jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Rebecca Birolo | Rıza Özçelik | Andrea Aramini | Roberto Gobetto | Michele R. Chierotti | Francesca Grisoni
Source: ChemRxiv (preprint, 2024-06-17)
Field: Materials Science — Materials Chemistry

Deep Supramolecular Language Processing for Co-crystal Prediction Approximately 40% of marketed drugs exhibit suboptimal pharmacokinetic profiles. Co-crystallization, where pairs of molecules form a multicomponent crystal, constitutes a promising strategy to enhance physicochemical properties without compromising the pharmacological activity. However, finding promising co-crystal pairs is resource-intensive, due to the vast number of possible combinations. We present DeepCocrystal, a novel deep learning approach designed to predict co-crystal formation by processing the 'chemical language' from a supramolecular vantage point. Rigorous validation of DeepCocrystal showed a balanced accuracy of 78% in realistic scenarios, outperforming existing models. By leveraging the properties of molecular string representations, DeepCocrystal can also estimate the uncertainty of its predictions. We harness this capability in a challenging prospective study, and successfully discovered two novel co-crystal of diflunisal, an anti-inflammatory drug. This study underscores the potential of deep learning -- and in particular of chemical language processing -- to accelerate co-crystallization, and ultimately drug development, in both academic and industrial contexts. Supramolecular chemistry ; Computer science ; Crystal (programming language) ; Natural language processing ; Artificial intelligence ; Chemistry ; Crystal structure ; Crystallography ; Programming language Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Hydrocarbon exploration and reservoir analysis
Authors: Tristan Maxson | Ademola Soyemi | Benjamin W. J. Chen | Tibor Szilvási
Source: arXiv (Cornell University) (preprint, 2024-01-04)
Field: Materials Science — Materials Chemistry

Enhancing the Quality and Reliability of Machine Learning Interatomic Potentials through Better Reporting Practices Recent developments in machine learning interatomic potentials (MLIPs) have empowered even non-experts in machine learning to train MLIPs for accelerating materials simulations. However, the current literature lacks clear standards for documenting the use of MLIPs, which hinders the reproducibility and independent evaluation of the presented results. In this perspective, we aim to provide guidance on best practices for documenting MLIP use while walking the reader through the development and deployment of MLIPs including hardware and software requirements, generating training data, training models, validating predictions, and MLIP inference. We also suggest useful plotting practices and analyses to validate and boost confidence in the deployed models. Finally, we provide a step-by-step checklist for practitioners to use directly before publication to standardize the information to be reported. Overall, we hope that our work will encourage reliable and reproducible use of these MLIPs, which will accelerate their ability to make a positive impact in various disciplines including materials science, chemistry, and biology, among others. Computer science ; Inference ; Reliability (semiconductor) ; Software deployment ; Checklist ; Quality (philosophy) ; Perspective (graphical) ; Data science ; Software ; Machine learning ; Work (physics) ; Artificial intelligence ; Software engineering ; Engineering ; Psychology Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Electronic and Structural Properties of Oxides
Authors: Martin Schwade | Maximilian J. Schilcher | Christian Reverón Baecker | Manuel Grumet | David A. Egger
Source: The Journal of Chemical Physics (article, 2024-04-01)
Field: Materials Science — Materials Chemistry

Temperature-transferable tight-binding model using a hybrid-orbital basis Finite-temperature calculations are relevant for rationalizing material properties, yet they are computationally expensive because large system sizes or long simulation times are typically required. Circumventing the need for performing many explicit first-principles calculations, tight-binding and machine-learning models for the electronic structure emerged as promising alternatives, but transferability of such methods to elevated temperatures in a data-efficient way remains a great challenge. In this work, we suggest a tight-binding model for efficient and accurate calculations of temperature-dependent properties of semiconductors. Our approach utilizes physics-informed modeling of the electronic structure in the form of hybrid-orbital basis functions and numerically integrating atomic orbitals for the distance dependence of matrix elements. We show that these design choices lead to a tight-binding model with a minimal amount of parameters that are straightforwardly optimized using density functional theory or alternative electronic-structure methods. The temperature transferability of our model is tested by applying it to existing molecular-dynamics trajectories without explicitly fitting temperature-dependent data and comparison with density functional theory. We utilize it together with machine-learning molecular dynamics and hybrid density functional theory for the prototypical semiconductor gallium arsenide. We find that including the effects of thermal expansion on the onsite terms of the tight-binding model is important in order to accurately describe electronic properties at elevated temperatures in comparison with experiment. Tight binding ; Atomic orbital ; Statistical physics ; Density functional theory ; Computer science ; Electronic structure ; Basis (linear algebra) ; Hybrid functional ; Physics ; Computational chemistry ; Chemistry ; Mathematics ; Quantum mechanics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; 2D Materials and Applications
Authors: Antoine Loew | Hai‐Chen Wang | Tiago F. T. Cerqueira | Miguel A. L. Marques
Source: Machine Learning Science and Technology (article, 2024-10-14)
Field: Materials Science — Materials Chemistry

Training machine learning interatomic potentials for accurate phonon properties Abstract One of the major challenges in the development of universal machine learning interatomic potentials is accurately reproducing phonon properties. This issue appears to arise from the limitations of available datasets rather than the models themselves. To address this, we develop an extensive dataset of phonon calculations using density-functional perturbation theory (DFPT). We then show how this dataset can be used to train neural-network force fields, by implementing the training and the prediction of force constants in periodic crystals. This approach improves the quality of phonon properties prediction while reducing the number of structures needed for neural network training. We demonstrate the efficiency of this method using two examples of ternary phase diagrams: Ti–Nb–Ta and Li–B–C. In both cases, neural network predictions for the energy and forces show a considerable improvement, while phonon properties are predicted with high precision for all structures across the entire phase diagrams. Phonon ; Interatomic potential ; Computer science ; Materials science ; Condensed matter physics ; Statistical physics ; Physics ; Molecular dynamics ; Quantum mechanics Machine Learning in Materials Science ; Thermal properties of materials
Authors: Mohammad Madani | Anna Tarakanova
Source: Matter (article, 2024-05-10)
Field: Materials Science — Materials Chemistry

Protein dynamics inform protein structure: An interdisciplinary investigation of protein crystallization propensity Protein crystallization ; Protein dynamics ; Protein structure ; Crystallization ; Chemistry ; Computational biology ; Biology ; Biochemistry Enzyme Structure and Function ; Protein Structure and Dynamics ; Mass Spectrometry Techniques and Applications
Authors: B Elizabeth Hemso | Kevizali Neikha | Parineeta Das | Anshika Gupta | Амрит Пузари
Source: ChemistrySelect (article, 2024-10-01)
Field: Materials Science — Polymers and Plastics

Theoretical Investigation of Para‐Substituted Aniline Based Dendritic Architecture: In‐Silico ADMET, DFT, and Machine Learning Approach Abstract Dendrimers are polymeric compounds which exhibit notable advantages, particularly in medicinal chemistry. The therapeutic potential of these molecular architectures can be screened through in silico method which facilitates the identification of the ones with the highest potential for further investigation. Here, we have reported the in silico investigation of a series of dendritic architecture based on para‐substituted aniline. Density functional theory (DFT) method was employed to optimize the structures and to analyze the quantum chemical parameters. The absorption, distribution, metabolism, excretion, and toxicity (ADMET) profiling was additionally assessed, which unveiled favorable drug‐like characteristics and moderate to good bioactivity across all compounds. Utilizing the drug‐likeness property in machine learning techniques, the potential for predicting novel drug‐like dendrimers was explored. In silico ; Aniline ; Architecture ; Computer science ; Chemistry ; Artificial intelligence ; Combinatorial chemistry ; Organic chemistry ; Biochemistry Dendrimers and Hyperbranched Polymers ; Computational Drug Discovery Methods ; Nonlinear Optical Materials Research
Authors: Université Hassan II de Casablanca, 1Département de physique, Laboratoire de Physique de la Matière Condensée (LPMC), Faculté des Sciences Ben M'Sik | Mouna Sbai Idrissi | Ahmed El Hamdaoui | Tarik Chafiq | Université Hassan II de Casablanca,Département de géologie, Laboratoire de Physique de la Matière Condensée (LPMC), Faculté des Sciences Ben M'Sik
Source: Journal of marine Technology and Environment (article, 2024-03-28)
Field: Materials Science — Ceramics and Composites

YOUNG’S MODULUS OF CALCIUM-ALUMINO-SILICATE GLASSES: INSIGHTFROM MACHINE LEARNING Modern technologies require the development of new materials with exceptional properties. Machine Learning (ML) and Deep Learning (DL) techniques have become important tools for discovering new materials and predicting the properties of specific materials, such as glasses. In this paper, we used ML and DL techniques to predict the Young's modulus E of Calcium-Alumino-Silicate (CAS) glasses based on their chemical composition. We evaluated four different algorithms, including Polynomial Regression (PR), Random Forest (RF), K-Nearest Neighbors (KNN), and Multi-Layer Perceptron Regressor (MLPRegressor). We found that the PR algorithm provides excellent predictions without Cross-Validation (CV), while the MLPRegressor yields the best performance when CV is implemented. Perceptron ; Random forest ; Calcium silicate ; Machine learning ; Artificial intelligence ; Modulus ; Materials science ; Regression ; Calcium aluminosilicate ; Multilayer perceptron ; Computer science ; Algorithm ; Artificial neural network ; Mineralogy ; Aluminosilicate ; Geology ; Mathematics ; Composite material ; Chemistry ; Statistics Glass properties and applications ; Pigment Synthesis and Properties ; Recycling and utilization of industrial and municipal waste in materials production
Authors: Bingqing Cheng
Source: Journal of Chemical Theory and Computation (article, 2024-10-04)
Field: Materials Science — Materials Chemistry

Response Matching for Generating Materials and Molecules Diffusion models have recently emerged as powerful tools for the generation of new molecular and material structures. The key insight is that the noise in these models is related to the response of the atoms to displacement, and the denoising step is thus analogous to the geometry relaxation of atomistic systems starting from a random structure. Building on this, we present a generative method called Response Matching (RM), which leverages the fact that each stable material or molecule exists at the minimum of its potential energy surface. Any perturbation induces a response in energy and stress, driving the structure back to equilibrium. Matching this response is closely related to score matching in diffusion models. Another important aspect of state-of-the-art diffusion models is the incorporation of physical symmetries such as translation, rotation, and periodicity. RM employs a machine learning interatomic potential and random structure search as the denoising model, inherently respecting these symmetries and exploiting the locality of atomic interactions. RM handles both molecules and bulk materials under the same framework. Its efficiency and generalization are demonstrated on three systems: a small organic molecular data set, stable crystals from the Materials Project, and one-shot learning on a single diamond configuration. Matching (statistics) ; Computer science ; Data science ; Nanotechnology ; Information retrieval ; Data mining ; Materials science ; Medicine Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Leonardo R. Almeida | Antônio S. N. Aguiar | Alex B. R. M. da Anunciação | Giulio D. C. D’Oliveira | Wesley F. Vaz | Jean M. F. Custódio | Caridad N. Pérez | Hamilton B. Napolitano
Source: ACS Omega (article, 2024-12-09)
Field: Chemistry — Organic Chemistry

Three Dihydroquinolin-4-one Derivatives as Potential Biodiesel Additives: From the Molecular Structure to Machine Learning Approach Biodiesel offers an alternative to fossil fuels, primarily because it is derived from renewable sources, with the potential to mitigate issues such as pollutant and greenhouse gas emissions, resource scarcity, and the market instability of petroleum derivatives. However, lower durability and stability pose challenges. To address this, researchers worldwide are exploring technologies that employ specific molecules to slow down biodiesel's oxidation process, thereby preserving its key physicochemical properties. This study investigates heterocyclic dihydroquinolinone derivatives as potential additives to enhance the oxidative stability of diesel-biodiesel blends. Comprehensive structural and computational analyses were carried out by density functional theory to investigate the reactivity aspects of these compounds as potential additive candidates. The supramolecular arrangements were predominantly stabilized by weak molecular interactions, such as C-H···O and C-H···π, which are associated with antioxidant and antibacterial properties. We demonstrate that these groups can act as electron-donating or electron-withdrawing substituents. We explored frontier molecular orbitals, which provide insights into chemical reactivity, acidity, basicity, and the best oxidizing and reducing agents. Finally, the molecular chemical potential maps indicate the nucleophilic and electrophilic regions and the Fukui indices show the sites of nucleophilic, electrophilic, and radical attacks. This comprehensive study paves the way to understanding how dihydroquinolinone-based compounds serve as alternatives for fuel additives. Biodiesel ; Combinatorial chemistry ; Artificial intelligence ; Computer science ; Chemistry ; Biochemical engineering ; Organic chemistry ; Catalysis ; Engineering Inorganic and Organometallic Chemistry ; Chemical Thermodynamics and Molecular Structure ; Analytical Chemistry and Chromatography
Authors: Yuanqing Lu | Timur R. Fazletdinov | Zhiwen Pan | Katrin Wondraczek | Lothar Wondraczek
Source: Advanced Materials Technologies (article, 2024-02-09)
Field: Materials Science — Materials Chemistry

Prediction of Phase Composition and Process Resilience in Plasma‐Assisted Hetero‐Aggregate Synthesis using a Machine‐Learning Model with Multivariate Output Abstract The synthesis of nanoscale particles and particle aggregates from liquid or gaseous precursors is affected by a variety of trade‐off relations, for example, in terms of product composition, yield, or energy efficiency. Machine‐supported process evaluation and learning (ML) of these relations enables optimization strategies for advanced material processing. Such a workflow is demonstrated on the example of plasma‐assisted aerosol deposition (PAAD) of alumina powders. Depending on processing conditions, these powders comprise of hetero‐aggregate mixtures of crystalline and amorphous polymorphs. Process optimization toward a specific target composition calls for ML approaches. For this, a sufficiently large and consistent dataset of PAAD input (processing) and output (product) parameters is initially generated by real‐world processing, and subsequently extrapolated into a cloud of ≈10 6 input‐output parameter matrices using Gaussian process regression with multivariate output and input‐output feature analysis. It is subsequently demonstrated how not only the phase composition of the obtained alumina powders, but also product resilience to variations in specific processing parameters, or – as a perspective – the energy efficiency of material processing can be predicted. Multivariate statistics ; Aggregate (composite) ; Resilience (materials science) ; Process (computing) ; Multivariate analysis ; Phase (matter) ; Computer science ; Machine learning ; Composition (language) ; Artificial intelligence ; Chemistry ; Materials science ; Nanotechnology ; Philosophy ; Programming language Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Advanced Materials Characterization Techniques
Authors: Eliška Mikmeková | Jiří Materna | Ivo Konvalina | Šárka Mikmeková | Ilona Müllerová | Tewodros Asefa
Source: Ultramicroscopy (article, 2024-04-10)
Field: Materials Science — Surfaces, Coatings and Films

A soft touch with electron beams: Digging out structural information of nanomaterials with advanced scanning low energy electron microscopy coupled with deep learning Digging ; Nanomaterials ; Nanotechnology ; Scanning electron microscope ; Electron ; Electron microscope ; Materials science ; Engineering physics ; Physics ; Optics ; Composite material ; Nuclear physics ; Geography Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications ; Machine Learning in Materials Science
Authors: Milo Roucairol | Alexios Georgiou | Tristan Cazenave | Filippo Prischi | Olivier E. Pardo
Source: ChemRxiv (preprint, 2024-05-28)
Field: Materials Science — Materials Chemistry

DrugSynthMC: an atom based generation of drug-like molecules with Monte Carlo Search A growing number of Deep Learning (DL) methodologies have recently been developed to design novel compounds and expand the chemical space within virtual libraries. Most of these Neural Network approaches design molecules to specifically bind a target, based on its structural information and/or knowledge of previously identified binders. Fewer attempts have been made to develop approaches for de novo design of virtual libraries, as synthesizability of generated molecules remains a challenge. In this work, we developed a new Monte Carlo Search (MCS) algorithm, DrugSynthMC (Drug Synthetise using Monte Carlo), in conjunction with DL and statistical-based priors to generate thousands of interpretable chemical structures and novel drug-like molecules per second. DrugSynthMC produces drug-like compounds using an atom-based search model that builds molecules as SMILES, character by character. Designed molecules follow Lipinski’s “rule of 5”, show a high proportion of predicted-to-be synthesisable compounds and efficiently expand the chemical space within the libraries, without reliance on training datasets, synthesizability metrics or enforcing during SMILES generation. Our approach can function with or without an underlying Neural Network and is thus easily explainable and versatile. This ease in drug-like molecule generation allows for future integration of score functions aimed at different target- or job -oriented goals. Thus, DrugSynthMC is expected to enable the functional assessment of large compound libraries covering an extensive novel chemical space, overcoming the limitations of existing drug collections. The software is available at https://github.com/RoucairolMilo/DrugSynthMC Monte Carlo method ; Atom (system on chip) ; Statistical physics ; Drug ; Molecule ; Computer science ; Physics ; Medicine ; Mathematics ; Quantum mechanics ; Pharmacology ; Statistics ; Parallel computing Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Nanomaterials for catalytic reactions
Authors: Fumiyasu Oba | Takayuki Nagai | Ryoji Katsube | Yasuhide Mochizuki | Masatake Tsuji | Guillaume Deffrennes | Kota Hanzawa | Akitoshi Nakano | Akira Takahashi | Kei Terayama | Ryo Tamura | Hidenori Hiramatsu | Yoshitaro Nose | Hiroki Taniguchi
Source: Science and Technology of Advanced Materials (review, 2024-11-04)
Field: Materials Science — Materials Chemistry

Theoretical and data-driven approaches to semiconductors and dielectrics: from prediction to experiment Computational approaches using theoretical calculations and data scientific methods have become increasingly important in materials science and technology, with the development of relevant methodologies and algorithms, the availability of large materials data, and the enhancement of computer performance. As reviewed herein, we have developed computational methods for the design and prediction of inorganic materials with a particular focus on the exploration of semiconductors and dielectrics. High-throughput first-principles calculations are used to systematically and accurately predict the local atomic and electronic structures of polarons, point defects, surfaces, and interfaces, as well as bulk fundamental properties. Machine learning techniques are utilized to efficiently predict various material properties, construct phase diagrams, and search for materials satisfying target properties. These computational approaches have elucidated the mechanisms behind material functionalities and explored promising materials in combination with synthesis, characterization, and device fabrication. Examples include the development of ternary nitride semiconductors for potential optoelectronic and photovoltaic applications, the exploration of phosphide semiconductors and the optimization of heterointerfaces toward the improvement of phosphide-based photovoltaic cells, and the discovery of ferroelectricity in layered perovskite oxides and the theoretical understanding of its origin, all of which demonstrate the effectiveness of our computer-aided materials research. Semiconductor ; Phosphide ; Materials science ; Ternary operation ; Characterization (materials science) ; Dielectric ; Computer science ; Nanotechnology ; Electronics ; Engineering physics ; Optoelectronics ; Physics ; Chemistry Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; Electronic and Structural Properties of Oxides