jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ekaterina I. Izgorodina
Source: Faculty of 1000 Research Ltd (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Referee report. For: scikit-matter : A Suite of Generalisable Machine Learning Methods Born out of Chemistry and Materials Science [version 1; peer review: 1 approved, 1 approved with reservations] Suite ; Feature (linguistics) Machine Learning in Materials Science ; Gaussian Processes and Bayesian Inference ; Advanced Electron Microscopy Techniques and Applications
Authors: ‬‬‬‬‬‬Kaveh Farshadfar | Arsalan Hashemi | Reza Khakpour | Kari Laasonen
Source: ChemRxiv (preprint, 2023-08-16)
Field: Chemistry — Organic Chemistry

Kinetics of N2 Release from Diazo Compounds: A Combined Machine Learning-Density Functional Theory Study Diazo compounds are commonly employed as carbene precursors in carbene transfer reactions during a variety of functionalization procedures. Release of N2 gas from diazo compounds may lead to carbene formation, and the ease of this dissociation is highly dependent on the characteristics of the substituents located in the vicinity of the diazo moiety. A quantum mechanical density functional theory assisted by machine learning was used to investigate the relationship between the chemical features of diazo compounds and the activation energy required for the N2 dissociation. Our results suggest that diazo molecules possessing a higher positive partial charge on the carbene carbon and more negative charge on the terminal nitrogen, encounter a lower energy barrier. A more positive C charge decreases the π-donor ability of the carbene lone pair to the π∗ orbital of N2 , while the more negative N charge is a result of a weak interaction between N2 lone pair and vacant p orbital of the carbene. The findings of this study can pave the way for molecular engineering for the purpose of carbene generation which serves as a crucial intermediate for many chemical transformations in synthetic chemistry. Diazo ; Carbene ; Dissociation (chemistry) ; Density functional theory ; Chemistry ; Lone pair ; Moiety ; Computational chemistry ; Photochemistry ; Molecule ; Organic chemistry ; Catalysis Cyclopropane Reaction Mechanisms
Authors: A. Rosen
Source: Faculty of 1000 Research Ltd (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Referee report. For: scikit-matter : A Suite of Generalisable Machine Learning Methods Born out of Chemistry and Materials Science [version 2; peer review: 1 approved, 1 approved with reservations] Suite ; Feature (linguistics) Machine Learning in Materials Science ; Gaussian Processes and Bayesian Inference ; Advanced Electron Microscopy Techniques and Applications
Authors: Jonathan Hollenbach | Cassandra Pate | Haili Jia | James L. Hart | Paulette Clancy | Mitra L. Taheri
Source: arXiv (Cornell University) (preprint, 2023-12-08)
Field: Materials Science — Materials Chemistry

Embedding theory in ML toward real-time tracking of structural dynamics through hyperspectral datasets In-situ Electron Energy Loss Spectroscopy (EELS) is an instrumental technique that has traditionally been used to understand how the choice of materials processing has the ability to change local structure and composition. However, more recent advances to observe and react to transient changes occurring at the ultrafast timescales that are now possible with EELS and Transmission Electron Microscopy (TEM) will require new frameworks for characterization and analysis. We describe a machine learning (ML) framework for the rapid assessment and characterization of in operando EELS Spectrum Images (EELS-SI) without the need for many labeled training datapoints as typically required for deep learning classification methods. By embedding computationally generated structures and experimental datasets into an equivalent latent space through Variational Autoencoders (VAE), we effectively predict the structural changes at latency scales relevant to closed-loop processing within the TEM. The framework described in this study is a critical step in enabling automated, on-the-fly synthesis and characterization which will greatly advance capabilities for materials discovery and precision engineering of functional materials at the atomic scale. Characterization (materials science) ; Hyperspectral imaging ; Computer science ; Embedding ; Biological system ; Electron energy loss spectroscopy ; Artificial intelligence ; Pattern recognition (psychology) ; Machine learning ; Nanotechnology ; Materials science ; Transmission electron microscopy ; Biology Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications
Authors: Alex Tullo﻿
Source: C&EN Global Enterprise (article, 2023-06-05)
Field: Materials Science — Materials Chemistry

Merck works with academia on AI Merck KGaA has signed a 3-year collaboration with the research group of John Hartwig, a professor of chemistry at the University of California, Berkeley, and senior faculty scientist at Lawrence Berkeley National Laboratory. Merck and the Hartwig group aim to use machine learning and artificial intelligence to predict novel catalysts. The partners plan to come up with optimal catalysts for processes that have limited reaction data. They will then employ high-throughput experimentation to hone the results. National laboratory ; Plan (archaeology) ; Throughput ; Library science ; Engineering ; Political science ; Engineering management ; Artificial intelligence ; Management ; Computer science ; Engineering physics ; History ; Telecommunications ; Archaeology ; Economics Machine Learning in Materials Science ; Scientific Computing and Data Management
Authors: Giuseppe Ferrara | Cosimo Ricci | Ágata Gadaleta | Annamaria Gerardino | Angelo Didonna | Francesca Romana Bertani
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Chemistry — Analytical Chemistry

Portable Optical Spectroscopy and Machine Learning Techniques for Quantification of the Biochemical Content of Raw Food Materials Content (measure theory) ; Spectroscopy ; Raw material ; Food science ; Computer science ; Materials science ; Process engineering ; Environmental science ; Artificial intelligence ; Nanotechnology ; Chemistry ; Engineering ; Mathematics ; Physics Spectroscopy and Chemometric Analyses
Authors: V Jayaram | G. Prakash | K. R. Jayaram
Source: International Journal of Business Intelligence and Data Mining (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Text Analytics of Materials and Scanning Electron Microscope Image Classification of Cadmium Sulphate A framework is proposed using machine learning techniques to extract information (text analytics and SEM image classification) from material research documents. This paper provides complete information on material [cadmium sulphide (CdS)] research about the ongoing research problem, knowing the country and funding agencies, different new joint research collaborations, and characterising techniques used to understand material's phase transformation. The proposed system includes a new citation network connecting author, material, phase change, and funding organisation related to material and instruments. The abstract section is extracted to build a summary of material research. SEM images of CdS using a deep CNN method are classified into the (cubic, hexagon, crystalline and amorphous) crystal structure. Based on the observation with extensive literature studies, it is found that no such work has been done with materials, characterisation techniques, and acknowledgment section in the material research domain using deep learning and machine learning techniques. Computer science ; Cadmium ; Scanning electron microscope ; Analytics ; Image (mathematics) ; Data science ; Information retrieval ; Artificial intelligence ; Materials science ; Metallurgy ; Composite material Machine Learning in Materials Science
Authors: K. R. Jayaram | Geethika Prakash | Jayaram Vishakantaiah
Source: International Journal of Business Intelligence and Data Mining (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Text analytics of materials and scanning electron microscope image classification of cadmium sulphide A framework is proposed using machine learning techniques to extract information (text analytics and SEM image classification) from material research documents. This paper provides complete information on material [cadmium sulphide (CdS)] research about the ongoing research problem, knowing the country and funding agencies, different new joint research collaborations, and characterising techniques used to understand material's phase transformation. The proposed system includes a new citation network connecting author, material, phase change, and funding organisation related to material and instruments. The abstract section is extracted to build a summary of material research. SEM images of CdS using a deep CNN method are classified into the (cubic, hexagon, crystalline and amorphous) crystal structure. Based on the observation with extensive literature studies, it is found that no such work has been done with materials, characterisation techniques, and acknowledgment section in the material research domain using deep learning and machine learning techniques. Computer science ; Section (typography) ; Analytics ; Domain (mathematical analysis) ; Crystal (programming language) ; Data science ; Visual analytics ; Artificial intelligence ; Visualization Machine Learning in Materials Science
Authors: Yaolong Zhang | Bin Jiang
Source: arXiv (Cornell University) (preprint, 2023-04-16)
Field: Materials Science — Materials Chemistry

Universal Machine Learning for the Response of Atomistic Systems to External Fields Machine learned interatomic interaction potentials have enabled efficient and accurate molecular simulations of closed systems. However, external fields, which can greatly change the chemical structure and/or reactivity, have been seldom included in current machine learning models. This work proposes a universal field-induced recursively embedded atom neural network (FIREANN) model, which integrates a pseudo field vector-dependent feature into atomic descriptors to represent system-field interactions with rigorous rotational equivariance. This "all-in-one" approach correlates various response properties like dipole moment and polarizability with the field-dependent potential energy in a single model, very suitable for spectroscopic and dynamics simulations in molecular and periodic systems in the presence of electric fields. Especially for periodic systems, we find that FIREANN can overcome the intrinsic multiple-value issue of the polarization by training atomic forces only. These results validate the universality and capability of the FIREANN method for efficient first-principles modeling of complicated systems in strong external fields. Polarizability ; Universality (dynamical systems) ; Dipole ; Computer science ; Artificial neural network ; Force field (fiction) ; Molecular dynamics ; Statistical physics ; Artificial intelligence ; Physics ; Quantum mechanics ; Molecule Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Electrocatalysts for Energy Conversion
Authors: Anders S. Larsen | Toms Rekis | Anders Ø. Madsen
Source: Acta Crystallographica Section A Foundations and Advances (article, 2023-08-22)
Field: Materials Science — Materials Chemistry

Phasing neural network: <i>ab initio</i> determination of crystal structures at 2 Å resolution For a crystal structure to be solved from the diffraction data, it is necessary to obtain the complex structure factors, FH, of the measured reflections.Their squared moduli, |FH| 2 , are proportional to the measured intensities, but the phase angles needed to reconstruct the complete complex numbers cannot be determined experimentally.Since the exact solution to this phase problem is not known, several methods to overcome this crucial step in crystal structure determination have been developed over time.For example, direct methods or the charge flipping algorithm can be used for most organic, inorganic, and metalorganic structures.Nevertheless, these methods fail if, for example, the available data resolution is not sufficiently high, the completeness of the data is low, or if the number of atoms in the asymmetric unit is very high -as is the case for macromolecular crystals and some framework structures. Artificial neural network ; Ab initio ; Resolution (logic) ; Crystal (programming language) ; Crystal structure ; Ab initio quantum chemistry methods ; Materials science ; Crystallography ; Physics ; Artificial intelligence ; Computer science ; Chemistry ; Molecule ; Quantum mechanics X-ray Diffraction in Crystallography
Authors: Marcin Mińkowski | Lasse Laurson
Source: arXiv (Cornell University) (preprint, 2023-02-27)
Field: Materials Science — Materials Chemistry

Predicting elastic and plastic properties of small iron polycrystals by machine learning Deformation of crystalline materials is an interesting example of complex system behaviour. Small samples typically exhibit a stochastic-like, irregular response to externally applied stresses, manifested as significant sample-to-sample variation in their mechanical properties. In this work we study the predictability of the sample-dependent shear moduli and yield stresses of a large set of small cube-shaped iron polycrystals generated by Voronoi tesselation, by combining molecular dynamics simulations and machine learning. Training a convolutional neural network to infer the mapping between the initial polycrystalline structure of the samples and features of the ensuing stress-strain curves reveals that the shear modulus can be predicted better than the yield stress. We discuss our results in the context of the sensitivity of the system's response to small perturbations of its initial state. Materials science ; Context (archaeology) ; Shear modulus ; Elastic modulus ; Shear (geology) ; Moduli ; Stress (linguistics) ; Composite material ; Geology ; Physics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: K. P. Arslanov | Buntov E.A. | К. П. Арсланов | Е. А. Бунтов
Source: Electronic scientific archive of UrFU (Ural Federal University) (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

EVALUATION OF THE ACCURACY AND COMPUTATIONAL SPEED FOR THE METHOD OF MOLECULAR DYNAMICS WITH MACHINE LEARNING POTENTIAL FOR CARBON STRUCTURES Comparison of the machine learning potential and ReaxFF by studying the stability of different carbon structures during simulations in various temperature ranges and under Ar+ ion bombardment. Comparison of the potentials by simulating the ion-stimulated process of linear chain carbon film growth. Molecular dynamics ; Stability (learning theory) ; Process (computing) ; Carbon fibers ; ReaxFF ; Carbon chain Machine Learning in Materials Science ; Ion-surface interactions and analysis ; Diamond and Carbon-based Materials Research
Authors: Jianzhong Wu | Mengyang Gu
Source: arXiv (Cornell University) (preprint, 2023-11-09)
Field: Materials Science — Materials Chemistry

Perfecting Liquid-State Theories with Machine Intelligence Recent years have seen a significant increase in the use of machine intelligence for predicting electronic structure, molecular force fields, and the physicochemical properties of various condensed systems. However, substantial challenges remain in developing a comprehensive framework capable of handling a wide range of atomic compositions and thermodynamic conditions. This perspective discusses potential future developments in liquid-state theories leveraging on recent advancements of functional machine learning. By harnessing the strengths of theoretical analysis and machine learning techniques including surrogate models, dimension reduction and uncertainty quantification, we envision that liquid-state theories will gain significant improvements in accuracy, scalability and computational efficiency, enabling their broader applications across diverse materials and chemical systems. Scalability ; Computer science ; Dimension (graph theory) ; Artificial intelligence ; State (computer science) ; Perspective (graphical) ; Dimensionality reduction ; Machine learning ; Range (aeronautics) ; Reduction (mathematics) ; Management science ; Data science ; Nanotechnology ; Engineering ; Materials science ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Hyun Park | Xiaoli Yan | Ruijie Zhu | E. A. Huerta | Santanu Chaudhuri | Donny Cooper | Ian Foster | Emad Tajkhorshid
Source: arXiv (Cornell University) (preprint, 2023-06-14)
Field: Chemistry — Inorganic Chemistry

A generative artificial intelligence framework based on a molecular diffusion model for the design of metal-organic frameworks for carbon capture Metal-organic frameworks (MOFs) exhibit great promise for CO2 capture. However, finding the best performing materials poses computational and experimental grand challenges in view of the vast chemical space of potential building blocks. Here, we introduce GHP-MOFassemble, a generative artificial intelligence (AI), high performance framework for the rational and accelerated design of MOFs with high CO2 adsorption capacity and synthesizable linkers. GHP-MOFassemble generates novel linkers, assembled with one of three pre-selected metal nodes (Cu paddlewheel, Zn paddlewheel, Zn tetramer) into MOFs in a primitive cubic topology. GHP-MOFassemble screens and validates AI-generated MOFs for uniqueness, synthesizability, structural validity, uses molecular dynamics simulations to study their stability and chemical consistency, and crystal graph neural networks and Grand Canonical Monte Carlo simulations to quantify their CO2 adsorption capacities. We present the top six AI-generated MOFs with CO2 capacities greater than 2 $m mol/g$, i.e., higher than 96.9% of structures in the hypothetical MOF dataset. Metal-organic framework ; Molecular dynamics ; Rational design ; Nanotechnology ; Materials science ; Adsorption ; Computer science ; Chemistry ; Topology (electrical circuits) ; Biological system ; Computational chemistry ; Mathematics ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Carbon dioxide utilization in catalysis
Authors: Meenu Upadhyay | Kai Töpfer | Markus Meuwly
Source: arXiv (Cornell University) (preprint, 2023-07-06)
Field: Chemistry — Spectroscopy

Molecular Simulation for Atmospheric Reaction Exploration and Discovery: Non-Equilibrium Dynamics, Roaming and Glycolaldehyde Formation Following Photo-Induced Decomposition of syn-Acetaldehyde Oxide The decomposition and chemical dynamics for vibrationally excited syn-CH$_3$CHOO is followed based on statistically significant numbers of molecular dynamics simulations. Using a neural network-based reactive potential energy surface, transfer learned to the CASPT2 level of theory, the final total kinetic energy release and rotational state distributions of the OH fragment are in quantitative agreement with experiment. In particular the widths of these distributions are sensitive to the experimentally unknown strength of the O--O bond strength, for which values $D_e \in [22,25]$ kcal/mol are found. Due to the non-equilibrium nature of the process considered, the energy-dependent rates do not depend appreciably on the O--O scission energy. Roaming dynamics of the OH-photoproduct leads to formation of glycolaldehyde on the picosecond time scale with subsequent decomposition into CH$_2$OH+HCO. Atomistic simulations with global reactive machine-learned energy functions provide a viable route to quantitatively explore the chemistry and reaction dynamics for atmospheric reactions. Glycolaldehyde ; Chemistry ; Molecular dynamics ; Reaction dynamics ; Excited state ; Potential energy surface ; Picosecond ; Kinetic energy ; Potential energy ; Photodissociation ; Decomposition ; Bond cleavage ; Photochemistry ; Computational chemistry ; Chemical physics ; Physical chemistry ; Atomic physics ; Molecule ; Physics ; Organic chemistry Spectroscopy and Laser Applications ; Atmospheric Ozone and Climate ; Atmospheric and Environmental Gas Dynamics
Authors: Xu Zhao | Yaochen Xie | Youzhi Luo | Xuan Zhang | Xinyi Xu | Meng Liu | Kaleb Dickerson | Cheng Deng | Maho Nakata | Shuiwang Ji
Source: arXiv (Cornell University) (preprint, 2023-05-01)
Field: Materials Science — Materials Chemistry

3D Molecular Geometry Analysis with 2D Graphs Ground-state 3D geometries of molecules are essential for many molecular analysis tasks. Modern quantum mechanical methods can compute accurate 3D geometries but are computationally prohibitive. Currently, an efficient alternative to computing ground-state 3D molecular geometries from 2D graphs is lacking. Here, we propose a novel deep learning framework to predict 3D geometries from molecular graphs. To this end, we develop an equilibrium message passing neural network (EMPNN) to better capture ground-state geometries from molecular graphs. To provide a testbed for 3D molecular geometry analysis, we develop a benchmark that includes a large-scale molecular geometry dataset, data splits, and evaluation protocols. Experimental results show that EMPNN can efficiently predict more accurate ground-state 3D geometries than RDKit and other deep learning methods. Results also show that the proposed framework outperforms self-supervised learning methods on property prediction tasks. Benchmark (surveying) ; Testbed ; Computer science ; Geometry ; Deep learning ; Artificial neural network ; Artificial intelligence ; Topology (electrical circuits) ; Theoretical computer science ; Algorithm ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics