale simulations of alloy surfaces at first-principles accuracy. Molecular dynamics ; Force field (fiction) ; Statistical physics ; Gaussian process ; Work (physics) ; Gaussian ; Bayesian optimization ; Interatomic potential ; Field (mathematics) ; Density functional theory ; Scale (ratio) ; Bayesian probability ; Computer science ; Atomic units ; Kriging ; Physics ; Artificial intelligence ; Machine learning ; Mathematics ; Quantum mechanics Machine Learning in Materials Science ; Ion-surface interactions and analysis ; Fuel Cells and Related Materials
Authors: Kai Xu | Lei Yan | Bingran You
Source: ChemRxiv (preprint, 2021-12-24)
Field: Materials Science — Materials Chemistry

Bayesian active learning of interatomic force field for molecular dynamics simulation of Pt/Ag(111) Force field is a central requirement in molecular dynamics (MD) simulation for accurate description of the potential energy landscape and the time evolution of individual atomic motions. Most energy models are limited by a fundamental tradeoff between accuracy and speed. Although ab initio MD based on density functional theory (DFT) has high accuracy, its high computational cost prevents its use for large-scale and long-timescale simulations. Here, we use Bayesian active learning to construct a Gaussian process model of interatomic forces to describe Pt deposited on Ag(111). An accurate model is obtained within one day of wall time after selecting only 126 atomic environments based on two- and three-body interactions, providing mean absolute errors of 52 and 142 meV/Å for Ag and Pt, respectively. Our work highlights automated and minimalistic training of machine-learning force fields with high fidelity to DFT, which would enable large-scale and long-timescale simulations of alloy surfaces at first-principles accuracy. Molecular dynamics ; Force field (fiction) ; Statistical physics ; Gaussian process ; Bayesian optimization ; Work (physics) ; Gaussian ; Interatomic potential ; Bayesian probability ; Computer science ; Field (mathematics) ; Density functional theory ; Scale (ratio) ; Atomic units ; High fidelity ; Physics ; Artificial intelligence ; Mathematics ; Quantum mechanics Machine Learning in Materials Science ; Ion-surface interactions and analysis ; Fuel Cells and Related Materials
Authors: James Chapman | Kyoung E. Kweon | Nir Goldman
Source:  (article, 2021-05-08)
Field: Materials Science — Materials Chemistry

Understanding hydrogen diffusivity in amorphous titania: A combined density functional theory, machine learning, and graph theory study Hydrogen diffusion plays an important role in understanding how materials chemically degrade over time. Open questions regarding the underlying diffusion pathways and whether they lead to the trapping of Hydrogen provide insight into properties such as the incubation time towards hydrogen embrittlement. For the case of polycrystalline materials, answering these questions are non-trivial, as hydrogen can diffuse through two unique environments: (1) bulk crystalline region and (2) grain boundary region. In this work we approximate the grain boundaries using the amorphous bulk phase due to similarities in the short-range atomic order. Density functional theory (DFT) was used to generate amorphous configurations of titania via ab initio molecular dynamics. A spectrum of hydrogen binding energies was then calculated using a multitude of oxygen sites with varying coordination number. Classical molecular dynamics were then performed on a larger titania system using a machine learning force field. A graph-based order parameter was then used to characterize the classically derived amorphous phase space and was mapped onto the oxygen coordination number. Our results qualitatively indicate that hydrogen diffusivity can be tailored to either inhibit or induce diffusion based on the specific local oxygen environments present in the amorphous phase. Density functional theory ; Amorphous solid ; Materials science ; Chemical physics ; Hydrogen ; Grain boundary ; Molecular dynamics ; Ab initio ; Thermal diffusivity ; Crystallite ; Hydrogen embrittlement ; Computational chemistry ; Chemistry ; Thermodynamics ; Crystallography ; Physics ; Metallurgy ; Quantum mechanics ; Microstructure Machine Learning in Materials Science ; Glass properties and applications ; X-ray Diffraction in Crystallography
Authors: Hirotaka Uryu | Tsunetomo Yamada | Hiroyuki Takakura | Yuki Inada | Kaoru Kimura | Ryuji Tamura | Chang Liu | Ryo Yoshida
Source: Acta Crystallographica Section A Foundations and Advances (article, 2021-08-14)
Field: Materials Science — Materials Chemistry

Exploration of new quasicrystals and approximants by using machine learning Very recently, Liu et. al. proposed a machine learning (ML) approach to distinguishing quasicrystals (QCs) and related approximants (ACs) from ordinary crystals. [1] They built a supervised ML model that classifies any given chemical composition into three structural classes (QCs, ACs, others), and demonstrated its potential predictive power. In this study, we built models according to the previous study and searched for new QCs and ACs base on the predictive candidate compositions from the given models. Quasicrystal ; Materials science ; Computer science ; Mathematics ; Geometry Quasicrystal Structures and Properties