jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Xin‐Hao Li | Wing-chi Ashley Lam | In Won Yeu | Abhiroop Mishra | Joaquín Rodríguez López | Alexander Urban
Source: ECS Meeting Abstracts (article, 2023-08-28)
Field: Materials Science — Materials Chemistry

Predicting Oxygen Release and Surface Reconstructions in Li-Ion Battery Cathodes Via Automated Construction of Computational Surface Phase Diagrams and Machine Learning Although LiNiO 2 (LNO) is chemically very similar to LiCoO 2 (LCO), LNO and related Co-free Ni-rich cathodes suffer from continuing surface degradation via oxygen gas release during electrochemical cycling that leads to the formation of surface phases with high impedance. While the surface degradation of LNO and related cathode compositions have been characterized experimentally on a phenomenological level, an understanding of the surface reconstructions that form on the atomic scale and the intrinsic surface instability of LNO compared with other related cathode compositions is still lacking. To shed light on surface reactivity, we developed a thermodynamic methodology for the prediction of voltage and temperature-dependent surface electrode reconstructions [1] and a computational framework to automate the time-consuming enumeration of surface reconstructions, the construction of symmetric surface-slab models, and the analysis of surface phase diagrams [2]. By applying first-principles atomistic modeling, we determined the self-reduction mechanism of LNO and compared the stable surface reconstructions with those of LCO. To further assess the surface stability of more complicated NMC/NCA cathodes with the help of our own generated LNO and LCO databases, we developed a supervised machine learning (ML) model to train on geometrical and electronic fingerprints from surface and bulk models, respectively. Our results provide insight into the initial stages of surface degradation in Ni-rich cathodes and lay the foundation for the computational design of stable cathode materials against oxygen release. Li, X.; Wang, Q.; Guo, H.; Artrith, N.; Urban, A. ACS Appl. Energy Mater. 2022, 5 (5), 5730–5741. Li, X.; Qu, J.; Yeu, I.; Li, Z.; Rodríguez-López, J.; Urban, A., in preparation , 2023 Cathode ; Surface (topology) ; Materials science ; Electrochemistry ; Battery (electricity) ; Surface reconstruction ; Electrode ; Chemistry ; Thermodynamics ; Physical chemistry ; Physics ; Geometry Machine Learning in Materials Science ; Advancements in Battery Materials ; Advanced Battery Technologies Research
Authors: Chen Zhan-Bin | Hai Sun
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Chemistry — Spectroscopy

Neural Network Cross Sections for Electron Collision Ionization of Complex Molecules Ionization ; Collision ; Electron ; Physics ; Artificial neural network ; Atomic physics ; Computer science ; Nuclear physics ; Artificial intelligence ; Computer security ; Quantum mechanics ; Ion Mass Spectrometry Techniques and Applications ; Pesticide Residue Analysis and Safety ; Water Quality Monitoring and Analysis
Authors: Dr. Elena Marquez
Source: Austra & Lian Journal of Basic Sciences (article, 2023-08-31)
Field: Materials Science — Materials Chemistry

Innovations in Computational Chemistry for Material Science Computational chemistry has revolutionized the landscape of material science by enabling accurate modeling, prediction, and design of advanced materials. Recent innovations in quantum mechanical methods, machine learning algorithms, and multiscale modeling approaches have significantly improved our understanding of structure-property relationships in materials. This article explores state-of-the-art developments in computational chemistry with a focus on their applications to catalysis, polymers, energy storage, and electronic materials. We discuss the integration of artificial intelligence in material discovery, the rise of high-throughput simulations, and quantum chemistry’s pivotal role in predicting material behavior at the atomic level. The paper concludes with future directions for computational material design driven by automation and data-centric paradigms. Computational model ; Focus (optics) ; Automation ; Multiscale modeling ; Energy (signal processing) ; Materials informatics Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Catalysis and Oxidation Reactions
Authors: Ata Madanchi | Michael Kilgour | Frederik Zysk | Thomas D. Kühne | Lena Simine
Source: arXiv (Cornell University) (preprint, 2023-01-11)
Field: Materials Science — Materials Chemistry

Simulations of Disordered Matter in 3D with the Morphological Autoregressive Protocol (MAP) and Convolutional Neural Networks Disordered molecular systems such as amorphous catalysts, organic thin films, electrolyte solutions, and water are at the cutting edge of computational exploration today. Traditional simulations of such systems at length-scales relevant to experiments in practice require a compromise between model accuracy and quality of sampling. To remedy the situation, we have developed an approach based on generative machine learning called the Morphological Autoregressive Protocol (MAP) which provides computational access to mesoscale disordered molecular configurations at linear cost at generation for materials in which structural correlations decay sufficiently rapidly. The algorithm is implemented using an augmented PixelCNN deep learning architecture that we previously demonstrated produces excellent results in 2 dimensions (2D) for mono-elemental molecular systems. Here, we extend our implementation to multielemental 3D and demonstrate performance using water as our test system in two scenarios: 1. liquid water, and 2. a sample conditioned on the presence of a rare motif. We trained the model on small-scale samples of liquid water produced using path-integral molecular dynamics simulation including nuclear quantum effects under ambient conditions. MAP-generated water configurations are shown to accurately reproduce the properties of the training set and to produce stable trajectories when used as initial conditions in classical and quantum dynamical simulations. We expect our approach to perform equally well on other disordered molecular systems while offering unique advantages in situations when the disorder is quenched rather than equilibrated. Convolutional neural network ; Computer science ; Molecular dynamics ; Quantum ; Water model ; Artificial intelligence ; Autoregressive model ; Deep learning ; Amorphous solid ; Statistical physics ; Materials science ; Chemistry ; Physics ; Mathematics ; Computational chemistry Machine Learning in Materials Science ; Quantum, superfluid, helium dynamics ; Computational Physics and Python Applications
Authors: David Buterez | Jon Paul Janet | Steven J. Kiddle | Dino Oglić | Píetro Lió
Source: ChemRxiv (preprint, 2023-06-08)
Field: Materials Science — Materials Chemistry

Modelling local and general quantum mechanical properties with attention-based pooling Atom-centred neural networks represent the state-of-the-art for approximating the quantum chemical properties of molecules, such as internal energies. While the design of machine learning architectures that respect chemical principles has continued to advance, the final atom pooling operation that is necessary to convert from atomic to molecular representations in most models remains relatively undeveloped. The most common choices, sum and average pooling, compute molecular representations that are naturally a good fit for many physical properties, while satisfying properties such as permutation invariance which are desirable from a geometric deep learning perspective. However, there are growing concerns that such simplistic functions might have limited representational power, while also being suboptimal for physical properties that are highly localised or intensive. Based on recent advances in graph representation learning, we investigate the use of a learnable pooling function that leverages an attention mechanism to model interactions between atom representations. The proposed pooling operation is a drop-in replacement requiring no changes to any of the other architectural components. Using SchNet and DimeNet++ as starting models, we demonstrate consistent uplifts in performance compared to sum pooling and a recent physics-aware pooling operation designed specifically for orbital energies, on several datasets, properties, and levels of theory, with up to 85% improvements depending on the specific task. Pooling ; Computer science ; Representation (politics) ; Function (biology) ; Artificial intelligence ; Theoretical computer science ; Quantum ; Atom (system on chip) ; Machine learning ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Sergio Bacallado | José Miguel Hernández-Lobato | Sukriti Singh | Austin Tripp
Source:  (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Tanimoto Random Features for Scalable Molecular Machine Learning Scalability ; Feature (linguistics) ; Pattern recognition (psychology) ; Random forest ; Support vector machine Machine Learning in Materials Science ; Machine Learning in Bioinformatics ; Computational Drug Discovery Methods
Authors: Ember Salas | Andrew Rohskopf | James Goff | Megan McCarthy | Mary Alice Cusentino | Mitchell Wood | Aidan P. Thompson
Source:  (article, 2023-07-01)
Field: Materials Science — Materials Chemistry

Recent Developments in Machine Learning Interatomic Potentials for Molecular Dynamics Computer science ; Molecular dynamics ; Interatomic potential ; Dynamics (music) ; Statistical physics ; Artificial intelligence ; Physics ; Computational chemistry ; Chemistry Machine Learning in Materials Science ; Various Chemistry Research Topics
Authors: Ishan Kumar | Prateek K. Jha
Source: arXiv (Cornell University) (preprint, 2023-11-20)
Field: Materials Science — Materials Chemistry

Coarse-Grained Configurational Polymer Fingerprints for Property Prediction using Machine Learning In this work, we present a method to generate a configurational level fingerprint for polymers using the Bead-Spring-Model. Unlike some of the previous fingerprinting approaches that employ monomer-level information where atomistic descriptors are computed using quantum chemistry calculations, this approach incorporates configurational information from a coarse-grained model of a long polymer chain. The proposed approach may be advantageous for the study of behavior resulting from large molecular weights. To create this fingerprint, we make use of two kinds of descriptors. First, we calculate certain geometric descriptors like Re2, Rg2 etc. and label them as Calculated Descriptors. Second, we generate a set of data-driven descriptors using an unsupervised autoencoder model and call them Learnt Descriptors. Using a combination of both of them, we are able to learn mappings from the structure to various properties of the polymer chain by training ML models. We test our fingerprint to predict the probability of occurrence of a configuration at equilibrium, which is approximated by a simple linear relationship between the instantaneous internal energy and equilibrium average internal energy. Fingerprint (computing) ; Computer science ; Autoencoder ; Artificial intelligence ; Chain (unit) ; Set (abstract data type) ; Algorithm ; Biological system ; Pattern recognition (psychology) ; Statistical physics ; Deep learning ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Anthony Onwuli | Ashish V. Hegde | Kevin P. Nguyen | Keith T. Butler | Aron Walsh
Source: arXiv (Cornell University) (preprint, 2023-07-03)
Field: Materials Science — Materials Chemistry

Element similarity in high-dimensional materials representations The traditional display of elements in the periodic table is convenient for the study of chemistry and physics. However, the atomic number alone is insufficient for training statistical machine learning models to describe and extract composition-structure-property relationships. Here, we assess the similarity and correlations contained within high-dimensional local and distributed representations of the chemical elements, as implemented in an open-source Python package ElementEmbeddings. These include element vectors of up to 200 dimensions derived from known physical properties, crystal structure analysis, natural language processing, and deep learning models. A range of distance measures are compared and a clustering of elements into familiar groups is found using dimensionality reduction techniques. The cosine similarity is used to assess the utility of these metrics for crystal structure prediction, showing that they can outperform the traditional radius ratio rules for the structural classification of AB binary solids. Python (programming language) ; Cluster analysis ; Similarity (geometry) ; Dimensionality reduction ; Curse of dimensionality ; Computer science ; Periodic table ; Crystal structure prediction ; Artificial intelligence ; Crystal structure ; Data mining ; Pattern recognition (psychology) ; Theoretical computer science ; Physics ; Chemistry ; Crystallography Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; History and advancements in chemistry
Authors: Alex Lee | Joshua A. Rackers | Shivesh Pathak | William P. Bricker
Source: ChemRxiv (preprint, 2023-06-28)
Field: Materials Science — Materials Chemistry

Building an ab initio solvated DNA model using Euclideanneural networks Accurately modeling large biomolecules such as DNA from first principles is fundamentally challenging due to the steep computational scaling of ab initio quantum chemistry methods. This limitation becomes even more prominent when modeling biomolecules in solution due to the need to include large numbers of solvent molecules. We present a machine learning electron density model based on a Euclidean neural network framework to model explicitly solvated double-stranded DNA. The neural network framework has a built-in understanding of equivariance, allowing the model to learn 3D structural information efficiently by exploiting the properties of Euclidean symmetry. By training the machine learning model using molecular fragments that sample the key DNA and solvent interactions, we show that the model predicts electron densities of arbitrary systems of solvated DNA accurately, resolves polarization effects that are neglected by classical force fields, and captures the physics of the DNA-solvent interaction at the ab initio level. Ab initio ; Biomolecule ; Artificial neural network ; Euclidean geometry ; Statistical physics ; Quantum ; Molecule ; Ab initio quantum chemistry methods ; Chemical physics ; Chemistry ; Physics ; Computational chemistry ; Computer science ; Biological system ; Quantum mechanics ; Materials science ; Nanotechnology ; Artificial intelligence ; Mathematics ; Geometry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Jian Zhu | Yasen Cui | Wei Zhou | Huaijuan Zang | Haiyan Zhang | Yongsheng Ren | Jiajia Xu | Shu Zhan | Wenhui Ma
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Materials Science — Materials Chemistry

Rkl: Reward-Kullback-Leibler Divergence Method for Improving Machine Learning Model Prediction Performance Kullback–Leibler divergence ; Divergence (linguistics) ; Computer science ; Artificial intelligence ; Machine learning Machine Learning in Materials Science ; Neural Networks and Applications ; Machine Learning and ELM
Authors: Niklas Breustedt | Paolo Climaco | Jochen Garcke | Jan Hamaekers | Gitta Kutyniok | Dirk A. Lorenz | Rick Oerder | Chirag Varun Shukla
Source: arXiv (Cornell University) (preprint, 2023-06-15)
Field: Materials Science — Materials Chemistry

On the Interplay of Subset Selection and Informed Graph Neural Networks Machine learning techniques paired with the availability of massive datasets dramatically enhance our ability to explore the chemical compound space by providing fast and accurate predictions of molecular properties. However, learning on large datasets is strongly limited by the availability of computational resources and can be infeasible in some scenarios. Moreover, the instances in the datasets may not yet be labelled and generating the labels can be costly, as in the case of quantum chemistry computations. Thus, there is a need to select small training subsets from large pools of unlabelled data points and to develop reliable ML methods that can effectively learn from small training sets. This work focuses on predicting the molecules atomization energy in the QM9 dataset. We investigate the advantages of employing domain knowledge-based data sampling methods for an efficient training set selection combined with informed ML techniques. In particular, we show how maximizing molecular diversity in the training set selection process increases the robustness of linear and nonlinear regression techniques such as kernel methods and graph neural networks. We also check the reliability of the predictions made by the graph neural network with a model-agnostic explainer based on the rate distortion explanation framework. Computer science ; Machine learning ; Artificial neural network ; Artificial intelligence ; Graph ; Robustness (evolution) ; Computation ; Training set ; Data mining ; Theoretical computer science ; Algorithm Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: F. E. Abeng
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Materials Science — Materials Chemistry

Combined Electrochemical, Dft/Molecular Dynamic Simulation and Machine Learning Models for Corrosion Inhibition of Mild Steel in 0.5 M H2so4 Solution Using Doxorubicin Electrochemistry ; Corrosion ; Doxorubicin ; Materials science ; Molecular dynamics ; Corrosion inhibitor ; Computer science ; Metallurgy ; Chemistry ; Computational chemistry ; Physical chemistry ; Electrode Corrosion Behavior and Inhibition ; Concrete Corrosion and Durability ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Astrid Klipfel | Yaël Frégier | Adlane Sayede | Zied Bouraoui
Source: arXiv (Cornell University) (preprint, 2023-06-07)
Field: Materials Science — Materials Chemistry

Optimized Crystallographic Graph Generation for Material Science Graph neural networks are widely used in machine learning applied to chemistry, and in particular for material science discovery. For crystalline materials, however, generating graph-based representation from geometrical information for neural networks is not a trivial task. The periodicity of crystalline needs efficient implementations to be processed in real-time under a massively parallel environment. With the aim of training graph-based generative models of new material discovery, we propose an efficient tool to generate cutoff graphs and k-nearest-neighbours graphs of periodic structures within GPU optimization. We provide pyMatGraph a Pytorch-compatible framework to generate graphs in real-time during the training of neural network architecture. Our tool can update a graph of a structure, making generative models able to update the geometry and process the updated graph during the forward propagation on the GPU side. Our code is publicly available at https://github.com/aklipf/mat-graph. Computer science ; Graph ; Implementation ; Theoretical computer science ; Generative grammar ; Artificial neural network ; Data structure ; Deep learning ; Generative model ; Parallel computing ; Artificial intelligence ; Programming language Machine Learning in Materials Science ; Graph Theory and Algorithms ; Advanced Graph Neural Networks
Authors: Jia Zhou
Source: University Chemistry (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Constructing Potential Energy Surface of Water Molecule by Quantum Chemistry and Machine Learning: Introduction to a Comprehensive Computational Chemistry Experiment Quantum chemistry ; Molecule ; Potential energy surface ; Chemistry ; Energy (signal processing) ; Engineering physics ; Nanotechnology ; Computer science ; Chemical physics ; Materials science ; Physics ; Quantum mechanics ; Organic chemistry Machine Learning in Materials Science
Authors: Rongzhi Dong | Nihang Fu | dirisuriya M. D. Siriwardane | Jianjun Hu
Source: arXiv (Cornell University) (preprint, 2023-09-30)
Field: Materials Science — Materials Chemistry

Generative Design of inorganic compounds using deep diffusion language models Due to the vast chemical space, discovering materials with a specific function is challenging. Chemical formulas are obligated to conform to a set of exacting criteria such as charge neutrality, balanced electronegativity, synthesizability, and mechanical stability. In response to this formidable task, we introduce a deep learning-based generative model for material composition and structure design by learning and exploiting explicit and implicit chemical knowledge. Our pipeline first uses deep diffusion language models as the generator of compositions and then applies a template-based crystal structure prediction algorithm to predict their corresponding structures, which is then followed by structure relaxation using a universal graph neural network-based potential. The density functional theory (DFT) calculations of the formation energies and energy-above-the-hull analysis are used to validate new structures generated through our pipeline. Based on the DFT calculation results, six new materials, including Ti2HfO5, TaNbP, YMoN2, TaReO4, HfTiO2, and HfMnO2, with formation energy less than zero have been found. Remarkably, among these, four materials, namely Ti2$HfO5, TaNbP, YMoN2, and TaReO4, exhibit an e-above-hull energy of less than 0.3 eV. These findings have proved the effectiveness of our approach. Computer science ; Crystal structure prediction ; Chemical space ; Pipeline (software) ; Generative grammar ; Stability (learning theory) ; Artificial intelligence ; Artificial neural network ; Electronegativity ; Algorithm ; Theoretical computer science ; Machine learning ; Chemistry ; Crystal structure Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Inorganic Chemistry and Materials