jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jin Dai | Santosh Adhikari | Mingjian Wen
Source: arXiv (Cornell University) (preprint, 2024-05-03)
Field: Materials Science — Materials Chemistry

Uncertainty Quantification and Propagation in Atomistic Machine Learning Machine learning (ML) offers promising new approaches to tackle complex problems and has been increasingly adopted in chemical and materials sciences. Broadly speaking, ML models employ generic mathematical functions and attempt to learn essential physics and chemistry from a large amount of data. Consequently, because of the limited physical or chemical principles in the functional form, the reliability of the predictions is oftentimes not guaranteed, particularly for data far out of distribution. It is critical to quantify the uncertainty in model predictions and understand how the uncertainty propagates to downstream chemical and materials applications. Herein, we review existing uncertainty quantification (UQ) and uncertainty propagation (UP) methods for atomistic ML under a united framework of probabilistic modeling. We first categorize the UQ methods, with the aim to elucidate the similarities and differences between them. We also discuss performance metrics to evaluate the accuracy, precision, calibration, and efficiency of the UQ methods and techniques for model recalibration. With these metrics, we survey existing benchmark studies of the UQ methods using molecular and materials datasets. Furthermore, we discuss UP methods to propagate the uncertainty obtained from ML models in widely used materials and chemical simulation techniques, such as molecular dynamics and microkinetic modeling. We also provide remarks on the challenges and future opportunities of UQ and UP in atomistic ML. Uncertainty quantification ; Propagation of uncertainty ; Computer science ; Artificial intelligence ; Machine learning ; Statistical physics ; Physics ; Algorithm Machine Learning in Materials Science ; Ion-surface interactions and analysis ; Advanced Materials Characterization Techniques
Authors: Daniel Farfán | Carolina Gómez-Márquez | Dania Sandoval-Nuñez | Omar Paredes | J. Alejandro Morales
Source: Franklin Open (article, 2024-05-24)
Field: Materials Science — Materials Chemistry

BARTReact: SELFIES-driven precision in reaction modeling We introduce Bidirectional and Auto-Regressive Transformer for Reactions (BARTReact), a selfsupervised deep learning model designed to predict chemical reactions. Built on the powerful Bidirectional and Auto-Regressive Transformer (BART) architecture, BARTReact is trained using the SELF-referencIng Embedded Strings (SELFIES), a molecular representation that ensures the production of only viable molecules, achieving an outstanding accuracy of 98.6%. Transformer ; Computer science ; Architecture ; Artificial intelligence ; Representation (politics) ; Deep learning ; Machine learning ; Engineering ; Electrical engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Topic Modeling
Authors: Yù Zhang | Chuntian Zhou | Fengfeng Liang | Guangjie Liu | Jinlong Zhu
Source: Coatings (article, 2024-10-03)
Field: Materials Science — Materials Chemistry

Magnetic Prediction of Doped Two-Dimensional Nanomaterials Based on Swin–ResNet Magnetism is an important property of doped two-dimensional nanostructures. By introducing dopant atoms or molecules, the electronic structure and magnetic behavior of the two-dimensional nanostructures can be altered. However, the complexity of the doping process requires different strategies for the preparation and testing of various types, layers, and scales of doped two-dimensional materials using traditional techniques. This process is resource-intensive, inefficient, and can pose safety risks when dealing with chemically unstable materials. Deep learning-based methods offer an effective solution to overcome these challenges and improve production efficiency. In this study, a deep learning-based method is proposed for predicting the magnetism of doped two-dimensional nanostructures. An image dataset was constructed for deep learning using a publicly available database of doped two-dimensional nanostructures. The ResNet model was enhanced by incorporating the Swin Transformer module, resulting in the Swin–ResNet network architecture. A comparative analysis was conducted with various deep learning models, including ResNet, Res2net, ResneXt, and Swin Transformer, to evaluate the performance of the optimized model in predicting the magnetism of doped two-dimensional nanostructures. The optimized model demonstrated significant improvements in magnetism prediction, with a best accuracy of 0.9. Nanomaterials ; Doping ; Materials science ; Nanotechnology ; Optoelectronics Machine Learning in Materials Science ; Advanced Memory and Neural Computing ; Ferroelectric and Negative Capacitance Devices
Authors: Rui Ding | Jianguo Liu | Hua Kang | Xuebin Wang | Xiaoben Zhang | Minhua Shao | Yuxin Chen | Junhong Chen
Source: arXiv (Cornell University) (preprint, 2024-07-05)
Field: Materials Science — Materials Chemistry

Leveraging Data Mining, Active Learning, and Domain Adaptation in a Multi-Stage, Machine Learning-Driven Approach for the Efficient Discovery of Advanced Acidic Oxygen Evolution Electrocatalysts Developing advanced catalysts for acidic oxygen evolution reaction (OER) is crucial for sustainable hydrogen production. This study introduces a novel, multi-stage machine learning (ML) approach to streamline the discovery and optimization of complex multi-metallic catalysts. Our method integrates data mining, active learning, and domain adaptation throughout the materials discovery process. Unlike traditional trial-and-error methods, this approach systematically narrows the exploration space using domain knowledge with minimized reliance on subjective intuition. Then the active learning module efficiently refines element composition and synthesis conditions through iterative experimental feedback. The process culminated in the discovery of a promising Ru-Mn-Ca-Pr oxide catalyst. Our workflow also enhances theoretical simulations with domain adaptation strategy, providing deeper mechanistic insights aligned with experimental findings. By leveraging diverse data sources and multiple ML strategies, we establish an efficient pathway for electrocatalyst discovery and optimization. This comprehensive, data-driven approach represents a paradigm shift and potentially new benchmark in electrocatalysts research. Domain adaptation ; Adaptation (eye) ; Computer science ; Domain (mathematical analysis) ; Active learning (machine learning) ; Oxygen evolution ; Machine learning ; Artificial intelligence ; Chemistry ; Biology Machine Learning in Materials Science ; Fuel Cells and Related Materials
Authors: Daniel Willimetz | Andreas Erlebach | Christopher J. Heard | Lukáš Grajciar
Source: ChemRxiv (preprint, 2024-09-24)
Field: Chemistry — Spectroscopy

27Al NMR chemical shifts in zeolite MFI via machine learning acceleration of structure sampling and shift prediction Acceleration ; Zeolite ; Sampling (signal processing) ; Chemical shift ; Chemical physics ; Computer science ; Materials science ; Chemistry ; Physical chemistry ; Physics ; Computer vision ; Classical mechanics ; Catalysis ; Organic chemistry Molecular spectroscopy and chirality ; Advanced NMR Techniques and Applications
Authors: Daniel Boschmann | Christian Stieghorst | David J. Knezevic | Loubna Kadri | Oliver Niggemann
Source:  (article, 2024-08-18)
Field: Chemistry — Analytical Chemistry

Automation of PGAA Spectra Analysis with Deep Learning Analyzing Prompt Gamma Activation Analysis (PG AA) spectra poses significant challenges, particularly in accurately identifying and quantifying the elements present. Traditional expert analysis, while effective, is time-consuming. This paper addresses the need for an efficient, automated solution to enhance the analysis process. The research investigates the use of machine learning (ML) and deep learning (DL) algorithms in the automated analysis of PGAA spectra. We aim to establish a new metric for comparing automated analysis with expert analysis, providing a baseline using Linear Regression, Random Forest, 1D Convolutional Neural Network, Feed Forward Neural Network, and Autoencoder algorithms. Established metrics like Mean Square Error (MSE) and Mean Absolute Error (MAE) are utilized to compare the performance of these automated approaches against traditional expert analysis. Using actual spectra from various research projects and semi-real augmented data, the study demonstrates that the Feed Forward Neural Network (FFNN) and Autoencoder algorithms can effectively predict the magnitude of the present elements. These findings suggest especially D L algorithms could signifi-cantly assist researchers and industry personnel by providing a rough overview of the material and saving valuable time. However, the automated approach requires further refinement, particularly in handling noisy data, predicting additional crucial information, and integrating more prior knowledge into the anal-ysis. This research offers valuable insights into the application of ML algorithms in spectral analysis and lays a foundation for further advancements in the field. Computer science ; Automation ; Artificial intelligence ; Engineering Spectroscopy and Chemometric Analyses ; CCD and CMOS Imaging Sensors
Authors: James Goff | Coreen Mullen | Shizhong Yang | Oleg N. Starovoytov | Mitchell Wood
Source: Journal of Physics Condensed Matter (article, 2024-11-26)
Field: Materials Science — Materials Chemistry

Generalized representative structures for atomistic systems A new method is presented to generate atomic structures that reproduce the essential characteristics of arbitrary material systems, phases, or ensembles. Previous methods allow one to reproduce the essential characteristics (e.g. the chemical disorder) of a large random alloy within a small crystal structure. The ability to generate small representations of random alloys, along with the restriction to crystal systems, results from using the fixed-lattice cluster correlations to describe structural characteristics. A more general description of the structural characteristics of atomic systems is obtained using complete sets of atomic environment descriptors. These are used within for generating representative atomic structures without restriction to fixed lattices. A general data-driven approach is provided here utilizing the atomic cluster expansion (ACE) basis. The<i>N</i>-body ACE descriptors are a complete set of atomic environment descriptors that span both chemical and spatial degrees of freedom and are used within for describing atomic structures. The generalized representative structure (GRS) method presented within generates small atomic structures that reproduce ACE descriptor distributions corresponding to arbitrary structural and chemical complexity. It is shown that systematically improvable representations of crystalline systems on fixed parent lattices, amorphous materials, liquids, and ensembles of atomic structures may be produced efficiently through optimization algorithms. With the GRS method, we highlight reduced representations of atomistic machine-learning training datasets that contain similar amounts of information and small 40-72 atom representations of liquid phases. The ability to use GRS methodology as a driver for informed novel structure generation is also demonstrated. The advantages over other data-driven methods and state-of-the-art methods restricted to high-symmetry systems are highlighted. Cluster (spacecraft) ; Crystal structure ; Atom (system on chip) ; Statistical physics ; Basis (linear algebra) ; Atomic radius ; Lattice (music) ; Amorphous solid ; Computer science ; Chemistry ; Physics ; Mathematics ; Crystallography ; Quantum mechanics ; Geometry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: G. Zhao | Taiping Hu | Hongshuai Wang | Lirong Wu | S.C. Lu | Fan Yang | Shangqian Chen | Zhifeng Gao | Xinyan Wang | Zheng Cheng
Source: ChemRxiv (preprint, 2024-12-27)
Field: Materials Science — Materials Chemistry

Data-Driven Parametrization of All-atom force fields for Organic Semiconductors Organic semiconductors (OSCs) composed of π conjugated molecules have gained significant interest in the study of bulk properties such as molecular arrangement and electron mobility. However, the types of torsion in the traditional force field (FF) are limited, fail to cover the chemical space of π conjugated molecules, and thus, hinder further molecular dynamics (MD) simulation to deduce these bulk properties through statistical mechanics. In this study, we introduce OSCFF, which supports various types of torsion for conjugated molecules and enables the generation of RESP charges with high accuracy through the neural network (NN). To develop the OSCFF, we construct two expansive and highly diverse molecular datasets: one consists of around 56,000 fragment geometries with torsion profiles, and another consists of around 472,000 optimized molecule geometries with RESP charges. The OSCFF demonstrates high accuracy in predicting torsional energy profiles, RESP charges, and the radial distribution function (RDF) for conjugated molecule systems. Furthermore, our OSCFF is compatible with the GAFF2 and a pipeline is provided for automatically generating the Gromacs supported topology file. We expect OSCFF will reduce the manual effort required for MD simulations of OSCs and serve as a valuable tool for multiple stages of OSCs design. Parametrization (atmospheric modeling) ; Atom (system on chip) ; Organic semiconductor ; Semiconductor ; Physics ; Force field (fiction) ; Classical mechanics ; Atomic physics ; Quantum mechanics ; Computer science Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Advanced Memory and Neural Computing
Authors: Sai Govind Hari Kumar | Carlota Bozal‐Ginesta | Ning Wang | Jehad Abed | Chung Hsuan Shan | Zhenpeng Yao | Alán Aspuru‐Guzik
Source: ChemRxiv (preprint, 2024-06-06)
Field: Materials Science — Materials Chemistry

From computational screening to the synthesis of a promising OER catalyst The search for new materials can be laborious and expensive. Given the challenges that mankind faces today concerning the climate change crisis, the need to accelerate materials discovery for applications like water-splitting could be very relevant for a renewable economy. In this work, we introduce a computational framework to predict the activity of oxygen evolution reaction (OER) catalysts, in order to accelerate the discovery of materials that can facilitate water splitting. We use this framework to screen 6155 ternary-phase spinel oxides and have isolated 33 candidates which are predicted to have potentially high OER activity. We have also trained a machine learning model to predict the binding energies of the *O, *OH and *OOH intermediates calculated within this workflow to gain a deeper understanding of the relationship between electronic structure descriptors and OER activity. Out of the 33 candidates predicted to have high OER activity, we have synthesized three compounds and characterized them using linear sweep voltammetry to gauge their performance in OER. From these three catalyst materials, we have identified a new material, Co2.5Ga0.5O4, that is competitive with benchmark OER catalysts in the literature with a low overpotential of 220mV at 10mAcm-2 and a Tafel slope at 56.0 mV dec-1. Given the vast size of chemical space as well as the success of this technique to date, we believe that further application of this computational framework based on the high-throughput virtual screening of materials can lead to the discovery of additional novel, high-performing OER catalysts. Catalysis ; Computer science ; Chemistry ; Organic chemistry Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Catalysis and Hydrodesulfurization Studies
Authors: Tim Kircher | Felix Döppel | Martin Votsmeier
Source: ChemRxiv (preprint, 2024-03-06)
Field: Materials Science — Materials Chemistry

Embedding Physics into Neural ODEs to learn Kinetics from Integral Reactors While the digitalization of chemical research and industry is vastly increasing the amountof data for developing kinetic models, model parametrization is not keeping up. To take advantage of the full potential of this data, machine learning tools are required that autonomously learn kinetics from reactor data. Previously, we introduced Global Reaction Neural Networks with embedded stoichiometry and thermodynamics for kinetic modelling. When trained as a neural ordinary differential equation (neural ODE), they discovered kinetics from integral reactor measurements of an equilibrium limited steam reforming reactor whereas conventional neural ODEs failed. We now extend their application to another industrially relevant case of reactors operating at full conversion. Using the preferential oxidation of CO in H2 rich streams as an example, we show that the physics-embedded neural network discovers kinetics from stiff systems containing cases of both full conversion and equilibrium limitation using integral reactor data Ode ; Artificial neural network ; Ordinary differential equation ; Embedding ; Kinetics ; Batch reactor ; Parametrization (atmospheric modeling) ; Computer science ; Differential equation ; Chemistry ; Applied mathematics ; Mathematics ; Physics ; Artificial intelligence ; Mathematical analysis ; Organic chemistry ; Catalysis ; Classical mechanics Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Catalytic Processes in Materials Science
Authors: Francesco Sapio | Filippo Aglietti | Paolo Ferreri | Alexandru Savuca
Source: SAE International Journal of Advances and Current Practices in Mobility (article, 2024-09-18)
Field: Materials Science — Materials Chemistry

Neural-Network-Based Modeling of SCR Systems for Emission Simulation: A Comprehensive Approach &lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;Selective Catalytic Reduction (SCR) systems are crucial for automotive emissions control, as they are essential to comply with stringent emissions regulations. Model-based SCR controls are used to minimize NOx emissions in a broad range of real-word driving scenarios, constantly adapting the urea injection to diverse load and temperature operating conditions, also accounting for different catalyst ageing status. In this framework, Neural Networks (NN) based models offer a promising alternative to reduced-order physical models or map-based controls.&lt;/div&gt;&lt;div class="htmlview paragraph"&gt;This study introduces a hybrid modeling approach for SCR systems, leveraging the integration of machine learning techniques with detailed physics-based models. A high fidelity 1D-CFD plant model of a SCR catalyst, previously calibrated on experimental data, was used as digital twin of the real component. A standardized simulation protocol was defined to virtually characterize the SCR thermal and chemical behavior under the full range of operating conditions typically covered during the real operation of the system. The generated dataset, including hard-to-measure physical quantities such as the catalyst wall temperature and the ammonia storage, was used to train and validate the neural network models. In particular, Recurrent Neural Networks (RNN) were used to mimic catalyst wall temperature and ammonia storage temporal trends, while Feed Forward Neural Networks (FFNN) were applied to model SCR outlet temperature and species concentrations such as NOx and NH3.&lt;/div&gt;&lt;div class="htmlview paragraph"&gt;The NN-based SCR model predictive capabilities were assessed against experimental driving cycles data. The results show that the NN model is able to accurately capture the non-linear characteristics of the system behavior, even under the highly transient conditions typical of real-driving scenarios, thus confirming the reliability of the proposed methodology.&lt;/div&gt;&lt;/div&gt; Computer science ; Artificial neural network ; Artificial intelligence Catalytic Processes in Materials Science ; Vehicle emissions and performance ; Advanced Combustion Engine Technologies
Authors: Shreya Sinha | Bruno Mladineo | Ivor Lončarić | Peter Saalfrank
Source: ChemRxiv (preprint, 2024-08-28)
Field: Materials Science — Materials Chemistry

Multi-dimensional neural network interatomic potentials for CO on NaCl(100) The advent of machine learning (ML) models has unlocked new possibilities in the realm of interatomic potentials. An equivariant, graph neural network (NN), NequIP, recently developed by Batzner et al., [1,2] is utilized for a versatile system, CO on a NaCl(100) surface, to construct interatomic potentials and mediate efficient large-scale atomistic simulations with ab initio molecular dynamics accuracy. We report two neural network potentials, one trained on equilibrium configurations at finite temperatures (T = 30, 300 K), and the other additionally trained upon non-equilibrium trajectories of pre-excited CO adsorbates. We demonstrate first applications of the ML potentials for (i) adsorption energies and barriers for reactions, (ii) potential energy landscapes for submonolayer and monolayer coverages, (iii) vibrational spectra at finite temperatures as well as (iv) vibrational relaxation dynamics. Further possible applications are discussed. Excited state ; Artificial neural network ; Ab initio ; Relaxation (psychology) ; Potential energy surface ; Molecular dynamics ; Interatomic potential ; Ab initio quantum chemistry methods ; Chemistry ; Molecular physics ; Statistical physics ; Materials science ; Chemical physics ; Physics ; Computer science ; Computational chemistry ; Atomic physics ; Machine learning ; Molecule ; Quantum mechanics Machine Learning in Materials Science ; CO2 Reduction Techniques and Catalysts ; Advanced Chemical Physics Studies
Authors: Rasmus M. Borup | Nicolai Ree | Jan H. Jensen
Source: ChemRxiv (preprint, 2024-03-14)
Field: Chemistry — Organic Chemistry

pKalculator: A pKa predictor for C-H bonds Determining the pKa values of various C-H sites in organic molecules offers valuable insights for synthetic chemists in predicting reaction sites. As molecular complexity increases, this task becomes more challenging. This paper introduces pKalculator, a quantum chemical (QM)-based workflow for automatic computations of C-H pKa values, which is used to generate a training dataset for a machine learning model (ML). The QM workflow is benchmarked against 695 experimentally determined C-H pKa values. The ML model is trained on a diverse dataset of 775 molecules with 3910 C-H sites. Our ML model predicts C-H pKa values with a mean absolute error (MAE) and a root mean squared error (RMSE) of 1.24 and 2.15 pKa units, respectively. Furthermore, we employ our model on 1043 pKa-dependent reactions (Aldol, Claisen, and Michael) and successfully indicate the reaction sites with a Matthew’s correlation coefficient (MCC) of 0.82. Bond ; Chemistry ; Economics Free Radicals and Antioxidants ; Organic Chemistry Cycloaddition Reactions
Authors: Sina Zare Pakzad | Mohammad Nasr Esfahani | D. Canadinç | B. Erdem Alaca
Source: Computational Materials Science (article, 2024-10-23)
Field: Materials Science — Materials Chemistry

Machine learning-driven atomistic analysis of mechanical behavior in silicon nanowires Nanowire ; Silicon nanowires ; Materials science ; Silicon ; Nanotechnology ; Molecular dynamics ; Chemical physics ; Engineering physics ; Metallurgy ; Chemistry ; Physics ; Computational chemistry Machine Learning in Materials Science ; Nanowire Synthesis and Applications ; Force Microscopy Techniques and Applications