jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Zhiqiang Zhao | Wanlin Guo | Zhuhua Zhang
Source: arXiv (Cornell University) (preprint, 2024-03-14)
Field: Materials Science — Materials Chemistry

A general-purpose neural network potential for Ti-Al-Nb alloys towards large-scale molecular dynamics with ab initio accuracy High Nb-containing TiAl alloys exhibit exceptional high-temperature strength and room-temperature ductility, making them widely used in hot-section components of automotive and aerospace engines. However, the lack of accurate interatomic interaction potentials for large-scale modeling severely hampers a comprehensive understanding of the failure mechanism of Ti-Al-Nb alloys and the development of strategies to enhance the mechanical properties. Here, we develop a general-purpose machine-learned potential (MLP) for the Ti-Al-Nb ternary system by combining the neural evolution potentials framework with an active learning scheme. The developed MLP, trained on extensive first-principles datasets, demonstrates remarkable accuracy in predicting various lattice and defect properties, as well as high-temperature characteristics such as thermal expansion and melting point for TiAl systems. Notably, this potential can effectively describe the key effect of Nb doping on stacking fault energies and formation energies. Of practical importance is that our MLP enables large-scale molecular dynamics simulations involving tens of millions of atoms with ab initio accuracy, achieving an outstanding balance between computational speed and accuracy. These results pave the way for studying micro-mechanical behaviors in TiAl lamellar structures and developing high-performance TiAl alloys towards applications at elevated temperatures. Scale (ratio) ; Ab initio ; Artificial neural network ; Molecular dynamics ; Dynamics (music) ; Atomic units ; Materials science ; Statistical physics ; Computational chemistry ; Computer science ; Artificial intelligence ; Physics ; Chemistry ; Quantum mechanics Machine Learning in Materials Science ; Titanium Alloys Microstructure and Properties ; Nuclear Materials and Properties
Authors: Andrew J. Lew
Source: Preprints.org (preprint, 2024-09-11)
Field: Materials Science — Materials Chemistry

A Brief Survey of ML Methods Predicting Molecular Solubility: Towards Lighter Models via Attention and Hyperparameter Optimization Traditional chemical research often relies on trial-and-error synthesis and characterization methods. Now, modern machine learning (ML) offers data-driven approaches for predicting properties, like water solubility, directly from chemical structure. But with various data representation schemes for molecular structure and model approaches to select from, it can be difficult for non-experts to determine best practices for utilizing ML. To clarify this landscape of choices, this study uses the ESOL molecular solubility dataset to compare the performance of a selection of different models on different data representations. First, we compare three classical regression methods (linear, ridge, LASSO) on three common data representations (RDKit fingerprint, Morgan fingerprint, intuition-selected molecular features). Then, we demonstrate how two distinct deep learning approaches (multilayer perceptron, graph convolution) can achieve accurate predictions even when prior intuition about feature-property correlations are absent. Finally, we outline a modern attention-based approach, inspired by successes in language modeling and fine-tuned by Bayesian optimization, to achieve a prediction methodology that is more general and performant than the previous approaches. This attention-based approach operates directly on the common SMILES string molecular representation, without requiring as many model parameters as other deep learning approaches or preprocessing into a representative fingerprint or vector of intuitively selected features. In short, when selected molecular features are known to likely correlate with a feature of interest, it may be possible to achieve good predictive modeling without turning to massive deep learning approaches. When particular features are not known a priori, graph approaches are a common solution, and we further demonstrate how a modern hyperparameter optimized attention approach can perform even better. Hyperparameter ; Solubility ; Machine learning ; Computer science ; Chemistry ; Organic chemistry Machine Learning in Materials Science ; Analytical Chemistry and Chromatography
Authors: Eric Warnemunde Vertina | Emily Sutherland | N. Aaron Deskins | Oren Mangoubi
Source:  (article, 2024-09-08)
Field: Materials Science — Materials Chemistry

MXene Property Prediction via Graph Contrastive Learning MXenes are an important class of 2-D materials with expected novel properties and myriad applications, including efficient energy conversion in batteries and solar cells, environmental and water treatment, supercapacitors, electromagnetic interference shielding, and catalysts. Unfortunately, MXenes are challenging to synthesize in the laboratory, and numerical models for simulating MXene properties, such as density functional theory (DFT), remain computationally expensive. For this reason, multiple prior works develop machine learning (ML) methods to rapidly predict MXene properties. However, these methods do not take into account the graphical structure of the MXene’s molecular bonds, reducing their accuracy. In this work, we introduce a novel ML method, based on Graph Neural Networks (GNNs), which includes MXene molecular graph information to predict electrical and physical properties. Our method transforms graph datasets to lower-dimensional embedding spaces with graph contrastive learning (GCL), a type of GNN, enabling high-accuracy predictions for downstream models trained with these embeddings. Our method obtains the following root mean square errors (RMSEs) when predicting properties from a DFT-based MXenes dataset (including many MXene properties not previously predicted with deep learning models): 0.418 J for Work Function, $0.518 \mathrm{eV}^{-1}$ for Density of States at Fermi Level, $\mathbf{0. 0 6 7}$ for Poisson’s Ratio, $21.65 \mathrm{~N} / \mathrm{m}$ for Bulk Modulus, 14.56 N/m for Shear Modulus, 35.66 N/m for Young’s Modulus, and 82% accuracy for Magnetic (T/F). Our method obtains lower mean absolute error (MAE) when predicting four target properties from the molecular QM9 dataset compared to a similar-complexity baseline. Predicting MXene properties more accurately, efficiently, and effectively promises to better enable MXene commercialization. Computer science ; Property (philosophy) ; Graph ; Artificial intelligence ; Natural language processing ; Theoretical computer science MXene and MAX Phase Materials ; Ferroelectric and Negative Capacitance Devices ; 2D Materials and Applications
Authors: Yoyo Hinuma
Source: The Journal of Physical Chemistry B (article, 2024-11-28)
Field: Chemistry — Inorganic Chemistry

Neural Network Potential Molecular Dynamics Simulations of (La,Ce,Pr,Nd)<sub>0.95</sub>(Mg,Zn,Pb,Cd,Ca,Sr,Ba)<sub>0.05</sub>F<sub>2.95</sub> Tysonite structure fluorides doped with divalent cations, represented by Ce<sub>0.95</sub>Ca<sub>0.05</sub>F<sub>2.95</sub>, are a class of good F<sup>-</sup> ion conductors together with fluorite-structured compounds. Computational understanding of the F<sup>-</sup> conduction process is difficult because of the complicated interactions between three symmetrically distinct F sites and the experimentally observed change in the F diffusion mechanism slightly above room temperature, effectively making first-principles molecular dynamics (FP-MD) simulations, which are often conducted well above the transition temperature, useless when analyzing behavior below the transition point. Neural network potential (NNP) MD simulations showed that the F diffusion coefficient is higher when the divalent dopant cation size is similar to the trivalent cation size. The diffusion behavior of F in different sites changes at roughly 500 K in Ce<sub>0.95</sub>Ca<sub>0.05</sub>F<sub>2.95</sub> because only the F1 site sublattice contributes to F diffusion below this temperature, but the remaining F2 and F3 sublattices become gradually active above this temperature. The paradox of higher diffusion coefficients in CeF<sub>3</sub>-based compounds than similar LaF<sub>3</sub>-based compounds even though the lattice parameters are larger in the latter may be caused by a shallower potential of Ce and F in CeF<sub>3</sub> compared to the LaF<sub>3</sub> counterparts. Molecular dynamics ; Artificial neural network ; Physical chemistry ; Analytical Chemistry (journal) ; Materials science ; Chemistry ; Physics ; Computer science ; Computational chemistry ; Artificial intelligence ; Environmental chemistry Inorganic Fluorides and Related Compounds ; Rare-earth and actinide compounds ; Chemical Thermodynamics and Molecular Structure
Authors: Bruno Focassio | Luis Paulo Mezzina Freitas | Gabriel R. Schleder
Source: arXiv (Cornell University) (preprint, 2024-03-07)
Field: Materials Science — Materials Chemistry

Models and code from: Performance Assessment of Universal Machine Learning Interatomic Potentials: Challenges and Directions for Materials' Surfaces All trained models, training data, and test data are available as files with instructions on their use in a compressed .zip file. Computer science ; Artificial intelligence ; Nanotechnology ; Materials science Machine Learning in Materials Science
Authors: Kangming Li | Brian DeCost | Kamal Choudhary | Jason Hattrick‐Simpers
Source: Digital Discovery (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

A reproducibility study of atomistic line graph neural networks for materials property prediction ALIGNN performance on 29 regression tasks can be generally well reproduced with minor disparity due to stochasticity. Reproducibility ; Property (philosophy) ; Graph ; Computer science ; Artificial neural network ; Artificial intelligence ; Mathematics ; Theoretical computer science ; Statistics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Welding Techniques and Residual Stresses
Authors: Junlin Dong | Shiyu Wang | Wenqiang Cui | Xiaolin Sun | Haojie Guo | Hailu Yan | Horst Vogel | Zhi Wang | Shuguang Yuan
Source: Journal of Chemical Theory and Computation (article, 2024-02-23)
Field: Materials Science — Materials Chemistry

Machine Learning Deciphered Molecular Mechanistics with Accurate Kinetic and Thermodynamic Prediction Time-lagged independent component analysis (tICA) and the Markov state model (MSM) have been extensively employed for extracting conformational dynamics and kinetic community networks from unbiased trajectory ensembles. However, these techniques may not be the optimal choice for elucidating transition mechanisms within low-dimensional representations, especially for intricate biosystems. Unraveling the association mechanism in such complex systems always necessitates permutations of several essential independent components or collective variables, a process that is inherently obscure and may require empirical knowledge for selection. To address these challenges, we have implemented an integrated unsupervised dimension reduction model: uniform manifold approximation and projection (UMAP) with hierarchy density-based spatial clustering of applications with noise (HDBSCAN). This approach effectively generates low-dimensional configurational embeddings. The hierarchical application of this architecture, in conjunction with MSM, reveals global kinetic connectivity while identifying local conformational states. Consequently, our methodology establishes a multiscale mechanistic elucidation framework. Leveraging the benefits of the uniform sample distribution and a denoising approach, our model demonstrates robustness in preserving global and local data structures compared to traditional dimension reduction methods in the field of MD analysis area. The interpretability of hyperparameter selection and compatibility with downstream tasks are cross-validated across various simulation data sets, utilizing both computational evaluation metrics and experimental kinetic observables. Furthermore, the predicted Mcl1-BH3 association kinetics (0.76 s<sup>-1</sup>) is in close agreement with surface plasmon resonance experiments (0.12 s<sup>-1</sup>), affirming the plausibility of the identified pathway composed of representative conformations. We anticipate that the devised workflow will serve as a foundational framework for studying recognition patterns in complex biological systems. Its contributions extend to the exploration of protein functional dynamics and rational drug design, offering a potent avenue for advancing research in these domains. Computer science ; Kinetic energy ; Physics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Hyunsoo Park | Anthony Onwuli | Keith T. Butler | Aron Walsh
Source: ChemRxiv (preprint, 2024-01-02)
Field: Materials Science — Materials Chemistry

Mapping inorganic crystal chemical space The combination of elements from the Periodic Table defines a vast chemical space. Only a small fraction of these combinations yield materials that occur naturally or are accessible synthetically. Here, we enumerate binary, ternary, and quaternary element combinations to produce an extensive library of over 10^10 stoichiometric inorganic compositions. The unique combinations are vectorised using compositional embeddings drawn from a variety of published machine-learning models. Dimensionality reduction techniques are employed to present a two-dimensional representation of inorganic crystal-chemical space, which is labelled according to whether they pass standard chemical filters and if they appear in known materials databases. Chemical space ; Ternary operation ; Stoichiometry ; Space (punctuation) ; Yield (engineering) ; Crystal (programming language) ; Representation (politics) ; Binary number ; Dimensionality reduction ; Fraction (chemistry) ; Characterization (materials science) ; Periodic table ; Chemical composition ; Computer science ; Chemistry ; Materials science ; Mathematics ; Nanotechnology ; Arithmetic ; Artificial intelligence ; Physical chemistry ; Organic chemistry Machine Learning in Materials Science ; Geochemistry and Geologic Mapping ; History and advancements in chemistry
Authors: Yukari Katsura | Masakazu Akiyama | Haruhiko Morito | M. Fujioka | Tohru Sugahara
Source: Science and Technology of Advanced Materials (review, 2024-11-11)
Field: Materials Science — Materials Chemistry

Systematic searches for new inorganic materials assisted by materials informatics We introduce our proprietary Materials Informatics (MI) technologies and our chemistry-oriented methodology for exploring new inorganic functional materials. Using machine learning on crystal structure databases, we developed 'Element Reactivity Maps' that displays the presence or the predicted formation probability of compounds for combinations of 80 × 80 × 80 elements. By analysing atomic coordinates with Delaunay tetrahedral decomposition, we established the concept of Delaunay Chemistry. This enabled us to design crystal structures by combining Delaunay tetrahedra of known compounds and to develop the 'Crystal Cluster Simulator' web system. We also developed the Starrydata2 web system to collect large-scale experimental data on material properties from plot images in academic papers. This dataset supported us to select candidate materials for new thermoelectric materials through various data analyses. In large-scale synthesis experiments involving over 7,000 samples, we discovered numerous new phases, including solid solutions of known structures in new combinations of elements. Using sodium metal in synthesis and our proprietary ion diffusion control technologies, we discovered new cage-like compounds by extracting monovalent cations from materials with nano-framework structures, as well as new intercalation compounds. The Element Reactivity Maps were also used to select barrier metals for device electrodes, and an autonomous contact resistance measurement system is under development. Delaunay triangulation ; Computer science ; Materials science ; Nanotechnology ; Algorithm Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Jinbin Zhao | Junwen Lai | Jiantao Wang | Yao Zhang | Junlin Li | Xing‐Qiu Chen | Peitao Liu
Source: Materials Genome Engineering Advances (article, 2024-10-28)
Field: Materials Science — Materials Chemistry

Accelerating spin Hall conductivity predictions via machine learning Abstract Accurately predicting the spin Hall conductivity (SHC) is crucial for designing novel spintronic devices that leverage the spin Hall effect. First‐principles calculations of SHCs are computationally intensive and unsuitable for quick high‐throughput screening. Here, we have developed a residual crystal graph convolutional neural network (Res‐CGCNN) deep learning model to classify and predict SHCs solely based on the structural and compositional information. This is enabled by having access to 9249 instances of SHCs data and incorporating extra residual networks into the standard CGCNN framework. We found that Res‐CGCNN surpasses CGCNN, achieving a mean absolute error of 115.4 (ℏ/e) (S/cm) for regression and an area under the receiver operating characteristic curve of 0.86 for classification. Additionally, we utilized Res‐CGCNN to conduct high‐throughput screenings of materials in the Materials Project database that were absent in the training set. This led to the prediction of several previously unreported materials displaying large SHCs exceeding 1000 (ℏ/e) (S/cm), which were validated through first‐principles calculations. This study represents the inaugural endeavor to construct a machine learning model capable of effectively capturing the intricate nonlinear relationship between SHCs and crystal structure and composition, serving as a useful tool for the efficient screening and design of materials exhibiting high SHCs. Conductivity ; Condensed matter physics ; Spin (aerodynamics) ; Hall conductivity ; Spin Hall effect ; Materials science ; Computer science ; Physics ; Artificial intelligence ; Hall effect ; Electrical resistivity and conductivity ; Spin polarization ; Quantum mechanics ; Electron ; Thermodynamics Machine Learning in Materials Science ; Ferroelectric and Negative Capacitance Devices ; Advanced Memory and Neural Computing
Authors: Benjamin W. Gung | Cole Kubesch | G.J. Bernstein
Source: Catalysts (article, 2024-11-12)
Field: Chemistry — Inorganic Chemistry

Natural Chiral Ligand Strategy: Metal-Catalyzed Reactions with Ligands Prepared from Amino Acids and Peptides Amino acids and peptides are readily available biomolecules and can function as chiral ligands for transition metal catalysis. An example is the copper complex catalyzed 1,4-addition of dialkylzinc to acyclic enones, which employs peptide ligands. This review provides a dataset of amino acids and peptides reported in the literature proving to be effective ligands for metal-centered catalysts. Several parameters were highlighted, including amino acid combination, metal atoms, carboxyl and amino protecting groups, modification of natural amino acids, and the mechanism of catalysis. Along with analyzing physical-chemical properties, the SMILES representation for each amino acid and/or peptide was generated and made available online, providing an easy-to-use means of training machine learning models. This review offers an opportunity for the development of more efficient peptide ligands for enantioselective metal-centered catalysts. The available online dataset is a reliable manually curated table, it enables the benchmark for comparison of new terminal functional groups. Moreover, the review provides insight into the structures of the more successful peptide ligands and can be used as the foundation for the development of the next generation of peptide-based chiral ligands. Chemistry ; Catalysis ; Ligand (biochemistry) ; Combinatorial chemistry ; Metal ; Amino acid ; Organic chemistry ; Biochemistry ; Receptor Asymmetric Hydrogenation and Catalysis ; Synthesis and Catalytic Reactions ; Asymmetric Synthesis and Catalysis
Authors: Hossein Shirani | Seyed Majid Hashemianzadeh
Source: Computational Biology and Chemistry (article, 2024-07-14)
Field: Materials Science — Materials Chemistry

Quantum-level machine learning calculations of Levodopa Levodopa ; Computer science ; Quantum ; Artificial intelligence ; Psychology ; Machine learning ; Cognitive psychology ; Medicine ; Parkinson's disease ; Physics ; Quantum mechanics ; Internal medicine ; Disease Machine Learning in Materials Science ; Advanced Memory and Neural Computing ; Computational Drug Discovery Methods
Authors: Federico Zipoli | Zeineb Ayadi | Philippe Schwaller | Teodoro Laino | Alain C. Vaucher
Source: Machine Learning Science and Technology (article, 2024-06-01)
Field: Chemistry — Physical and Theoretical Chemistry

Completion of partial chemical equations Abstract Inferring missing molecules in chemical equations is an important task in chemistry and drug discovery. In fact, the completion of chemical equations with necessary reagents is important for improving existing datasets by detecting missing compounds, making them compatible with deep learning models that require complete information about reactants, products, and reagents in a chemical equation for increased performance. Here, we present a deep learning model to predict missing molecules using a multi-task approach, which can ultimately be viewed as a generalization of the forward reaction prediction and retrosynthesis models, since both can be expressed in terms of incomplete chemical equations. We illustrate that a single trained model, based on the transformer architecture and acting on reaction SMILES strings, can address the prediction of products (forward), precursors (retro) or any other molecule in arbitrary positions such as solvents, catalysts or reagents (completion). Our aim is to assess whether a unified model trained simultaneously on different tasks can effectively leverage diverse knowledge from various prediction tasks within the chemical domain, compared to models trained individually on each application. The multi-task models demonstrate top-1 performance of 72.4%, 16.1%, and 30.5% for the forward, retro, and completion tasks, respectively. For the same model we computed round-trip accuracy of 83.4%. The completion task exhibiting improvements due to the multi-task approach. Chemical equation ; Applied mathematics ; Mathematics ; Chemistry ; Physical chemistry Various Chemistry Research Topics ; History and advancements in chemistry