jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Challenger Mishra | Niklas von Wolff | Abhinav Tripathi | Éric Brémond | Annika Preiss | Amit Kumar
Source: ChemRxiv (preprint, 2022-01-10)
Field: Materials Science — Materials Chemistry

Predicting Ruthenium Catalysed Hydrogenation of Esters using Machine Learning Catalytic hydrogenation of esters is a sustainable approach for the production of fine chemicals, and pharmaceutical drugs. However, the efficiency and cost of catalysts are often the bottlenecks in the commercialization of such technologies. The conventional approach of catalyst discovery is based on empiricism that makes the discovery process time-consuming and expensive. There is an urgent need to develop effective approaches to discover efficient catalysts for hydrogenation reactions. We demonstrate here the approach of machine learning for the prediction of out-comes for the catalytic hydrogenation of esters. Our models can predict the reaction yields with high mean accuracies of up to 91% (test set) and suggest that the use of certain chemical descriptors selectively can result in a more accurate model. Furthermore, cata-lysts and some of their corresponding descriptors can also be pre-dicted with mean accuracies of 85%, and >90%, respectively. Catalysis ; Ruthenium ; Commercialization ; Biochemical engineering ; Process (computing) ; Chemistry ; Computer science ; Drug discovery ; Catalytic hydrogenation ; Combinatorial chemistry ; Machine learning ; Organic chemistry ; Artificial intelligence ; Engineering ; Business Machine Learning in Materials Science ; Topic Modeling ; Data Quality and Management
Authors: Naoya Chiba | Yuta Suzuki | Tatsunori Taniai | Ryo Igarashi | Yoshitaka Ushiku | Kotaro Saito | Kanta Ono
Source: arXiv (Cornell University) (preprint, 2022-12-08)
Field: Materials Science — Materials Chemistry

Neural Structure Fields with Application to Crystal Structure Autoencoders Representing crystal structures of materials to facilitate determining them via neural networks is crucial for enabling machine-learning applications involving crystal structure estimation. Among these applications, the inverse design of materials can contribute to explore materials with desired properties without relying on luck or serendipity. We propose neural structure fields (NeSF) as an accurate and practical approach for representing crystal structures using neural networks. Inspired by the concepts of vector fields in physics and implicit neural representations in computer vision, the proposed NeSF considers a crystal structure as a continuous field rather than as a discrete set of atoms. Unlike existing grid-based discretized spatial representations, the NeSF overcomes the tradeoff between spatial resolution and computational complexity and can represent any crystal structure. We propose an autoencoder of crystal structures that can recover various crystal structures, such as those of perovskite structure materials and cuprate superconductors. Extensive quantitative results demonstrate the superior performance of the NeSF compared with the existing grid-based approach. Artificial neural network ; Autoencoder ; Artificial intelligence ; Computer science ; Crystal (programming language) ; Crystal structure prediction ; Crystal structure ; Discretization ; Grid ; Field (mathematics) ; Algorithm ; Mathematics ; Geometry ; Chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Xinggao Gong | Mao Su | Ji‐Hui Yang | Hongjun Xiang
Source: Research Square (preprint, 2022-11-11)
Field: Materials Science — Materials Chemistry

Efficient determination of the Hamiltonian and electronic properties using graph neural network Abstract Despite past decades have witnessed many successes of machine learning methods in physical sciences, prediction of the Hamiltonian, and thus electronic properties, is still unsatisfactory. Here, based on graph neural network architecture, we present an extendable neural network model to determine the Hamiltonian from ab initio data, with only local atomic structures as inputs. The local coordinates information, encoded using the convolutional neural network and designed to preserve Hermitian symmetry, is used to map hopping parameters onto local structures. We demonstrate the performance of our model using graphene and SiGe random alloys as examples. We show that our neural network model, although trained using small-size systems, can predict the Hamiltonian, as well as electronic properties such as band structures and densities of states (DOS) for large-size systems within the ab initio accuracy, justifying its extensibility. In combination with the high efficiency of our model, which takes only seconds to get the Hamiltonian of a 1728-atom system, present work provides a general framework to predict electronic properties efficiently and accurately, which provides new insights into computational physics and will accelerate the research for large-scale materials. Hamiltonian (control theory) ; Graph ; Artificial neural network ; Computer science ; Mathematics ; Combinatorics ; Artificial intelligence ; Mathematical optimization Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Surface and Thin Film Phenomena
Authors: Hoje Chun | Joonhee Kang | Dohun Kang | Sung Jun Hong | Junyoung Heo | Hyeongjin Cho | Jaehyeon Heo | Byung Hyo Kim | Jungwon Park | Byungchan Han
Source: Research Square (preprint, 2022-03-21)
Field: Materials Science — Materials Chemistry

Tracking the 3D atomic structures during thermal treatment and catalytic activity of individual Pt nanoparticles Tracking (education) ; Catalysis ; Nanoparticle ; Thermal ; Nanotechnology ; Materials science ; Chemical engineering ; Chemistry ; Psychology ; Engineering ; Physics ; Thermodynamics ; Organic chemistry Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Electrocatalysts for Energy Conversion
Authors: Elliot Chang | Mavrik Zavarin | Haruko Wainwright
Source: Goldschmidt2022 abstracts (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

A Chemistry Informed Hybrid Machine Learning Approach to Predict Metal-Mineral Surface Interactions Computer science ; Metal ; Mineral ; Artificial intelligence ; Machine learning ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; History and advancements in chemistry
Authors: 亮 高橋 | Akira Takahashi | 史康 大場 | Fumiyasu Oba
Source: Institutional Repositories DataBase (IRDB) (article, 2022-10-01)
Field: Materials Science — Materials Chemistry

Autonomous Materials Exploration System via High-Throughput Calculations and Machine learning Robotics ; Robot ; Feature (linguistics) ; Automation ; Work (physics) Machine Learning in Materials Science ; AI-based Problem Solving and Planning ; Advanced Neural Network Applications
Authors: Arindam Debnath | Wesley F. Reinhart
Source: arXiv (Cornell University) (preprint, 2022-12-31)
Field: Materials Science — Materials Chemistry

Code and data for 'Investigating representation schemes for surrogate modeling of High Entropy Alloys The design of new High Entropy Alloys that can achieve exceptional mechanical properties is presently of great interest to the materials science community. However, due to the difficulty of designing these alloys using traditional methods, machine learning has recently emerged as an essential tool. Particularly, the screening of candidate alloy compositions using surrogate models has become a mainstay of materials design in recent years. Many of these models use the atomic fractions of the alloying elements as inputs. However, there are many possible representation schemes for encoding alloy compositions, including both unstructured and structured variants. As the input features play a critical role in determining surrogate model performance, we have systematically compared these representation schemes on the basis of their performance in single-task deep learning models and in transfer learning scenarios. The results from these tests indicate that compared to the unstructured and randomly ordered schemes, chemically meaningful arrangements of elements within spatial representation schemes generally lead to better models. However, we also observed that tree-based models using only the atomic fractions as input were able to outperform these models in transfer learning. Representation (politics) ; Computer science ; High entropy alloys ; Entropy (arrow of time) ; Machine learning ; Task (project management) ; Artificial intelligence ; Alloy ; Materials science ; Engineering Machine Learning in Materials Science ; High Entropy Alloys Studies ; Advanced Materials Characterization Techniques
Authors: Yu Zhang | Shicheng Wei | Bo Wang | Linwei Li
Source: Journal of Physics Conference Series (article, 2022-10-01)
Field: Materials Science — Materials Chemistry

An Improved Artificial Neural Network Elastic Constant Prediction Method Based on Particle Swarm Optimization As machine learning methods keep developing in materials science, using machine learning to mine novel materials has become a research hotspot. For microscopic crystals, the first-principles calculation of elastic constants has always been a conventional research method. However, the first-principles calculation method is time-consuming and laborious, which limits the development of materials. In order to achieve efficient discovery of new materials, this paper proposed an improved artificial neural network (ANN) method to predict the elastic modulus of crystals improved by particle swarm optimization (PSO), which build the PSO-ANN model in use of structures and elastic constants of crystals. Experiments show that the root mean square error (RMSE) can reach within 1GPa. To verify the accuracy of the method for other crystal predictions, the MgZn 2 crystal is modelled and calculated by using the first-principles calculation method. Comparing with the prediction method proposed, the average error of the predicted elastic constants is within the range 5.6%. Particle swarm optimization ; Artificial neural network ; Mean squared error ; Range (aeronautics) ; Computer science ; Elastic modulus ; Algorithm ; Materials science ; Biological system ; Artificial intelligence ; Mathematical optimization ; Mathematics ; Statistics ; Composite material Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Nuclear Materials and Properties
Authors: Alejandro Ribés | François Mazen | Lucas Meyer
Source: SPIRE - Sciences Po Institutional REpository (article, 2022-11-13)
Field: Materials Science — Materials Chemistry

In Situ Monitoring and Steering Deep Learning Training from Numerical Simulations in ParaView-Catalyst International audience In situ ; Training (meteorology) ; Computer science ; Artificial intelligence ; Simulation ; Meteorology ; Physics Catalytic Processes in Materials Science ; Neural Networks and Applications ; Air Quality Monitoring and Forecasting
Authors: Saeed Mohammadzadeh | Peerasait Prachaseree | Emma Lejeune
Source: arXiv (Cornell University) (preprint, 2022-12-01)
Field: Materials Science — Materials Chemistry

Investigating Deep Learning Model Calibration for Classification Problems in Mechanics Recently, there has been a growing interest in applying machine learning methods to problems in engineering mechanics. In particular, there has been significant interest in applying deep learning techniques to predicting the mechanical behavior of heterogeneous materials and structures. Researchers have shown that deep learning methods are able to effectively predict mechanical behavior with low error for systems ranging from engineered composites, to geometrically complex metamaterials, to heterogeneous biological tissue. However, there has been comparatively little attention paid to deep learning model calibration, i.e., the match between predicted probabilities of outcomes and the true probabilities of outcomes. In this work, we perform a comprehensive investigation into ML model calibration across seven open access engineering mechanics datasets that cover three distinct types of mechanical problems. Specifically, we evaluate both model and model calibration error for multiple machine learning methods, and investigate the influence of ensemble averaging and post hoc model calibration via temperature scaling. Overall, we find that ensemble averaging of deep neural networks is both an effective and consistent tool for improving model calibration, while temperature scaling has comparatively limited benefits. Looking forward, we anticipate that this investigation will lay the foundation for future work in developing mechanics specific approaches to deep learning model calibration. Calibration ; Machine learning ; Artificial intelligence ; Computer science ; Deep learning ; Artificial neural network ; Scaling ; Uncertainty quantification ; Mathematics ; Statistics Machine Learning in Materials Science ; Software Engineering Research ; Machine Learning and Data Classification
Authors: Qi‐Jun Hong
Source: arXiv (Cornell University) (preprint, 2022-04-10)
Field: Materials Science — Materials Chemistry

Melting temperature prediction via first principles and deep learning Melting is a high temperature process that requires extensive sampling of configuration space, thus making melting temperature prediction computationally very expensive and challenging. Over the past few years, I have built two methods to address this challenge, one via direct density functional theory (DFT) molecular dynamics (MD) simulations and the other via deep learning graph neural networks. The DFT approach is based on statistical analysis of small-size solid-liquid coexistence MD simulations. It eliminates the risk of metastable superheated solid in the fast-heating method, while also significantly reducing the computer cost relative to the traditional large-scale coexistence method. Being both accurate and efficient (at the speed of several days per material), it is considered as one of the best methods for direct DFT melting temperature calculation. The deep learning method is based on graph neural networks that effectively handles permutation invariance in chemical formula, which drastically improves efficiency and reduces cost. At the speed of milliseconds per material, the model is extremely fast, while being moderately accurate, especially within the composition space expanded by the dataset. I have implemented both methods into automated computer code packages, making them publicly available and free to download. The DFT and deep learning methods are highly complementary to each other, and hence they can be potentially well integrated into a framework for melting temperature prediction. I demonstrated examples of applying the methods to materials design and discovery of high-melting-point materials. Computer science ; Chemical space ; Metastability ; Deep learning ; Algorithm ; Artificial neural network ; Computational science ; Artificial intelligence ; Machine learning ; Theoretical computer science ; Chemistry Machine Learning in Materials Science ; Advanced Graph Neural Networks
Authors: Yoonho Jeong | Jihoo Kim | Yeji Kim | Insung S. Choi
Source: Bulletin of the Korean Chemical Society (article, 2022-04-26)
Field: Materials Science — Materials Chemistry

Development of a chemically intuitive filter for chemical graph convolutional network Abstract This work proposes the use of (: identity matrix; : adjacency matrix), instead of , the normalized form of which has intensively been used for the construction of graph convolutional networks (GCNs), in deep‐learning chemistry. The performance of the GCN model with in its convolution step is at least on a par with the vanilla GCN that uses (: degree matrix of ) in various chemistry datasets, such as FreeSolv, ESOL, lipophilicity, and blood–brain barrier penetration datasets. It could be seen that the use of might be more chemically intuitive than the use of , potentially embracing the information on bond properties, such as dipole moment, and functional groups in a molecule. This work suggests unavoidable necessity of tackling molecular‐representation problems in deep‐learning chemistry from unprecedented angles of view for advanced development and construction of chemically intuitive deep‐learning models. Adjacency matrix ; Computer science ; Deep learning ; Graph ; Lipophilicity ; Artificial intelligence ; Representation (politics) ; Theoretical computer science ; Chemistry ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Alberto Ferrari | V. A. Sotskov | Alexander V. Shapeev | Fritz Körmann
Source: arXiv (Cornell University) (preprint, 2022-12-08)
Field: Materials Science — Materials Chemistry

Modelling Surface Segregation in Compositionally Complex Alloys with Ab-Initio Accuracy Compositionally complex alloys or concentrated solid solutions are the latest frontier in catalyst design, but mixing different elements in one catalyst may result in surface segregation. Atomistic simulations can predict segregation patterns, but standard approaches based on mean-field models, cluster expansion, or classical interatomic potentials are often limited for the description of multicomponent alloys. We present machine learning potentials that can describe surface segregation with near DFT accuracy. The method is used to study a complex Co-Cu-Fe-Mo-Ni quinary alloy. For this alloy, an unexpected segregation of Co, which has a relatively high surface energy, is observed. We rationalize this surprising mechanism in terms of simple transition-metal chemistry. Quinary ; Materials science ; Cluster (spacecraft) ; Alloy ; Ab initio ; Interatomic potential ; Surface (topology) ; Mixing (physics) ; Ab initio quantum chemistry methods ; Cluster expansion ; Chemical physics ; Thermodynamics ; Metallurgy ; Computational chemistry ; Molecular dynamics ; Chemistry ; Computer science ; Physics ; Mathematics Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; nanoparticles nucleation surface interactions
Authors: Diwei Wang | Zhenxing Shen | Qian Zhang | Yali Lei | Tian Zhang | Shasha Huang | Jian Sun | Hongmei Xu | Junji Cao
Source:  (preprint, 2022-09-06)
Field: Chemistry — Spectroscopy

Supplementary material to "Winter brown carbon over six China’s megacities: Light absorption, molecular characterization, and improved source apportionment revealed by multilayer perceptron neural network" Apportionment ; Characterization (materials science) ; Megacity ; Multilayer perceptron ; China ; Artificial neural network ; Carbon fibers ; Materials science ; Absorption (acoustics) ; Environmental science ; Geography ; Artificial intelligence ; Computer science ; Nanotechnology ; Political science ; Composite material ; Economics ; Economy ; Archaeology Molecular spectroscopy and chirality ; Geochemistry and Geologic Mapping ; Advanced Chemical Sensor Technologies
Authors: Brenden G. Pelkie | Stephanie Valleau
Source: arXiv (Cornell University) (preprint, 2022-06-03)
Field: Materials Science — Materials Chemistry

Machine learning the quantum flux-flux correlation function for catalytic surface reactions A dataset of fully quantum flux-flux correlation functions and reaction rate constants was constructed for organic heterogeneous catalytic surface reactions. Gaussian process regressors were successfully fitted to training data to predict previously unseen test set reaction rate constant products and Cauchy fits of the flux-flux correlation function. The optimal regressor prediction mean absolute percent errors were on the order of 0.5% for test set reaction rate constant products and 1.0% for test set flux-flux correlation functions. The Gaussian process regressors were accurate both when looking at kinetics at new temperatures and reactivity of previously unseen reactions and provide a significant speedup respect to the computationally demanding time propagation of the flux-flux correlation function. Flux (metallurgy) ; Reaction rate constant ; Correlation function (quantum field theory) ; Gaussian process ; Constant (computer programming) ; Function (biology) ; Gaussian function ; Gaussian ; Reaction rate ; Mathematics ; Physics ; Kinetics ; Chemistry ; Catalysis ; Statistics ; Computer science ; Computational chemistry ; Quantum mechanics Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Advanced Chemical Physics Studies
Authors: Şener Özönder | H. Kübra Küçükkartal
Source: arXiv (Cornell University) (preprint, 2022-08-16)
Field: Materials Science — Materials Chemistry

Rapid Discovery of Graphene Nanocrystals Using DFT and Bayesian Optimization with Neural Network Kernel Density functional theory (DFT) is a powerful computational method used to obtain physical and chemical properties of materials. In the materials discovery framework, it is often necessary to virtually screen a large and high-dimensional chemical space to find materials with desired properties. However, grid searching a large chemical space with DFT is inefficient due to its high computational cost. We propose an approach utilizing Bayesian optimization (BO) with an artificial neural network kernel to enable smart search. This method leverages the BO algorithm, where the neural network, trained on a limited number of DFT results, determines the most promising regions of the chemical space to explore in subsequent iterations. This approach aims to discover materials with target properties while minimizing the number of DFT calculations required. To demonstrate the effectiveness of this method, we investigated 63 doped graphene quantum dots (GQDs) with sizes ranging from 1 to 2 nm to find the structure with the highest light absorbance. Using time-dependent DFT (TDDFT) only 12 times, we achieved a significant reduction in computational cost, approximately 20% of what would be required for a full grid search, by employing the BO algorithm with a neural network kernel. Considering that TDDFT calculations for a single GQD require about half a day of wall time on high-performance computing nodes, this reduction is substantial. Our approach can be generalized to the discovery of new drugs, chemicals, crystals, and alloys with high-dimensional and large chemical spaces, offering a scalable solution for various applications in materials science. Bayesian optimization ; Chemical space ; Computer science ; Artificial neural network ; Space (punctuation) ; Gaussian process ; Artificial intelligence ; Graphene ; Gaussian ; Nanotechnology ; Machine learning ; Materials science ; Chemistry ; Computational chemistry ; Drug discovery Machine Learning in Materials Science
Authors: A. N. Filanovich | Povzner A .A. | А. Н. Филанович | А. А. Повзнер
Source: Electronic scientific archive of UrFU (Ural Federal University) (article, 2022-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Prediction of Thermodynamic and Electronic Properties of Double Half Heusler Alloys Based on Machine Learning Methods Based on data from the open Aflow database and other sources, a regression model is constructed that is used to predict properties of new class of double half Heusler alloys, such as lattice thermal conductivity, Grüneisen parameter, Debye temperature, band gap, magnetic moment per atom, etc. Moment (physics) ; Magnetic moment ; Lattice (music) ; Thermal ; Regression analysis ; Debye model ; Support vector machine Heusler alloys: electronic and magnetic properties ; Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices