jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Hanwen Zhang | Veronika Jurásková | Fernanda Duarte
Source: ChemRxiv (preprint, 2024-07-08)
Field: Materials Science — Materials Chemistry

Modeling Chemical Processes in Explicit Solvents with Machine Learning Potentials Solvent effects influence all stages of the chemical processes, modulating the stability of intermediates and transition states, as well as altering reaction rates and product ratios. However, accurately modelling these effects remains challenging. Here, we present a general strategy for generating reactive machine learning potentials to model chemical processes in solution. Our approach combines active learning with descriptor-based selectors and automation, enabling the construction of data-efficient training sets that span the relevant chemical and conformational space. We apply this strategy to investigate a Diels-Alder reaction in water and methanol. The generated machine learning potentials enable us to obtain reaction rates that are in agreement with experimental data and analyse the influence of these solvents on the reaction mechanism. Our strategy offers an efficient approach to the routine modelling of chemical reactions in solution, opening up avenues for studying complex chemical processes in an efficient manner. Chemical space ; Chemical reaction ; Chemical process ; Computer science ; Stability (learning theory) ; Biochemical engineering ; Reaction mechanism ; Mechanism (biology) ; Biological system ; Artificial intelligence ; Chemistry ; Machine learning ; Organic chemistry ; Catalysis ; Physics ; Engineering ; Drug discovery Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Tomohito Amano | Tamio Yamazaki | Shinji Tsuneyuki
Source: arXiv (Cornell University) (preprint, 2024-07-11)
Field: Chemistry — Analytical Chemistry

A chemical bond-based machine learning model for dipole moment: Application to dielectric properties of liquid methanol and ethanol We introduce a versatile machine-learning scheme for predicting dipole moments of molecular liquids to study dielectric properties. We attribute the center of mass of Wannier functions, called Wannier centers, to each chemical bond and create neural network models that predict the Wannier centers for each chemical bond. Application to liquid methanol and ethanol shows that our neural network models successfully predict the dipole moment of various liquid configurations in close agreement with DFT calculations. We show that the dipole moment and dielectric constant in the liquids are greatly enhanced by the polarization of Wannier centers due to local intermolecular interactions. The calculated dielectric spectra agree well with experiments quantitatively over terahertz (THz) to infrared regions. Furthermore, we investigate the physical origin of THz absorption spectra of methanol, confirming the importance of translational and librational motions. Our method is applicable to other molecular liquids and can be widely used to study their dielectric properties. Moment (physics) ; Dielectric ; Dipole ; Methanol ; Bond dipole moment ; Ethanol ; Bond ; Materials science ; Condensed matter physics ; Transition dipole moment ; Engineering physics ; Chemical physics ; Chemistry ; Physics ; Organic chemistry ; Optoelectronics ; Classical mechanics ; Economics Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Thermodynamic properties of mixtures
Authors: Qianhong qiu | Changming Du
Source: Materials Today Communications (article, 2024-07-23)
Field: Materials Science — Materials Chemistry

An application of graph neural network in pollution abatement: Acceleration heterogeneous catalyst design Interpretability ; Catalysis ; Artificial neural network ; Materials science ; Atom (system on chip) ; Graph ; Computer science ; Biological system ; Artificial intelligence ; Theoretical computer science ; Embedded system Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Catalytic Processes in Materials Science
Authors: G.J. Jeevan Kumar | V D N Kumar Abbaraju | P. Sunil Reddy | B.S.A. Andrews
Source: International Research Journal on Advanced Engineering and Management (IRJAEM) (article, 2024-07-15)
Field: Chemistry — Spectroscopy

Integrating Machine Learning into Analytical Chemistry: A Focus on Pattern Recognition and Data Analysis in Spectrometry Machine learning with its integration into analytical chemistry, and more specifically spectrometry, is imposing new trends in data analysis and pattern recognition. On the other hand, spectral measurement is an important technique to analyze the influence of matter and electromagnetic radiation that generates complex and multidimensional data, which is difficult to study effectively with traditional methods. Infrared, Raman, mass, and nuclear magnetic resonance spectroscopy achieve high-throughput analyses by accuracy and efficiency with the help of machine learning algorithms, which include support vector machines, random forests, neural networks, and clustering techniques. This review represents an application of machine learning at preliminary data extraction, modeling, and validation stages in IR, Raman, mass, and NMR spectroscopy. In infrared spectroscopy, machine learning models help to identify organic compounds, while subtle improvements in spectral differences enable diagnosis by Raman spectroscopy. In mass spectrometry, machine learning helps in identifying complex mixtures and trace quantification, and in nuclear magnetic resonance spectroscopy, it improves the deconvolution of overlapping signals. Quality of data, computational needs, and multidisciplinary expertise required are still some of the challenges, but the continuous process of research and collaboration keeps innovating. In cloud computing, high performance can compute problems and attain real-time, high-degree analysis. With a view to providing researchers and practitioners in analytical chemistry with a better understanding of the status, challenges, and future prospects in this cross-application of machine learning in spectroscopic analysis, the present study is conducted. Chromatography ; Acetonitrile ; Chemistry ; High-performance liquid chromatography ; Phosphoric acid ; Impurity ; Analytical Chemistry (journal) ; Dosage form Analytical Chemistry and Chromatography ; Analytical Methods in Pharmaceuticals ; Multicomponent Synthesis of Heterocycles
Authors: Guillermo Vazquez | Daniel Sauceda | Raymundo Arróyave
Source: arXiv (Cornell University) (preprint, 2024-03-27)
Field: Materials Science — Materials Chemistry

Deciphering Chemical Ordering in High Entropy Materials: A Machine Learning-Accelerated High-throughput Cluster Expansion Approach The Cluster Expansion (CE) Method encounters significant computational challenges in multicomponent systems due to the computational expense of generating training data through density functional theory (DFT) calculations. This work aims to refine the cluster and structure selection processes to mitigate these challenges. We introduce a novel method that significantly reduces the computational load associated with the calculation of fitting parameters. This method employs a Graph Neural Network (GNN) model, leveraging the M3GNet network, which is trained using a select subset of DFT calculations at each ionic step. The trained surrogate model excels in predicting the volume and energy of the final structure for a relaxation run. By employing this model, we sample thousands of structures and fit a CE model to the energies of these GNN-relaxed structures. This approach, utilizing a large training dataset, effectively reduces the risk of overfitting, yielding a CE model with a root-mean-square error (RMSE) below 10 meV/atom. We validate our method's effectiveness in two test cases: the (Cr,Hf,Mo,Ta,Ti,Zr)B$_2$ diboride system and the Refractory High-Entropy Alloy (HEA) AlHfNbTaTiZr system. Our findings demonstrate the significant advantages of integrating a GNN model, specifically the M3GNet network, with CE methods for the efficient predictive analysis of chemical ordering in High Entropy Materials. The accelerating capabilities of the hybrid ML-CE approach to investigate the evolution of Short Range Ordering (SRO) in a large number of stoichiometric systems. Finally, we show how it is possible to correlate the strength of chemical ordering to easily accessible alloy parameters. Cluster expansion ; High dimensional ; Cluster (spacecraft) ; Computer science ; High entropy alloys ; Throughput ; Artificial intelligence ; Materials science ; Physics ; Thermodynamics ; Computer network ; Metallurgy ; Operating system Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; X-ray Diffraction in Crystallography
Authors: D. Di Pinto | Michael Greenwood | Nikolas Provatas
Source: arXiv (Cornell University) (preprint, 2024-06-21)
Field: Materials Science — Materials Chemistry

LeapFrog: Getting the Jump on Multi-Scale Materials Simulations Using Machine Learning The development of novel materials in recent years has been accelerated greatly by the use of computational modelling techniques aimed at elucidating the complex physics controlling microstructure formation in materials, the properties of which control material function. One such technique is the phase field method, a field theoretic approach that couples various thermophysical fields to microscopic order parameter fields that track the phases of microstructure. Phase field models are framed as multiple, non-linear, partial differential equations, which are extremely challenging to compute efficiently. Recent years have seen an explosion of computational algorithms aimed at enhancing the efficiency of phase field simulations. One such technique, adaptive mesh refinement (AMR), dynamically adapts numerical meshes to be highly refined around steep spatial gradients of the PDE fields and coarser where the fields are smooth. This reduces the number of computations per time step significantly, thus reducing the total time of computation. What AMR doesn't do is allow for adaptive time stepping. This work combines AMR with a neural network algorithm that uses a U-Net with a Convolutional Long-Short Term Memory (CLSTM) base to accelerate phase field simulations. Our neural network algorithm is described in detail and tested in on simulations of directional solidification of a dilute binary alloy, a paradigm that is highly practical for its relevance to the solidification of alloys. Jump ; Scale (ratio) ; Computer science ; Artificial intelligence ; Machine learning ; Industrial engineering ; Engineering ; Cartography ; Physics ; Geography Machine Learning in Materials Science
Authors: Daniel Wines | Kamal Choudhary
Source: arXiv (Cornell University) (preprint, 2024-12-13)
Field: Materials Science — Materials Chemistry

CHIPS-FF: Evaluating Universal Machine Learning Force Fields for Material Properties In this work, we introduce CHIPS-FF (Computational High-Performance Infrastructure for Predictive Simulation-based Force Fields), a universal, open-source benchmarking platform for machine learning force fields (MLFFs). This platform provides robust evaluation beyond conventional metrics such as energy, focusing on complex properties including elastic constants, phonon spectra, defect formation energies, surface energies, and interfacial and amorphous phase properties. Utilizing 16 graph-based MLFF models including ALIGNN-FF, CHGNet, MatGL, MACE, SevenNet, ORB, MatterSim and OMat24, the CHIPS-FF workflow integrates the Atomic Simulation Environment (ASE) with JARVIS-Tools to facilitate automated high-throughput simulations. Our framework is tested on a set of 104 materials, including metals, semiconductors and insulators representative of those used in semiconductor components, with each MLFF evaluated for convergence, accuracy, and computational cost. Additionally, we evaluate the force-prediction accuracy of these models for close to 2 million atomic structures. By offering a streamlined, flexible benchmarking infrastructure, CHIPS-FF aims to guide the development and deployment of MLFFs for real-world semiconductor applications, bridging the gap between quantum mechanical simulations and large-scale device modeling. Universal testing machine ; Computer science ; Materials science ; Composite material Machine Learning in Materials Science
Authors: Prasert Aengchuan | Perawat Boonpuek | Jintapha Klinsuk
Source: Materials science forum (article, 2024-09-27)
Field: Materials Science — Polymers and Plastics

Prediction of Stress Relaxation Behavior of Polymer Foam Using Artificial Neural Network Stress relaxation is one of the methods used to characterize polymer foam materials. Stress relaxation data are valuable and provide important information, as they can prevent failure or unsafe usage of these materials under different loads. The aim of this study is to introduce the artificial neural network (ANN) technique for predicting the stress relaxation of polymer foam over time. The neural network model was constructed with relaxation time, stress, and strain as input parameters, and normalized stress relaxation as the output. The results demonstrate that the ANN model achieved highly accurate predictions for stress. Materials science ; Artificial neural network ; Composite material ; Stress relaxation ; Polymer ; Stress (linguistics) ; Relaxation (psychology) ; Polymer science ; Artificial intelligence ; Creep ; Computer science Polymer Foaming and Composites ; Polymer composites and self-healing ; Natural Fiber Reinforced Composites
Authors: Ziyue Zou | Dedi Wang | Pratyush Tiwary
Source: arXiv (Cornell University) (preprint, 2024-09-18)
Field: Materials Science — Materials Chemistry

Graph Neural Network-State Predictive Information Bottleneck (GNN-SPIB) approach for learning molecular thermodynamics and kinetics Molecular dynamics simulations offer detailed insights into atomic motions but face timescale limitations. Enhanced sampling methods have addressed these challenges but even with machine learning, they often rely on pre-selected expert-based features. In this work, we present the Graph Neural Network-State Predictive Information Bottleneck (GNN-SPIB) framework, which combines graph neural networks and the State Predictive Information Bottleneck to automatically learn low-dimensional representations directly from atomic coordinates. Tested on three benchmark systems, our approach predicts essential structural, thermodynamic and kinetic information for slow processes, demonstrating robustness across diverse systems. The method shows promise for complex systems, enabling effective enhanced sampling without requiring pre-defined reaction coordinates or input features. Bottleneck ; Kinetics ; Graph ; Artificial neural network ; Information bottleneck method ; Computer science ; Artificial intelligence ; Chemistry ; Mathematics ; Statistical physics ; Thermodynamics ; Physics ; Theoretical computer science ; Cluster analysis Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Jingzi Zhang | Chengquan Zhong | Xiaoting Lu | Jiakai Liu | Kailong Hu | Xi Lin
Source: Science China Materials (article, 2024-08-23)
Field: Materials Science — Materials Chemistry

Crystal structure graph neural networks for high-performance superconducting critical temperature prediction Materials science ; Superconductivity ; Artificial neural network ; Crystal (programming language) ; Crystal structure ; Crystal structure prediction ; Condensed matter physics ; Artificial intelligence ; Crystallography ; Computer science ; Physics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Physics of Superconductivity and Magnetism
Authors: Jiaxin Tian | Haoyuan Li
Source: ChemRxiv (preprint, 2024-04-08)
Field: Materials Science — Materials Chemistry

Machine-Learning Discovered Crystallization Model for TwoDimensional Covalent Organic Frameworks: Towards Precise Control of the Crystal Quality The rational molecular design and experimental condition optimizations for two-dimensional co-valent organic frameworks (2D COFs) call for a crystallization model capable of capturing exper-imental time and size scales. However, accurately describing their crystallization process remains a significant challenge due to the presence of non-classical pathways. Here, we demonstrate the implementation of a machine-learning approach, overcoming the difficulties associated with bot-tom-up model derivation. The resulting model, referred to as NEgen1, establishes correlations among the induction time, nucleation rate, growth rate, material parameters, and common solu-tion synthesis conditions for 2D COFs that belong to the nucleation-elongation category. NEg-en1 represents the emergence of practical crystallization models for 2D COFs, enabling the direct calculation of their crystallization processes in both experimental times and sizes. The results elu-cidate the detailed competition between the nucleation and growth dynamics in solution, which has been inappropriately apprehended via classical, empirical models with assumptions invalid for 2D COFs. Importantly, we demonstrate the potential application of the NEgen1 model in opti-mizing the synthesis conditions, which has predominantly relied on empirical knowledge to date. The identification of conditions superior to those routinely used experimentally reveals a promis-ing strategy of gradually increasing monomer addition speed for growing large 2D COF crystals while maintaining a reasonable synthesis time. These results highlight the potential for systemati-cally improving the crystal quality of 2D COFs for wider applications. Crystallization ; Nucleation ; Computer science ; Crystal (programming language) ; Process (computing) ; Quality (philosophy) ; Crystal growth ; Artificial intelligence ; Materials science ; Biological system ; Physics ; Thermodynamics Covalent Organic Framework Applications ; Metal-Organic Frameworks: Synthesis and Applications ; Advanced Photocatalysis Techniques
Authors: Eric Qu | Aditi S. Krishnapriyan
Source: arXiv (Cornell University) (preprint, 2024-10-31)
Field: Materials Science — Materials Chemistry

The Importance of Being Scalable: Improving the Speed and Accuracy of Neural Network Interatomic Potentials Across Chemical Domains Scaling has been critical in improving model performance and generalization in machine learning. It involves how a model's performance changes with increases in model size or input data, as well as how efficiently computational resources are utilized to support this growth. Despite successes in other areas, the study of scaling in Neural Network Interatomic Potentials (NNIPs) remains limited. NNIPs act as surrogate models for ab initio quantum mechanical calculations. The dominant paradigm here is to incorporate many physical domain constraints into the model, such as rotational equivariance. We contend that these complex constraints inhibit the scaling ability of NNIPs, and are likely to lead to performance plateaus in the long run. In this work, we take an alternative approach and start by systematically studying NNIP scaling strategies. Our findings indicate that scaling the model through attention mechanisms is efficient and improves model expressivity. These insights motivate us to develop an NNIP architecture designed for scalability: the Efficiently Scaled Attention Interatomic Potential (EScAIP). EScAIP leverages a multi-head self-attention formulation within graph neural networks, applying attention at the neighbor-level representations. Implemented with highly-optimized attention GPU kernels, EScAIP achieves substantial gains in efficiency--at least 10x faster inference, 5x less memory usage--compared to existing NNIPs. EScAIP also achieves state-of-the-art performance on a wide range of datasets including catalysts (OC20 and OC22), molecules (SPICE), and materials (MPTrj). We emphasize that our approach should be thought of as a philosophy rather than a specific model, representing a proof-of-concept for developing general-purpose NNIPs that achieve better expressivity through scaling, and continue to scale efficiently with increased computational resources and training data. Artificial neural network ; Scalability ; Computer science ; Interatomic potential ; Artificial intelligence ; Statistical physics ; Biological system ; Computational chemistry ; Molecular dynamics ; Chemistry ; Physics ; Biology Machine Learning in Materials Science
Authors: Mashroor S. Nitol | Doyl Dickel | Saryu Fensin
Source: Materials Today Communications (article, 2024-09-10)
Field: Materials Science — Materials Chemistry

Neural network interatomic potential-driven analysis of phase stability in Ti–V alloys at the atomistic scale Materials science ; Interatomic potential ; Scale (ratio) ; Phase (matter) ; Molecular dynamics ; Atomic units ; Stability (learning theory) ; Chemical physics ; Thermodynamics ; Computational chemistry ; Machine learning Machine Learning in Materials Science ; Titanium Alloys Microstructure and Properties ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Jiaxin Tian | Haoyuan Li
Source: ChemRxiv (preprint, 2024-03-05)
Field: Materials Science — Materials Chemistry

Machine-Learning Discovered Crystallization Model for Two-Dimensional Covalent Organic Frameworks: Towards Precise Control of the Crystal Quality The rational molecular design and experimental condition optimizations for two-dimensional covalent organic frameworks (2D COFs) call for a crystallization model capable of capturing experimental time and size scales. However, accurately describing their crystallization process remains a significant challenge due to the presence of non-classical pathways. Here, we demon-strate the implementation of a machine-learning approach, overcoming the difficulties associ-ated with bottom-up model derivation. The resulting model, referred to as NEgen1, establishes correlations among the induction time, nucleation rate, growth rate, material parameters, and common solution synthesis conditions for 2D COFs that belong to the nucleation-elongation category. NEgen1 represents the emergence of practical crystallization models for 2D COFs, enabling the direct calculation of their crystallization processes in both experimental times and sizes. The results elucidate the detailed competition between the nucleation and growth dynam-ics in solution, which has been inappropriately apprehended via classical, empirical models with assumptions invalid for 2D COFs. Importantly, we demonstrate the potential application of the NEgen1 model in optimizing the synthesis conditions, which has predominantly relied on empirical knowledge to date. The identification of conditions superior to those routinely used experimentally reveals a promising strategy of gradually increasing monomer addition speed for growing large 2D COF crystals while maintaining a reasonable synthesis time. These results highlight the potential for systematically improving the crystal quality of 2D COFs for wider applications. Crystallization ; Nucleation ; Crystal (programming language) ; Computer science ; Crystal growth ; Quality (philosophy) ; Process (computing) ; Artificial intelligence ; Biological system ; Materials science ; Nanotechnology ; Biochemical engineering ; Chemistry ; Physics ; Thermodynamics ; Crystallography ; Engineering Covalent Organic Framework Applications ; Metal-Organic Frameworks: Synthesis and Applications ; Advanced Photocatalysis Techniques