jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Lucía Morán‐González | Jørn Eirik Betten | Hannes Kneiding | David Balcells
Source: ChemRxiv (preprint, 2023-09-05)
Field: Materials Science — Materials Chemistry

AABBA: Atom–Atom Bond–Bond Bond–Atom Graph Kernel for Machine Learning on Molecules and Materials Graphs are one of the most natural and powerful representations available for molecules; natural because they have an intuitive correspondence to skeletal formulas, the language used by chemists worldwide, and powerful, because they are highly expressive both globally (molecular topology) and locally (atomic properties). Graph kernels are used to transform molecular graphs into fixed-length vectors, which can be used as fingerprints in machine learning (ML) models. To date, kernels have mostly focused on the atomic nodes of the graph. In this work, we developed an extended graph kernel computing atom–atom, bond–bond, and bond–atom (AABBA) autocorrelations. The resulting AABBA representations were evaluated with a transition metal complex benchmark, motivated by the higher complexity of these compounds relative to organic molecules. In particular, we tested different flavors of the AABBA kernel in the prediction of the energy barriers and bond distances of the Vaska’s complex dataset (Friederich et al., Chem. Sci., 2020, 11, 4584). For a variety of ML models, including neural networks, gradient boosting machines, and Gaussian processes, we showed that AABBA outperforms the baseline including only atom–atom autocorrelations. Dimensionality reduction studies also showed that the bond–bond and bond–atom autocorrelations yield many of the most relevant features. We believe that the AABBA graph kernel can accelerate the discovery of chemical compounds and inspire novel molecular representations in which both atomic and bond properties play an important role. Molecular graph ; Atom (system on chip) ; Molecule ; Bond graph ; Chemistry ; Bond energy ; Computer science ; Algorithm ; Machine learning ; Graph ; Computational chemistry ; Topology (electrical circuits) ; Mathematics ; Theoretical computer science ; Combinatorics ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Daniel Schwalbe‐Koda | Daniel E. Widdowson | Tuan Anh Pham | Vitaliy Kurlin
Source: arXiv (Cornell University) (preprint, 2023-07-20)
Field: Chemistry — Inorganic Chemistry

Inorganic synthesis-structure maps in zeolites with machine learning and crystallographic distances Zeolites are inorganic materials known for their diversity of applications, synthesis conditions, and resulting polymorphs. Although their synthesis is controlled both by inorganic and organic synthesis conditions, computational studies of zeolite synthesis have focused mostly on organic template design. In this work, we use a strong distance metric between crystal structures and machine learning (ML) to create inorganic synthesis maps in zeolites. Starting with 253 known zeolites, we show how the continuous distances between frameworks reproduce inorganic synthesis conditions from the literature without using labels such as building units. An unsupervised learning analysis shows that neighboring zeolites according to our metric often share similar inorganic synthesis conditions, even in template-based routes. In combination with ML classifiers, we find synthesis-structure relationships for 14 common inorganic conditions in zeolites, namely Al, B, Be, Ca, Co, F, Ga, Ge, K, Mg, Na, P, Si, and Zn. By explaining the model predictions, we demonstrate how (dis)similarities towards known structures can be used as features for the synthesis space. Finally, we show how these methods can be used to predict inorganic synthesis conditions for unrealized frameworks in hypothetical databases and interpret the outcomes by extracting local structural patterns from zeolites. In combination with template design, this work can accelerate the exploration of the space of synthesis conditions for zeolites. Zeolite ; Hydrothermal synthesis ; Crystal structure ; Metric (unit) ; Space (punctuation) ; Materials science ; Computer science ; Algorithm ; Crystallography ; Chemistry ; Chemical engineering ; Catalysis ; Organic chemistry ; Engineering Zeolite Catalysis and Synthesis ; Polyoxometalates: Synthesis and Applications ; Machine Learning in Materials Science
Authors: A. Thorn | Daviti Gochitashvili | Saba Kharabadze | Aleksey N. Kolmogorov
Source: arXiv (Cornell University) (preprint, 2023-06-17)
Field: Materials Science — Materials Chemistry

Machine learning search for stable binary Sn alloys with Na, Ca, Cu, Pd, and Ag We present our findings of a large-scale screening for new synthesizable materials in five M-Sn binaries, M = Na, Ca, Cu, Pd, and Ag. The focus on these systems was motivated by the known richness of M-Sn properties with potential applications in energy storage, electronics packaging, and superconductivity. For the systematic exploration of the large configuration space, we relied on our recently developed MAISE-NET framework that constructs accurate neural network interatomic potentials and utilizes them to accelerate ab initio global structure searches. The scan of over two million candidate phases at a fraction of the typical ab initio calculation cost has uncovered 29 possible intermetallics thermodynamically stable at different temperatures and pressures (1 bar and 20 GPa). Notable predictions of ambient-pressure materials include a simple hP6-NaSn$_2$ phase, fcc-based Pd-rich alloys, tI36-PdSn$_2$ with a new prototype, and several high-temperature Sn-rich ground states in the Na-Sn, Cu-Sn, and Ag-Sn systems. Our modeling work also involved ab initio (re)examination of previously observed M-Sn compounds that helped explain the entropy-driven stabilization of known Cu-Sn phases. The study demonstrates the benefits of guiding structure searches with machine learning potentials and significantly expands the number of predicted thermodynamically stable crystalline intermetallics achieved with this strategy so far. Intermetallic ; Ab initio ; Materials science ; Ab initio quantum chemistry methods ; Alloy ; Thermodynamics ; Chemistry ; Metallurgy ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Arsalan Hashemi | Reza Khakpour | Amir Mahdian | Michael Busch | Pekka Peljo | Kari Laasonen
Source: ChemRxiv (preprint, 2023-08-22)
Field: Materials Science — Materials Chemistry

Density Functional Theory and Machine Learning for Electrochemical Square-Scheme Prediction: An Application to Quinone-type Molecules Relevant to Redox Flow Batteries Proton-electron transfer (PET) reactions are rather common in chemistry and crucial in energy storage applications. How electrons and protons are involved or which mechanism dominates is strongly molecule and pH dependent. It is the nature of the participants in the reaction that dictates how electrons and protons are involved and which mechanism dominates. Quantum chemical methods can be used to assess redox potential and acidity constant values but the computations are rather time consuming. In this work, supervised machine learning (ML) models are used to predict PET reactions and analyze molecular space. The data for ML have been created by density functional theory (DFT) calculations. Random Forest Regression models are trained and tested on a dataset that we created. The dataset contains more than 8200 organic molecules that each underwent a two-proton two-electron transfer process. Both structural and chemical descriptors are used. The HOMO of the reactant and LUMO of the product participating in the oxidation reaction appeared to be inversely associated with \oxE. Trained models using a SMILES-based descriptor can efficiently predict the pKa and redox potential with a mean absolute error of less than 1 and 66 mV, respectively. High prediction accuracy of $R^2 > 0.76$ and $> 0.90$ was also obtained on the external test set for redox potential and pKa, respectively. This hybrid DFT-ML study can be applied to speed up the screening of quinone-type molecules for energy storage and other applications. Redox ; Quinone ; Density functional theory ; Chemistry ; HOMO/LUMO ; Electron transfer ; Molecule ; Proton ; Computational chemistry ; Biological system ; Chemical physics ; Physical chemistry ; Physics ; Quantum mechanics ; Organic chemistry Machine Learning in Materials Science ; Conducting polymers and applications ; Electrochemical Analysis and Applications
Authors: Arsalan Hashemi | Reza Khakpour | Amir Mahdian | Michael Busch | Pekka Peljo | Kari Laasonen
Source: ChemRxiv (preprint, 2023-05-22)
Field: Materials Science — Materials Chemistry

Density Functional Theory and Machine Learning for Electrochemical Square-Scheme Prediction: An Application to Quinone-type Molecules Relevant to Redox Flow Batteries Proton-electron transfer (PET) reactions are rather common in chemistry and crucial in energy storage applications. How electrons and protons are involved or which mechanism dominates is strongly molecule and pH dependent. It is the nature of the participants in the reaction that dictates how electrons and protons are involved and which mechanism dominates. Quantum chemical methods can be used to assess redox potential and acidity constant values but the computations are rather time consuming. In this work, supervised machine learning (ML) models are used to predict PET reactions and analyze molecular space. The data for ML have been created by density functional theory (DFT) calculations. Random Forest Regression models are trained and tested on a dataset that we created. The dataset contains more than 8200 organic molecules that each underwent a two-proton two-electron transfer process. Both structural and chemical descriptors are used. The HOMO of the reactant and LUMO of the product participating in the oxidation reaction appeared to be inversely associated with \oxE. Trained models using a SMILES-based descriptor can efficiently predict the pKa and redox potential with a mean absolute error of less than 1 and 66 mV, respectively. High prediction accuracy of $R^2 > 0.76$ and $> 0.90$ was also obtained on the external test set for redox potential and pKa, respectively. This hybrid DFT-ML study can be applied to speed up the screening of quinone-type molecules for energy storage and other applications. Redox ; Density functional theory ; Quinone ; HOMO/LUMO ; Chemistry ; Electron transfer ; Molecule ; Proton ; Computational chemistry ; Chemical physics ; Biological system ; Physical chemistry ; Physics ; Quantum mechanics ; Organic chemistry Machine Learning in Materials Science ; Conducting polymers and applications ; Electrochemical Analysis and Applications
Authors: Jiheng Fang | Shangrong yang | Fang Jiheng | Jieqiong Hu | hongsheng sun | Yongtai Chen | Youcai Yang | dekui ning | guohua liu | Xingqun He | Qinglin Jin
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Materials Science — Materials Chemistry

Solid-Liquid Phase Temperature Prediction of Alloys Based on Machine Learning Key Feature Screening Key (lock) ; Feature (linguistics) ; Phase (matter) ; Materials science ; Artificial intelligence ; Computer science ; Machine learning ; Pattern recognition (psychology) ; Chemistry Machine Learning in Materials Science
Authors: Rajath Alexander | Amit Kaushal | Jyoti Prakash | P.T. Rao | Kinshuk Dasgupta
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Materials Science — Materials Chemistry

Identification and Classification of Disordered Carbon Materials in a Composite Matrix Through Machine Learning Approach Integrated with Raman Mapping Raman spectroscopy ; Composite number ; Identification (biology) ; Carbon fibers ; Matrix (chemical analysis) ; Artificial intelligence ; Materials science ; Computer science ; Composite material ; Pattern recognition (psychology) ; Physics ; Optics Graphene research and applications ; Fiber-reinforced polymer composites ; Recycling and Waste Management Techniques
Authors: Masaya Hagai | Mahito Sugiyama | Koji Tsuda | Takeshi Yanai
Source: ChemRxiv (preprint, 2023-01-11)
Field: Materials Science — Materials Chemistry

Artificial Neural Network Encoding of Molecular Wavefunction for Quantum Computing Artificial neural networks (ANNs) for material modeling have received significant interest. We recently reported an adaptation of ANNs based on Boltzmann machine (BM) architectures to an ansatz of the multiconfigurational many-electron wavefunction, designated neural-network quantum state (NQS), for quantum chemistry calculations [Yang et al., J. Chem. Theory Comput., 2020, 16, 3513--3529]. Here, this study presents its extended formalism to a quantum algorithm that enables the preparation of the NQS through quantum gates. The descriptors of the ANN model, which are chosen as the occupancies of electronic configurations, are quantum-mechanically represented by qubits. Our algorithm may thus bring potential advantages over classical sampling-based computation employed in the previous studies. The NQS can be accurately formed using quantum-native procedures. Still, the training of the model in terms of energy minimization is efficiently performed on a classical computer; thus, our approach is a class of variational quantum eigensolver. The BM models are related to the Gibbs distribution, and our preparation procedures exploit techniques of quantum phase estimation but with no Hamiltonian evolution. The proposed algorithm is assessed by implementing it on a quantum computer simulator. Illustrative molecular calculations at the complete-active-space configuration interaction level of theory are displayed, confirming consistency with the accuracy of our previous classical approaches. Quantum computer ; Wave function ; Ansatz ; Quantum ; Boltzmann machine ; Quantum algorithm ; Qubit ; Artificial neural network ; Hamiltonian (control theory) ; Statistical physics ; Computer science ; Quantum phase estimation algorithm ; Restricted Boltzmann machine ; Algorithm ; Quantum mechanics ; Quantum simulator ; Physics ; Mathematics ; Artificial intelligence ; Mathematical optimization Machine Learning in Materials Science ; Quantum Computing Algorithms and Architecture ; Quantum many-body systems
Authors: Ziyuan Rao | Yue Li | Hongbin Zhang | Timoteo Colnaghi | Andreas Marek | Markus Rampp | Baptiste Gault
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Materials Science — Materials Chemistry

Direct Recognition of Crystal Structures Via Three-Dimensional Convolutional Neural Networks with High Accuracy and Tolerance to Noise Convolutional neural network ; Noise (video) ; Computer science ; Pattern recognition (psychology) ; Speech recognition ; Artificial intelligence ; Image (mathematics) X-ray Diffraction in Crystallography ; Mineral Processing and Grinding ; Non-Destructive Testing Techniques
Authors: Frank Cerasoli | Davide Donadio
Source: Research Square (preprint, 2023-12-31)
Field: Materials Science — Materials Chemistry

Effective optimization of atomic decoration in giant and superstructurally ordered crystals with machine learning Materials science ; Computer science ; Artificial intelligence ; Crystallography ; Chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Crystallization and Solubility Studies
Authors: Haobo Li
Source: SSRN Electronic Journal (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Machine-learning Assisted Understanding of Tri-metallic Active Site for Oxygen Evolution Reaction Oxygen evolution ; Active learning (machine learning) ; Oxygen ; Active site ; Active oxygen ; Chemistry ; Computer science ; Artificial intelligence ; Catalysis ; Organic chemistry ; Physical chemistry Machine Learning in Materials Science ; Fault Detection and Control Systems ; Water Quality Monitoring and Analysis
Authors: Hexiong Liu | Yongfeng Cai | Yunfei Yang | Jinshu Wang
Source:  (article, 2023-11-10)
Field: Materials Science — Materials Chemistry

Machine Learning Assisted Density Prediction of Cathodes Alloy Matrix The porous structure of the dispenser cathodes matrix provides a fast channel for the supplement of active substances, which greatly improves the emission performance of the cathode. However, controlling the porosity of the matrix is still challenging. In this work, the dataset consists of the density of two hundred binary tungsten alloy matrices. The Pearson correlation coefficient and recursive elimination method were used as feature selection methods to select potential influencing factors of matrix density. Comparing different machine learning models, it was found that MLP neural networks have accurate predictions of density. By training the learning model, the prediction accuracy (R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>) is as high as 0.918. In addition, the predicted density of the porous matrix is in good agreement with the experimental values. Alloy ; Cathode ; Matrix (chemical analysis) ; Materials science ; Computer science ; Metallurgy ; Composite material ; Electrical engineering ; Engineering Machine Learning in Materials Science
Authors: Rosanna Rizzi | Nicola Corriero | Nicoletta Del Buono | Gaetano Settembre | Domenico Diacono
Source: Acta Crystallographica Section A Foundations and Advances (article, 2023-08-22)
Field: Materials Science — Materials Chemistry

<i>CrystalMELA</i>: a machine learning-based web platform for crystal system classification A powder diffraction pattern is mainly affected by peak overlaps, difficulty in the correct background estimation, presence of preferred orientation effects, and limited experimental resolution.All this makes the structure solution process non-trivial.Most importantly, it is difficult to establish the critical initial steps such as pattern indexation and space group determination, especially if more than one chemical phase is present in the compound.On the other hand, an incorrectly defined unit cell does not lead to the structural solution.It happens despite the progress, availability, and variety (in terms of strategies and methods implemented) of automatic indexing software such as DICVOL [1], N-TREOR09 [2] and ITO [3].In the past few years, extraordinary advances in data-driven models and the availability of large amounts of experimental data from many different sources have enabled the development and application of Artificial Intelligence in materials science [4], especially machine learning (ML) algorithms for diffraction data analysis.A new machine learning (ML) based web platform, named CrystalMELA (Crystallographic MachinE LeArning) [5], for crystal system classification has been developed.The aim is to try to overcome the difficulties posed by the structure solution process from powder diffraction data, and to complement traditional indexing approaches.The tool is currently designed for the classification of the seven crystal classes.In the current version, the platform can run three different and complementary ML models: a Convolutional Neural Network (CNN), a Random Forest (RF) and an Extremely randomized trees (ExRT).The models have been trained on theoretical powder diffraction patterns of more than 280,000 crystal structures of inorganic, organic, organo-metallic compounds and minerals as collected in the POW_COD database [6].A 70% of classification accuracy was achieved, improved to 90% if the top-2 accuracy is considered.CrystalMELA is free available for the scientific community and its home web page is shown in Fig. 1.All the classification options in CrystalMELA platform are designed to be powerful and easy to use, supported by a user-friendly graphic interface.Their main aspects and some examples of applications to real cases will be presented.Figure 1.The Home web page of CrystalMELA platform Computer science ; World Wide Web ; Artificial intelligence ; Machine learning Crystallization and Solubility Studies ; X-ray Diffraction in Crystallography ; Mineral Processing and Grinding
Authors: jingfqian huang | Tang Chengying
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Chemistry — Inorganic Chemistry

Machine Learning-Assisted Design and Synthesis with Excellent Performance of Fe-Based Amorphous/Nanocrystalline Soft Magnetic Materials Nanocrystalline material ; Materials science ; Amorphous solid ; Nanotechnology ; Metallurgy ; Crystallography ; Chemistry Pigment Synthesis and Properties ; Magnetic Properties and Synthesis of Ferrites ; Nanomaterials for catalytic reactions
Authors: Dongyu Bai | Yihan Nie | Jing Shang | Minghao Liu | Yang Yang | Haifei Zhan | Liangzhi Kou | Yuantong Gu
Source: arXiv (Cornell University) (preprint, 2023-08-22)
Field: Materials Science — Materials Chemistry

Ferroelectric Domain and Switching Dynamics in Curved In2Se3: First Principle and Deep Learning Molecular Dynamics Simulations Complex strain status can exist in 2D materials during their synthesis process, resulting in significant impacts on the physical and chemical properties. Despite their prevalence in experiments, their influence on the material properties and the corresponding mechanism are often understudied due to the lack of effective simulation methods. In this work, we investigated the effects of bending, rippling, and bubbling on the ferroelectric domains in In2Se3 monolayer by density functional theory (DFT) and deep learning molecular dynamics (DLMD) simulations. The analysis of the tube model shows that bending deformation imparts asymmetry into the system, and the polarization direction tends to orient towards the tensile side, which has a lower energy state than the opposite polarization direction. The energy barrier for polarization switching can be reduced by compressive strain according DFT results. The dynamics of the polarization switching is investigated by the DLMD simulations. The influence of curvature and temperature on the switching time follows the Arrhenius-style function. For the complex strain status in the rippling and bubbling model, the lifetime of the local transient polarization is analyzed by the autocorrelation function, and the size of the stable polarization domain is identified. Local curvature and temperature can influence the local polarization dynamics following the proposed Arrhenius-style equation. Through cross-scale simulations, this study demonstrates the capability of deep-learning potentials in simulating polarization for ferroelectric materials. It further reveals the potential to manipulate local polarization in ferroelectric materials through strain engineering. Ferroelectricity ; Polarization (electrochemistry) ; Materials science ; Molecular dynamics ; Curvature ; Condensed matter physics ; Chemical physics ; Physics ; Chemistry ; Optoelectronics ; Computational chemistry ; Dielectric ; Geometry Machine Learning in Materials Science ; Perovskite Materials and Applications ; 2D Materials and Applications