jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yagyank Srivastava | Ankit Jain
Source: ArXiv.org (preprint, 2023-11-06)
Field: Materials Science — Materials Chemistry

End-to-end Material Thermal Conductivity Prediction through Machine Learning We investigated the accelerated prediction of the thermal conductivity of materials through end- to-end structure-based approaches employing machine learning methods. Due to the non-availability of high-quality thermal conductivity data, we first performed high-throughput calculations based on first principles and the Boltzmann transport equation for 225 materials, effectively more than doubling the size of the existing dataset. We assessed the performance of state-of-the-art machine learning models for thermal conductivity prediction on this expanded dataset and observed that all these models suffered from overfitting. To address this issue, we introduced a novel graph-based neural network model, which demonstrated more consistent and regularized performance across all evaluated datasets. Nevertheless, the best mean absolute percentage error achieved on the test dataset remained in the range of 50-60%. This suggests that while these models are valuable for expediting material screening, their current accuracy is still limited. Overfitting ; Expediting ; Thermal conductivity ; Computer science ; Machine learning ; Artificial neural network ; Artificial intelligence ; Graph ; Algorithm ; Materials science ; Engineering ; Theoretical computer science ; Composite material Machine Learning in Materials Science ; Thermal properties of materials
Authors: Or Shafir | Ilya Grinberg
Source: arXiv (Cornell University) (preprint, 2023-06-14)
Field: Materials Science — Materials Chemistry

Bonding-aware Materials Representation for Deep Learning Atomistic Models Deep potentials for molecular dynamics (MD) achieve first-principles accuracy at much lower computational cost. However, their use in large length- and time-scale simulations is limited by their lower speeds compared to analytical atomistic potentials, primarily due to network complexity and long embedding time. Here, based on the moments theorem, we develop a chemical-bonding-aware embedding for neural network potentials that achieve state-of-the-art accuracy in forces and local electronic density of states prediction with an ultrasmall 16x32 neural network resulting in significantly lower computational cost. Embedding ; Representation (politics) ; Artificial neural network ; Molecular dynamics ; Statistical physics ; Computer science ; Computational complexity theory ; Deep learning ; Scale (ratio) ; Artificial intelligence ; Theoretical computer science ; Algorithm ; Computational science ; Machine learning ; Computational chemistry ; Physics ; Chemistry ; Quantum mechanics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Fuel Cells and Related Materials
Authors: Atwakyire Moses | Ding Chen | Peng Wan
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Materials Science — Materials Chemistry

Prediction of Electrochemical Corrosion Behaviour of Magnesium Alloy Using Machine Learning Methods Corrosion ; Magnesium ; Magnesium alloy ; Materials science ; Alloy ; Metallurgy ; Electrochemistry ; Computer science ; Artificial intelligence ; Chemistry ; Electrode Corrosion Behavior and Inhibition ; Non-Destructive Testing Techniques ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Yangjun Wu | Xiansong Xu | Dario Poletti | Yi Fan | Chu Guo | Honghui Shang
Source: arXiv (Cornell University) (preprint, 2023-01-10)
Field: Materials Science — Materials Chemistry

A real neural network state for quantum chemistry The restricted Boltzmann machine (RBM) has been successfully applied to solve the many-electron Schr$\ddot{\text{o}}$dinger equation. In this work we propose a single-layer fully connected neural network adapted from RBM and apply it to study ab initio quantum chemistry problems. Our contribution is two-fold: 1) our neural network only uses real numbers to represent the real electronic wave function, while we obtain comparable precision to RBM for various prototypical molecules; 2) we show that the knowledge of the Hartree-Fock reference state can be used to systematically accelerate the convergence of the variational Monte Carlo algorithm as well as to increase the precision of the final energy. Artificial neural network ; Wave function ; Boltzmann machine ; Restricted Boltzmann machine ; Convergence (economics) ; Ab initio ; Electronic structure ; Quantum ; Function (biology) ; Computer science ; Boltzmann constant ; Quantum chemistry ; Statistical physics ; Energy (signal processing) ; Chemistry ; Molecule ; Computational chemistry ; Quantum mechanics ; Physics ; Artificial intelligence Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Quantum Computing Algorithms and Architecture
Authors: Aakash Bhardwaj | Akshdeep Singh Ahluwalia | Kamal Kishore Pant | Sreedevi Upadhyayula
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Materials Science — Materials Chemistry

A Principal Component Analysis Assisted Machine Learning Modeling and Validation of Methanol Formation Over Cu-Based Catalysts in Direct Co2 Hydrogenation Principal component analysis ; Catalysis ; Methanol ; Component (thermodynamics) ; Chemistry ; Process engineering ; Computer science ; Materials science ; Chemical engineering ; Artificial intelligence ; Thermodynamics ; Engineering ; Organic chemistry ; Physics Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Catalysts for Methane Reforming
Authors: Dong Hyeon Mok | Daeun Chloe Shin | Jonggeol Na | Seoin Back
Source: ChemRxiv (preprint, 2023-01-29)
Field: Materials Science — Materials Chemistry

Chemically Inspired Convolutional Neural Network using Electronic Structure Representation In recent years, a development of appropriate crystal representations for accurate prediction of inorganic crystal properties has been considered as one of the essential tasks to accelerate materials discovery through high-throughput virtual screening (HTVS). However, many of them were developed aiming to predict properties of the given structures, although property predictions of ground state structures using unrelaxed structures as inputs are much more important in practical HTVS. To tackle this challenge, we develop a chemically inspired convolutional neural network based on convolution block attention modules using density of states of unrelaxed initial structures (IS-DOS) as inputs. Our model, Electronic Structure Network (ESNet), achieved the highest accuracy for predicting formation energy, proving that IS-DOS is appropriate input for the property prediction and the attention module is capable of properly featurizing DOS signals by capturing contributions of each spin and orbital state. In addition, we statistically evaluated a stability screening performance of ESNet, measuring computational cost and capability of materials discovery simultaneously. We found that ESNet outperformed previously reported models and various models with different types of input features and architectures. Indeed, ESNet successfully discovered 926 stable materials from 15,318 unrelaxed structures with 82 % reduced computational cost compared to the complete DFT validation. Convolution (computer science) ; Computer science ; Block (permutation group theory) ; Property (philosophy) ; Representation (politics) ; Stability (learning theory) ; Convolutional neural network ; Crystal structure prediction ; Throughput ; Artificial neural network ; Computation ; Artificial intelligence ; Theoretical computer science ; Algorithm ; Machine learning ; Crystal structure ; Chemistry ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Hanwen Zhang | Veronika Jurásková | Fernanda Duarte
Source: ChemRxiv (preprint, 2023-07-19)
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 (MLPs) 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 demonstrate the versatility of this strategy by applying it to investigate a Diels-Alder reaction in water and methanol. The generated MLPs exhibit excellent agreement with experimental data and provide insights into the differences in reaction rates observed between the two solvents. 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 process ; Chemical reaction ; Computer science ; Stability (learning theory) ; Biochemical engineering ; Biological system ; Artificial intelligence ; Machine learning ; Chemistry ; Organic chemistry ; Engineering ; Drug discovery Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Meena Rittiruam | Sorawee Setasuban | Jakapob Noppakhun | Tinnakorn Saelee | Annop Ektarawong | Nuttanon Aumnongpho | Suphawich Boonchuay | Patcharaporn Khajondetchairit | Supareak Praserthdam | Björn Alling | Piyasan Praserthdam
Source: Advanced Theory and Simulations (article, 2023-04-01)
Field: Materials Science — Materials Chemistry

First‐Principles Density Functional Theory and Machine Learning Technique for the Prediction of Water Adsorption Site on PtPd‐Based High‐Entropy‐Alloy Catalysts (Adv. Theory Simul. 4/2023) First-Principles Density Functional Theory and Machine Learning Technique In article number 2200926, Supareak Praserthdam and co-workers screen for water-gas shift PtPd-based high-entropy alloys catalysts via Density Functional Theory and Machine Learning based on adsorption energy descriptor providing preferred H2O adsorption site since the H2O activation step plays an important role as the first step in such a reaction. Density functional theory ; Adsorption ; Entropy (arrow of time) ; Catalysis ; Alloy ; Functional theory ; Computer science ; Materials science ; Artificial intelligence ; Thermodynamics ; Machine learning ; Chemistry ; Physical chemistry ; Computational chemistry ; Physics ; Metallurgy Machine Learning in Materials Science ; Additive Manufacturing Materials and Processes ; High Entropy Alloys Studies
Authors: Wojciech G. Stark | Julia Westermayr | Oscar A. Douglas‐Gallardo | James E. Gardner | Scott Habershon | Reinhard J. Maurer
Source: arXiv (Cornell University) (preprint, 2023-05-18)
Field: Materials Science — Materials Chemistry

Machine learning interatomic potentials for reactive hydrogen dynamics at metal surfaces based on iterative refinement of reaction probabilities Reactive chemistry of molecular hydrogen at surfaces, notably dissociative sticking and hydrogen evolution, plays a crucial role in energy storage and fuel cells. Theoretical studies can help to decipher underlying mechanisms and reaction design, but studying dynamics at surfaces is computationally challenging due to the complex electronic structure at interfaces and the high sensitivity of dynamics to reaction barriers. In addition, ab initio molecular dynamics, based on density functional theory, is too computationally demanding to accurately predict reactive sticking or desorption probabilities as it requires averaging over tens of thousands of initial conditions. High-dimensional machine learning-based interatomic potentials are starting to be more commonly used in gas-surface dynamics, yet, robust approaches to generate reliable training data and to assess how model uncertainty affects the prediction of dynamic observables are not well established. Here, we employ ensemble learning to adaptively generate training data while assessing model performance with full uncertainty quantification for reaction probabilities of hydrogen scattering on different copper facets. We use this approach to investigate the performance of two message-passing neural networks, SchNet and PaiNN. Ensemble-based uncertainty quantification and iterative refinement allow us to expose the shortcomings of the invariant pairwise-distance-based feature representation in the SchNet model for gas-surface dynamics. Molecular dynamics ; Computer science ; Reaction dynamics ; Observable ; Density functional theory ; Statistical physics ; Chemistry ; Biological system ; Artificial intelligence ; Computational chemistry ; Physics ; Molecule Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Electrocatalysts for Energy Conversion
Authors: M.M. Korop
Source: Journal of Thermoelectricity (article, 2023-03-25)
Field: Materials Science — Materials Chemistry

Machine learning in thermoelectric materials science The paper presents machine learning methods and their application in thermoelectric materials science. The results of their application, strong points and application areas are shown. The difficulties that arise in the process of predicting the properties of thermoelectric materials and ways to overcome them were taken into account. Bibl. 30, Fig. 1, Tabl. 2. Materials science ; Thermoelectric effect ; Thermoelectric materials ; Engineering physics ; Nanotechnology ; Computer science ; Engineering ; Composite material ; Physics ; Thermal conductivity ; Thermodynamics Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science
Authors: Felix Döppel | Martin Votsmeier
Source: ChemRxiv (preprint, 2023-04-10)
Field: Materials Science — Materials Chemistry

Efficient Neural Network Models of Chemical Kinetics Using a Latent asinh Rate Transformation We propose a new modeling strategy to build efficient neural network representations of chemical kinetics. Instead of fitting the logarithm of rates, we embed the hyperbolic sine function into neural networks and fit the actual rates. We demonstrate this approach on two detailed surface mechanisms: the preferential oxidation of CO in the presence of H2 and the ammonia oxidation under industrially relevant conditions of the Ostwald process. Implementing the surrogate models into reactor simulations shows accurate results with a speed-up of 100 000. Overall, the approach proposed in this work will significantly facilitate the application of detailed mechanistic knowledge to the simulation-based design of realistic catalytic systems. We foresee that combining this approach with neural ordinary differential equations will allow building machine learning representations of chemical kinetics directly from experimental data. Artificial neural network ; Ordinary differential equation ; Computer science ; Logarithm ; Process (computing) ; Biological system ; Kinetics ; Transformation (genetics) ; Biochemical engineering ; Function (biology) ; Artificial intelligence ; Chemistry ; Differential equation ; Mathematics ; Physics ; Engineering Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Catalysis and Oxidation Reactions
Authors: Kryštof Březina | Hubert Beck | Ondřej Maršálek
Source: arXiv (Cornell University) (preprint, 2023-03-27)
Field: Materials Science — Materials Chemistry

Reducing the cost of neural network potential generation for reactive molecular systems Although machine-learning potentials have recently had substantial impact on molecular simulations, the construction of a robust training set can still become a limiting factor, especially due to the requirement of a reference ab initio simulation that covers all the relevant geometries of the system. Recognizing that this can be prohibitive for certain systems, we develop the method of transition tube sampling that mitigates the computational cost of training set and model generation. In this approach, we generate classical or quantum thermal geometries around a transition path describing a conformational change or a chemical reaction using only a sparse set of local normal mode expansions along this path and select from these geometries by an active learning protocol. This yields a training set with geometries that characterize the whole transition without the need for a costly reference trajectory. The performance of the method is evaluated on different molecular systems with the complexity of the potential energy landscape increasing from a single minimum to a double proton-transfer reaction with high barriers. Our results show that the method leads to training sets that give rise to models applicable in classical and path integral simulations alike that are on par with those based directly on ab initio calculations while providing the computational speed-up we have come to expect from machine-learning potentials. Computer science ; Path (computing) ; Set (abstract data type) ; Artificial neural network ; Ab initio ; Trajectory ; Quantum ; Molecular dynamics ; Statistical physics ; Artificial intelligence ; Computational chemistry ; Physics ; Chemistry ; Quantum mechanics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Fuel Cells and Related Materials
Authors: Ethan M. Sunshine | Muhammed Shuaibi | Zachary W. Ulissi | John R. Kitchin
Source: arXiv (Cornell University) (preprint, 2023-09-09)
Field: Materials Science — Materials Chemistry

Chemical Properties from Graph Neural Network-Predicted Electron Densities According to density functional theory, any chemical property can be inferred from the electron density, making it the most informative attribute of an atomic structure. In this work, we demonstrate the use of established physical methods to obtain important chemical properties from model-predicted electron densities. We introduce graph neural network architectural choices that provide physically relevant and useful electron density predictions. Despite not training to predict atomic charges, the model is able to predict atomic charges with an order of magnitude lower error than a sum of atomic charge densities. Similarly, the model predicts dipole moments with half the error of the sum of atomic charge densities method. We demonstrate that larger data sets lead to more useful predictions in these tasks. These results pave the way for an alternative path in atomistic machine learning, where data-driven approaches and existing physical methods are used in tandem to obtain a variety of chemical properties in an explainable and self-consistent manner. Dipole ; Artificial neural network ; Electron density ; Computer science ; Statistical physics ; Graph ; Electron ; Atomic charge ; Property (philosophy) ; Physics ; Biological system ; Machine learning ; Theoretical computer science ; Quantum mechanics ; Molecule Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Joy Datta | Dibakar Datta | Vidushi Sharma
Source: arXiv (Cornell University) (preprint, 2023-06-25)
Field: Materials Science — Materials Chemistry

Transferable and Robust Machine Learning Model for Predicting Stability of Si Anodes for Multivalent Cation Batteries Data-driven methodology has become a key tool in computationally predicting material properties. Currently, these techniques are priced high due to computational requirements for generating sufficient training data for high-precision machine learning models. In this study, we present a Support Vector Regression (SVR)-based machine learning model to predict the stability of silicon (Si) - alkaline metal alloys, with a strong emphasis on the transferability of the model to new silicon alloys with different electronic configurations and structures. We elaborate on the role of the structural descriptor in imparting transferability to the model that is trained on limited data (~750 Si alloys) derived from the Material Project database. Three popular descriptors, namely X-Ray Diffraction (XRD), Sine Coulomb Matrix (SCM), and Orbital Field Matrix (OFM), are evaluated for representing Si alloys. The material structures are represented by descriptors in the SVR model, coupled with hyperparameter tuning techniques like Grid Search CV and Bayesian Optimization (BO), to find the best performing model for predicting total energy, formation energy and packing fraction of the Si alloy systems. The models are trained on Si alloys with lithium (Li), sodium (Na), potassium (K), magnesium (Mg), calcium (Ca), and aluminum (Al) metals, where Si-Na and Si-Al systems are used as test structures. Our results show that XRD, an experimentally derived characterization of structures, performs most reliably as a descriptor for total energy prediction of new Si alloys. The study demonstrates that by qualitatively selection of training data, using hyperparameter tuning methods, and employing appropriate structural descriptors, the data requirements for robust and accurate ML models can be reduced. Hyperparameter optimization ; Hyperparameter ; Artificial intelligence ; Machine learning ; Computer science ; Support vector machine ; Materials science ; Bayesian optimization ; Stability (learning theory) ; Algorithm Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Advancements in Battery Materials
Authors: Haoxiang Li | Hao Tian | Yewei Chen | Sian Xiao | Yangyang Gao | Xiuying Zhao | Liqun Zhang
Source: ChemRxiv (preprint, 2023-03-13)
Field: Materials Science — Materials Chemistry

Analyzing and Predicting the Viscosity of Polymer Nanocomposites in the Conditions of Temperature, Shear Rate, and Nanoparticles Loading with Molecular Dynamics Simulations and Machine Learning Predicting the viscosity of polymer nanocomposites (PNCs) is of critical importance as it governs a dominant role in PNCs processing and application. Machine learning (ML) algorithms, enabled by pre-existing experimental and computational data, have emerged as robust tools for the prediction of quantitative relationships between feature parameters and various physical properties of materials. In this work, we employed nonequilibrium molecular dynamics (NEMD) simulation with ML models to systematically investigate the viscosity of PNCs over a wide range of NPs loading, shear rates and temperature.With the increase in shear rates, shear thinning takes place as the value of viscosity decreases on the orders of magnitude. In addition, the NPs loading -dependence and T-dependence reduce to the extent that it is not visible at high shear rates. The value of viscosity for PNCs is proportional to NPs loading and inversely proportional to T below the intermediate shear rates. Using the obtained NEMD results, four machine learning models were trained to provides eective predictions for the viscosity. The extreme gradient boosting (XGBoost) model yields the best accuracy in viscosity prediction under complex conditions and is further used to evaluate feature importance. This quantitative structure-property relationship (QSPR) model used physical views to investigate the effect of process parameters, such as temperature, NPs loading and shear rates, on the viscosity of PNCs and paves the path for theoretically proposing reasonable parameters for successful processing. Shear rate ; Viscosity ; Materials science ; Shear thinning ; Rheology ; Shear viscosity ; Shear (geology) ; Thermodynamics ; Machine learning ; Mechanics ; Composite material ; Computer science ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Polymer crystallization and properties