jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: G. H. Teichert | S. Das | Mostafa Faghih Shojaei | Jamie Holber | T. Mueller | L. Hung | V. Gavini | K. Garikipati
Source: arXiv (Cornell University) (preprint, 2023-02-17)
Field: Materials Science — Materials Chemistry

Bridging scales with Machine Learning: From first principles statistical mechanics to continuum phase field computations to study order disorder transitions in LixCoO2 LixTMO2 (TM=Ni, Co, Mn) forms an important family of cathode materials for Li-ion batteries, whose performance is strongly governed by Li composition-dependent crystal structure and phase stability. Here, we use LixCoO2 (LCO) as a model system to benchmark a machine learning-enabled framework for bridging scales in materials physics. We focus on two scales: (a) assemblies of thousands of atoms described by density functional theory-informed statistical mechanics, and (b) continuum phase field models to study the dynamics of order-disorder transitions in LCO. Central to the scale bridging is the rigorous, quantitatively accurate, representation of the free energy density and chemical potentials of this material system by coarsegraining formation energies for specific atomic configurations. We develop active learning workflows to train recently developed integrable deep neural networks for such high-dimensional free energy density and chemical potential functions. The resulting, first principles-informed, machine learning-enabled, phase-field computations allow us to study LCO cathodes' phase evolution in terms of temperature, morphology, charge cycling and particle size. Bridging (networking) ; Statistical mechanics ; Statistical physics ; Phase transition ; Computation ; Atomic units ; Computer science ; Physics ; Algorithm ; Thermodynamics ; Quantum mechanics Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications
Authors: Ritesh Kumar | Canh Vu | Chibueze V. Amanchukwu
Source: ECS Meeting Abstracts (article, 2023-08-28)
Field: Materials Science — Materials Chemistry

A Big Data Approach to Rational Design and Discovery of Electrolytes Lithium-metal batteries (LMBs) have much higher energy densities than the current state-of-the-art lithium-ion batteries that drive the green energy landscape. However, their wide-scale commercialization is hampered by the lack of desirable electrolytes that suffer from stability and cyclability issues. The discovery of electrolytes for LMBs has been mainly guided by chemical intuition and trial-and-error experiments. While the emerging machine learning (ML)-based initiatives for electrolyte discovery are promising, most studies have been carried out on limited datasets of ionic conductivity, they remain experimentally unvalidated, and lack in design principles governing performance of efficient electrolytes. The present work addresses these problems by curating exhaustive datasets of the most critical parameters affecting the LMB electrolytes – ionic conductive, oxidative stability, and Coulombic efficiency and by developing highly accurate ML and deep learning (DL) models. The models were consistent with the known chemical principles of ionic conductivity and pinpointed few non-trivial trends. After undergoing stringent checks on out-of-distribution (OOD) in-house data sets for the three target properties, the ML models were finally deployed on large unlabeled datasets to identify new and promising electrolytes, and a score was devised to rank the molecules in terms of the predicted target properties. The subsequent experimental investigations led to an entirely new class of LMB electrolytes, establishing the efficacy of our heuristic approach. The proposed “multi-pronged” strategy for LMB electrolyte discovery will also enable researchers to find extraordinary electrolytes for other battery chemistries. Electrolyte ; Computer science ; Ionic conductivity ; Chemistry Machine Learning in Materials Science ; Advanced Battery Technologies Research
Authors: Evan Askanazi | Ilya Grinberg
Source: arXiv (Cornell University) (preprint, 2023-04-03)
Field: Materials Science — Materials Chemistry

Distance-based Analysis of Machine Learning Prediction Reliability for Datasets in Materials Science and Other Fields Despite successful use in a wide variety of disciplines for data analysis and prediction, machine learning (ML) methods suffer from a lack of understanding of the reliability of predictions due to the lack of transparency and black-box nature of ML models. In materials science and other fields, typical ML model results include a significant number of low-quality predictions. This problem is known to be particularly acute for target systems which differ significantly from the data used for ML model training. However, to date, a general method for characterization of the difference between the predicted and training system has not been available. Here, we show that a simple metric based on Euclidean feature space distance and sampling density allows effective separation of the accurately predicted data points from data points with poor prediction accuracy. We show that the metric effectiveness is enhanced by the decorrelation of the features using Gram-Schmidt orthogonalization. To demonstrate the generality of the method, we apply it to support vector regression models for various small data sets in materials science and other fields. Our method is computationally simple, can be used with any ML learning method and enables analysis of the sources of the ML prediction errors. Therefore, it is suitable for use as a standard technique for the estimation of ML prediction reliability for small data sets and as a tool for data set design. Computer science ; Metric (unit) ; Reliability (semiconductor) ; Orthogonalization ; Machine learning ; Data mining ; Artificial intelligence ; Generality ; Data set ; Support vector machine ; Algorithm Machine Learning in Materials Science ; Industrial Vision Systems and Defect Detection ; Machine Learning and Data Classification
Authors: Javier Emilio Alfonso Ramos | Rebecca M. Neeser | Thijs Stuyver
Source: ChemRxiv (preprint, 2023-12-07)
Field: Materials Science — Materials Chemistry

Repurposing Quantum Chemical Descriptor Datasets for on-the-Fly Generation of Informative Reaction Representations: Application to Hydrogen Atom Transfer Reactions In this work, we explore how existing datasets of quantum chemical properties can be repurposed to build data-efficient downstream machine learning models, with a particular focus on predicting the activation energy of hydrogen atom transfer (HAT) reactions. Starting from a valence bond (VB) analysis of a generic HAT process, a set of informative descriptors is identified. Next, a surrogate neural network model is constructed to predict an informative representation, based on the identified VB descriptors, with the help of a publicly available dataset of (pre-computed) quantum chemical properties of organic radicals. We demonstrate that coupling the resulting on-the-fly informative representation to a secondary machine-learning model for activation energy prediction outperforms various predictive model architectures starting from conventional machine-learning inputs by a wide margin, at no additional computational cost. As a bonus, by basing their final predictions on physically meaningful descriptors, our models become inherently interpretable. Finally, because of the extreme data efficiency of our descriptor-augmented models, we are able to fine-tune and apply them to small datasets across various reaction conditions, settings and application domains, ranging from regular (liquid phase) synthesis, over metabolism and drug design, to atmospheric chemistry. Computer science ; Artificial intelligence ; Artificial neural network ; Machine learning ; Quantum chemical ; Representation (politics) ; Biological system ; Chemistry ; Molecule Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Free Radicals and Antioxidants
Source: Nature Computational Science (article, 2023-05-01)
Field: Materials Science — Materials Chemistry

A graph neural network for predicting the adsorption energy of molecules on metal surfaces Adsorption ; Artificial neural network ; Molecule ; Metal ; Materials science ; Computer science ; Chemistry ; Artificial intelligence ; Physical chemistry ; Metallurgy ; Organic chemistry Machine Learning in Materials Science ; Advanced Chemical Sensor Technologies ; Gas Sensing Nanomaterials and Sensors
Authors: Honglei YU | Dexi WANG | Gong Chen | Yunlong LI
Source: SSRN Electronic Journal (article, 2023-01-01)
Field: Chemistry — Inorganic Chemistry

Insights into the Diffusion Coefficient and Adsorption Energy of NH3 in MGCL2 from Molecular Simulation, Experiments, And Machine Learning Adsorption ; Effective diffusion coefficient ; Diffusion ; Energy (signal processing) ; Materials science ; Thermodynamics ; Chemistry ; Psychology ; Physical chemistry ; Physics ; Statistics ; Mathematics ; Medicine Metal-Organic Frameworks: Synthesis and Applications ; Gas Sensing Nanomaterials and Sensors ; Ammonia Synthesis and Nitrogen Reduction
Authors: Malte Esders | Gimmy Alex Fernandez Ramirez | Michael Gastegger | Satya Swarup Samal
Source: Computers & Chemical Engineering (article, 2023-12-29)
Field: Materials Science — Materials Chemistry

Scaling up machine learning-based chemical plant simulation: A method for fine-tuning a model to induce stable fixed points Initialization ; Solver ; Scaling ; Chemical plant ; Computer science ; Simple (philosophy) ; Graph ; Algorithm ; Mathematics ; Theoretical computer science ; Engineering Machine Learning in Materials Science ; Advanced Control Systems Optimization ; Scientific Computing and Data Management
Authors: Kai Riedmiller | Patrick Reiser | Elizaveta Bobkova | Kiril Maltsev | Ganna Gryn’ova | Pascal Friederich | Frauke Gräter
Source: ChemRxiv (preprint, 2023-02-12)
Field: Materials Science — Materials Chemistry

Predicting reaction barriers of hydrogen atom transfer in proteins Hydrogen atom transfer (HAT) reactions are important in many biological systems. As these reactions are hard to observe experimentally, it is of high interest to shed light on them using simulations. Here, we present a machine learning model based on graph neural networks for the prediction of activation energies of HAT reactions in proteins. It is trained on more than 17,000 energy barriers calculated using hybrid density functional theory. We built and evaluated the model in the context of HAT in collagen, but the same workflow can easily be applied to HAT reactions in other biological or synthetic polymers. We obtain for relevant reactions (small reaction distances) a model with good predictive power (R2 <0.9 and mean absolute error of <3 kcal/mol). As the inference speed is high, this model enables evaluations of many chemical situations in rapid succession. When combined with molecular dynamics in a kinetic Monte-Carlo scheme, the model paves the way toward reactive simulations. Hydrogen atom ; Context (archaeology) ; Monte Carlo method ; Inference ; Hydrogen ; Molecular machine ; Chemistry ; Workflow ; Computer science ; Molecular dynamics ; Biological system ; Statistical physics ; Computational chemistry ; Materials science ; Physics ; Artificial intelligence ; Nanotechnology ; Mathematics Corrosion Behavior and Inhibition ; Collagen: Extraction and Characterization ; Photopolymerization techniques and applications
Authors: Daisuke Wakabayashi | Kohei Shimamura | Akihide Koura | Fuyuki Shimojo
Source: Journal of the Physical Society of Japan (article, 2023-06-16)
Field: Materials Science — Materials Chemistry

Large-scale Molecular-dynamics Simulations of SiO<sub>2</sub> Melt under High Pressure with Robust Machine-learning Interatomic Potentials We have carried out molecular-dynamics simulations of SiO2 melt at 60 GPa in a system of about 30,000 atoms, with robust machine-learning interatomic potentials based on ab initio calculations. The large-scale simulations not only reproduced the ab initio calculations, but were also found to significantly improve the precision of the intermediate-range structure. Interatomic potentials determined using machine learning easily destabilize simulations of highly covalent melts under high pressure in an extended system with over one order of magnitude more atoms than those in the initial calculations used for data generation. The combination of potential averaging and active learning can efficiently suppress computational instability. Molecular dynamics ; Interatomic potential ; Ab initio quantum chemistry methods ; Ab initio ; Materials science ; Instability ; Range (aeronautics) ; Chemical physics ; Molecular physics ; Statistical physics ; Physics ; Computational chemistry ; Chemistry ; Molecule ; Mechanics ; Quantum mechanics Machine Learning in Materials Science ; High-pressure geophysics and materials ; Theoretical and Computational Physics
Authors: Austin Tripp | Sergio Bacallado | Sukriti Singh | José Miguel Hernández-Lobato
Source: arXiv (Cornell University) (preprint, 2023-06-26)
Field: Chemistry — Spectroscopy

Tanimoto Random Features for Scalable Molecular Machine Learning The Tanimoto coefficient is commonly used to measure the similarity between molecules represented as discrete fingerprints, either as a distance metric or a positive definite kernel. While many kernel methods can be accelerated using random feature approximations, at present there is a lack of such approximations for the Tanimoto kernel. In this paper we propose two kinds of novel random features to allow this kernel to scale to large datasets, and in the process discover a novel extension of the kernel to real-valued vectors. We theoretically characterize these random features, and provide error bounds on the spectral norm of the Gram matrix. Experimentally, we show that these random features are effective at approximating the Tanimoto coefficient of real-world datasets and are useful for molecular property prediction and optimization tasks. Kernel (algebra) ; Computer science ; Pattern recognition (psychology) ; Artificial intelligence ; Kernel method ; Metric (unit) ; Similarity (geometry) ; Algorithm ; Variable kernel density estimation ; Random forest ; Extension (predicate logic) ; Scalability ; Mathematics ; Support vector machine ; Discrete mathematics Molecular spectroscopy and chirality ; Face and Expression Recognition ; Neural Networks and Applications
Authors: M. A. Perfilev | Pavel M. Vassiliev | А. А. Озеров | D. V. Maltsev | A. R. Pluzhnikova | D. V. Merezhkina
Source: Russian Chemical Bulletin (article, 2023-04-01)
Field: Chemistry — Organic Chemistry

New acetamide derivatives of quinazoline-2,4(1H,3H)-dione: neural network prediction, synthesis, and psychotropic activity Chemistry ; Acetamide ; GABAA receptor ; Anxiolytic ; Docking (animal) ; Diazepam ; Flurbiprofen ; Flumazenil ; Pharmacology ; Combinatorial chemistry ; Receptor ; Organic chemistry ; Biochemistry Quinazolinone synthesis and applications ; Phenothiazines and Benzothiazines Synthesis and Activities ; Multicomponent Synthesis of Heterocycles
Authors: Srimai Vuppala | Ramesh Kumar Chitumalla | Seyong Choi | Tae‐Ho Kim | Hwangseo Park | Joonkyung Jang
Source: ACS Omega (article, 2023-12-19)
Field: Materials Science — Materials Chemistry

Machine Learning-Assisted Computational Screening of Adhesive Molecules Derived from Dihydroxyphenyl Alanine Marine mussels adhere to virtually any surface via 3,4-dihydroxyphenyl-L-alanines (L-DOPA), an amino acid largely contained in their foot proteins. The biofriendly, water-repellent, and strong adhesion of L-DOPA are unparalleled by any synthetic adhesive. Inspired by this, we computationally designed diverse derivatives of DOPA and studied their potential as adhesives or coating materials. We used first-principles calculations to investigate the adsorption of the DOPA derivatives on graphite. The presence of an electron-withdrawing group, such as nitrogen dioxide, strengthens the adsorption by increasing the π-π interaction between DOPA and graphite. To quantify the distribution of electron charge and to gain insights into the charge distribution at interfaces, we performed Bader charge analysis and examined charge density difference plots. We developed a quantitative structure-property relationship (QSPR) model using an artificial neural network (ANN) to predict the adsorption energy. Using the three-dimensional and quantum mechanical electrostatic potential of a molecule as a descriptor, the present quantum NN model shows promising performance as a predictive QSPR model. Adsorption ; Charge density ; Molecule ; Coating ; Quantitative structure–activity relationship ; Adhesion ; Adhesive ; Computational chemistry ; Density functional theory ; Alanine ; Electrostatics ; Quantum ; Materials science ; Chemistry ; Chemical physics ; Nanotechnology ; Amino acid ; Organic chemistry ; Stereochemistry ; Physical chemistry ; Physics Machine Learning in Materials Science ; Molecular Junctions and Nanostructures ; Surface Chemistry and Catalysis
Authors: Jan Čermák | Ondřej Ambrož | Martin Zouhar | Patrik Jozefovič | Šárka Mikmeková
Source: Microscopy and Microanalysis (article, 2023-07-22)
Field: Materials Science — Surfaces, Coatings and Films

Methodology for Collecting and Aligning Correlative SEM, CLSM and LOM Images of Bulk Material Microstructure to Create a Large Machine Learning Training Dataset Journal Article Methodology for Collecting and Aligning Correlative SEM, CLSM and LOM Images of Bulk Material Microstructure to Create a Large Machine Learning Training Dataset Get access Jan Čermák, Jan Čermák Department of Electron Microscopy, Institute of Scientific Instruments of the Czech Academy of Science, Brno, Czechia Corresponding author: cermak@isibrno.cz Search for other works by this author on: Oxford Academic Google Scholar Ondřej Ambrož, Ondřej Ambrož Department of Electron Microscopy, Institute of Scientific Instruments of the Czech Academy of Science, Brno, Czechia Search for other works by this author on: Oxford Academic Google Scholar Martin Zouhar, Martin Zouhar Department of Electron Microscopy, Institute of Scientific Instruments of the Czech Academy of Science, Brno, Czechia Search for other works by this author on: Oxford Academic Google Scholar Patrik Jozefovič, Patrik Jozefovič Department of Electron Microscopy, Institute of Scientific Instruments of the Czech Academy of Science, Brno, Czechia Search for other works by this author on: Oxford Academic Google Scholar Šárka Mikmeková Šárka Mikmeková Department of Electron Microscopy, Institute of Scientific Instruments of the Czech Academy of Science, Brno, Czechia Search for other works by this author on: Oxford Academic Google Scholar Microscopy and Microanalysis, Volume 29, Issue Supplement_1, 1 August 2023, Pages 2016–2018, https://doi.org/10.1093/micmic/ozad067.1044 Published: 22 July 2023 Czech ; Library science ; Computer science ; Philosophy Electron and X-Ray Spectroscopy Techniques ; Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications
Authors: Ankur Barsode | Aneesh A.M.
Source: AIP conference proceedings (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Bypassing traditional molecular dynamics with artificial neural networks Computer science ; Artificial neural network ; Artificial intelligence ; Artificial life ; Dynamics (music) ; Nervous system network models ; Types of artificial neural networks ; Time delay neural network ; Psychology Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; Advanced Physical and Chemical Molecular Interactions
Authors: Xiaoliang Pan | Ryan Snyder | Jianing Wang | Chance Lander | Carly Wickizer | Richard Van | Andrew D. Chesney | Yuanfei Xue | Yuezhi Mao | Ye Mei | Jingzhi Pu | Yihan Shao
Source: ChemRxiv (preprint, 2023-08-25)
Field: Materials Science — Materials Chemistry

The Training of Machine Learning Potentials for Reactive Systems: A Colab Tutorial on Basic Models In the last several years, there has been a surge in the development of machine learning potential (MLP) models for describing molecular systems. We are interested in a particular area of this field — the training of system-specific MLPs for reactive systems — with the goal of using these MLPs to accelerate free energy simulations of chemical and enzyme reactions. To help new members in our labs become familiar with the basic techniques, we have put together a self-guided Colab tutorial (https://cc-ats.github.io/mlp_tutorial/), which we expect to be also useful to other young researchers in the community. Our tutorial begins with the introduction of simple fitting neural network (FNN) and kernel-based (using Gaussian Process Regression, GPR) models by fitting the two-dimensional Müller-Brown potential. Subsequently, two simple descriptors are presented for extracting features of molecular systems: symmetry functions (including the ANI variant) and embedding neural networks (such as DeepPot-SE). Lastly, these features will be fed into FNN and GPR models to reproduce the energy/force of molecular configurations of the Claisen rearrangement. Computer science ; Artificial intelligence ; Simple (philosophy) ; Artificial neural network ; Kriging ; Machine learning ; Process (computing) ; Energy (signal processing) ; Kernel (algebra) ; Field (mathematics) ; Physics ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Kai Brunnengräber | Katharina Jeschonek | M. A. George | Guirong Zhang | Bastian J. M. Etzold
Source: Chemistry - Methods (article, 2023-03-27)
Field: Chemistry — Electrochemistry

Ionic Liquid Modified Electrocatalysts: a STEM‐EDX Approach for Identification of Local Distributions within Ionomer Containing Catalysts Layers Abstract Driven by the transition to a CO 2 ‐neutral energy economy, research on polymer electrolyte fuel cells gained much interest during the last decade, with researchers trying to overcome the sluggish kinetics of the oxygen reduction reaction (ORR) limiting their performance. Modification of existing ORR catalysts with small amounts of ionic liquids (IL) represents an innovative approach to altering the catalytic activity and stability. ILs are supposed to take effect by modifying the local microenvironment at electrochemical interfaces. Nevertheless, a thorough understanding about the local distribution of ILs over solid catalysts is still lacking, hindering the IL modification strategy to be a generic approach to rationally modulating the catalytic performance of a catalyst. In this study we employed STEM‐EDS spectral imaging to locate the IL distribution on the catalyst in presence of Nafion TM . To overcome the difficulties associated with low energy STEM‐EDS we setup a sophisticated data processing routine based on machine learning. Ionic liquid ; Catalysis ; Ionomer ; Electrolyte ; Electrochemistry ; Chemical engineering ; Nafion ; Materials science ; Fuel cells ; Polymer ; Limiting ; Nanotechnology ; Chemistry ; Electrode ; Organic chemistry ; Engineering ; Physical chemistry ; Mechanical engineering ; Composite material Electrochemical Analysis and Applications ; Fuel Cells and Related Materials ; Electrocatalysts for Energy Conversion