jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Michał Koziarski | Andrei Rekesh | Dmytro Shevchuk | Almer M. van der Sloot | Piotr Gaiński | Yoshua Bengio | Chenghao Liu | Mike Tyers | Robert A. Batey
Source: arXiv (Cornell University) (preprint, 2024-06-01)
Field: Materials Science — Materials Chemistry

RGFN: Synthesizable Molecular Generation Using GFlowNets Generative models hold great promise for small molecule discovery, significantly increasing the size of search space compared to traditional in silico screening libraries. However, most existing machine learning methods for small molecule generation suffer from poor synthesizability of candidate compounds, making experimental validation difficult. In this paper we propose Reaction-GFlowNet (RGFN), an extension of the GFlowNet framework that operates directly in the space of chemical reactions, thereby allowing out-of-the-box synthesizability while maintaining comparable quality of generated candidates. We demonstrate that with the proposed set of reactions and building blocks, it is possible to obtain a search space of molecules orders of magnitude larger than existing screening libraries coupled with low cost of synthesis. We also show that the approach scales to very large fragment libraries, further increasing the number of potential molecules. We demonstrate the effectiveness of the proposed approach across a range of oracle models, including pretrained proxy models and GPU-accelerated docking. Computer science ; Computer architecture Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Saurabh Sivakumar | Ambarish Kulkarni
Source: The Journal of Physical Chemistry C (article, 2024-07-27)
Field: Materials Science — Materials Chemistry

Toward an ab Initio Description of Adsorbate Surface Dynamics The advent of machine learning potentials (MLPs) provides a unique opportunity to access simulation time scales and to directly compute physicochemical properties that are typically intractable using density functional theory (DFT). In this study, we use an active learning curriculum to train a generalizable MLP using the DeepMD-kit architecture. By using sufficiently long MLP-based molecular dynamics (MD) simulations, which provide DFT-level accuracy, we investigate the diffusion of key surface-bound adsorbates on a Ag(111) facet. Detailed analysis of the MLP/MD-calculated diffusivities sheds light on the potential shortcomings of using DFT-based nudged elastic band to estimate surface diffusion barriers. More generally, while this study is focused on a specific system, we anticipate that the underlying workflows and the resulting models can be extended to other adsorbates and other materials in the future. Density functional theory ; Molecular dynamics ; Computer science ; Ab initio ; Diffusion ; Workflow ; Key (lock) ; Surface (topology) ; Facet (psychology) ; Statistical physics ; Materials science ; Chemical physics ; Computational chemistry ; Chemistry ; Physics ; Mathematics ; Thermodynamics ; Quantum mechanics ; Geometry Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Surface and Thin Film Phenomena
Authors: Salvador R. G. Balestra | Noelia Rodríguez-Sánchez | Dayrelis Mena-Torres | A. Rabdel Ruiz‐Salvador
Source: Crystal Growth & Design (article, 2024-01-29)
Field: Chemistry — Inorganic Chemistry

Structural Features and Zeolite Stability: A Linearized Equation Approach Zeolite stability, in terms of lattice energy, is revisited from a crystal-chemistry point of view. A linearized equation relates the zeolite lattice energy using simple structural data readily available from experiments or modeling. The equation holds for a large range of zeolite energies, up to 3 eV per tetrahedron with respect to quartz, and has been validated internally via two simple machine learning automatic procedures for data fitting/reference partitions and externally using data from recently synthesized zeolites. The approach is certain in locating those recently synthesized zeolites in the energy range of those experimentally known zeolites used in the parametrization of the linearized equation. Hidden intrinsic structural data-energy correlations were found for data sets built from energy-relaxed structures along with energy values computed using the same energy functions employed in the structural relaxation. The asymmetry of the structural features is relevant for an accurate description of the energy. Lattice energy ; Tetrahedron ; Structural stability ; Zeolite ; Lattice (music) ; Parametrization (atmospheric modeling) ; Thermodynamics ; Chemistry ; Crystal structure ; Statistical physics ; Physics ; Crystallography ; Quantum mechanics Zeolite Catalysis and Synthesis ; Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Yasemin Şi̇mşek Türker | Şemsettin KILINÇARSLAN | Ebru Yılmaz İnce
Source: Wood Material Science and Engineering (article, 2024-07-25)
Field: Materials Science — Polymers and Plastics

Performance of ANN, Random Forest and XGBoost methods in predicting the flexural properties of wood beams reinforced with carbon-FRP Wooden material can be used in different areas due to its various positive properties. Glued Laminated wooden elements (glulam) are wood composite materials widely used especially in the construction industry. Carbon fiber-reinforced polymers (FRP) are widely used to increase the bearing capacity values of glulam beams and improve their overall load-displacement behavior. This study was carried out in two stages. In the first stage, the bending properties of glulam timbers of different sizes with wide spans reinforced with carbon fiber-reinforced polymers were experimentally examined. In the next stage, the obtained data were predicted with three different machine learning techniques (ANN, Random Forest and XGBoost). As a result of the study, it was determined that as the section dimensions increased, the bending properties increased, and the reinforcement was effective by approximately 22%. All three different prediction techniques used could make predictions with high accuracy. However, it was determined that the best prediction was made with Random Forest (R2: 0.9892). Therefore, the bending properties of reinforced beams of different sizes can be predicted with machine learning (ML) techniques. Fibre-reinforced plastic ; Flexural strength ; Random forest ; Structural engineering ; Composite material ; Reinforced concrete ; Carbon fibers ; Materials science ; Engineering ; Computer science ; Machine learning ; Composite number Natural Fiber Reinforced Composites ; Wood Treatment and Properties ; Structural Behavior of Reinforced Concrete
Authors: Nathan Rebello | Akash Arora | Hidenobu Mochigase | Tzyy‐Shyang Lin | Jiale Shi | Debra J. Audus | Eric S. Muckley | A. Sonan Osmani | Bradley D. Olsen
Source: Journal of Chemical Information and Modeling (article, 2024-08-10)
Field: Materials Science — Materials Chemistry

The Block Copolymer Phase Behavior Database The Block Copolymer Database (BCDB) is a platform that allows users to search, submit, visualize, benchmark, and download experimental phase measurements and their associated characterization information for di- and multiblock copolymers. To the best of our knowledge, there is no widely accepted data model for publishing experimental and simulation data on block copolymer self-assembly. This proposed data schema with traceable information can accommodate any number of blocks and at the time of publication contains over 5400 block copolymer total melt phase measurements mined from the literature and manually curated and simulation data points of the phase diagram generated from self-consistent field theory that can rapidly be augmented. This database can be accessed via the Community Resource for Innovation in Polymer Technology (CRIPT) web application and the Materials Data Facility. The chemical structure of the polymer is encoded in BigSMILES, an extension of the Simplified Molecular-Input Line-Entry System (SMILES) into the macromolecular domain, and the user can search repeat units and functional groups using the SMARTS search syntax (SMILES Arbitrary Target Specification). The user can also query characterization and phase information using Structured Query Language (SQL) and download custom sets of block copolymer data to train machine learning models. Finally, a protocol is presented in which GPT-4, an AI-powered large language model, can be used to rapidly screen and identify block copolymer papers from the literature using only the abstract text and determine whether they have BCDB data, allowing the database to grow as the number of published papers on the World Wide Web increases. The F1 score for this model is 0.74. This platform is an important step in making polymer data more accessible to the broader community. Computer science ; Database ; Copolymer ; Block (permutation group theory) ; Relational database ; Benchmark (surveying) ; Information retrieval ; Data mining ; Algorithm ; Chemistry ; Polymer Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Advanced Polymer Synthesis and Characterization
Authors: Incheol Jeong | Yoonsu Shim | Seeun Oh | Jong Min Yuk | Ki‐Min Roh | Chan‐Woo Lee | Kang Taek Lee
Source: Advanced Energy Materials (article, 2024-08-05)
Field: Materials Science — Materials Chemistry

A Machine Learning‐Enhanced Framework for the Accelerated Development of Spinel Oxide Electrocatalysts Abstract The surging demand for sustainable energy has spurred intensive research into electrochemical conversion devices such as fuel cells, water splitting, and metal‐air batteries. The performance of oxygen electrocatalysts significantly impacts overall electrochemical efficiency. Recently, spinel oxides (AB 2 O 4 ) have emerged as promising candidates; however, the scarcity of prior studies underscores the need for a thorough and comprehensive exploration. This study presents a computational framework that integrates machine learning and density functional theory (DFT) calculations for the systematic screening of 1240 spinel oxides. The data scarcity is addressed while enhancing prediction accuracy. Selected candidates are identified to outperform the benchmarking perovskite oxide. Additionally, their potential as mixed ionic and electronic conductors with a 3D network of ion diffusion pathways is highlighted. To further enhance the understanding and prediction of stability, catalytic activity, and reaction mechanisms, a new undemanding descriptor is introduced: the covalency indicator. This study offers a design principle for the development of high‐performance spinel oxide oxygen electrocatalysts. Materials science ; Spinel ; Oxide ; Nanotechnology ; Chemical engineering ; Engineering physics ; Metallurgy ; Engineering Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Advancements in Solid Oxide Fuel Cells
Authors: Qingkun Zhao | Qi Zhu | Zhenghao Zhang | Binglun Yin | Huajian Gao | Haofei Zhou
Source: Proceedings of the National Academy of Sciences (article, 2024-09-16)
Field: Materials Science — Materials Chemistry

A machine learning–based framework for mapping hydrogen at the atomic scale Hydrogen, the lightest and most abundant element in the universe, plays essential roles in a variety of clean energy technologies and industrial processes. For over a century, it has been known that hydrogen can significantly degrade the mechanical properties of materials, leading to issues like hydrogen embrittlement. A major challenge that has significantly limited scientific advances in this field is that light atoms like hydrogen are difficult to image, even with state-of-the-art microscopic techniques. To address this challenge, here, we introduce Atom-H, a versatile and generalizable machine learning-based framework for imaging hydrogen atoms at the atomic scale. Using a high-resolution electron microscope image as input, Atom-H accurately captures the distribution of hydrogen atoms and local stresses at lattice defects, including dislocations, grain boundaries, cracks, and phase boundaries. This provides atomic-scale insights into hydrogen-governed mechanical behaviors in metallic materials, including pure metals like Ni, Fe, Ti and alloys like FeCr. The proposed framework has an immediate impact on current research into hydrogen embrittlement and is expected to have far-reaching implications for mapping "invisible" atoms in other scientific disciplines. Atomic units ; Scale (ratio) ; Computer science ; Hydrogen ; Artificial intelligence ; Machine learning ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals ; Nuclear Physics and Applications
Authors: Naoto Kawaguchi | Kiyou Shibata | Teruyasu Mizoguchi
Source: ACS Physical Chemistry Au (article, 2024-03-07)
Field: Chemistry — Inorganic Chemistry

Unraveling the Stability of Layered Intercalation Compounds through First-Principles Calculations: Establishing a Linear Free Energy Relationship with Aqueous Ions Layered intercalation compounds, where atoms or molecules (intercalants) are inserted into layered materials (hosts), hold great potential for diverse applications. However, the lack of a systematic understanding of stable host-intercalant combinations poses challenges in materials design due to the vast combinatorial space. In this study, we performed first-principles calculations on 9024 compounds, unveiling a novel linear regression equation based on the principle of hard and soft acids and bases. This equation, incorporating the intercalant ion formation energy and ionic radius, quantitatively reveals the stability factors. Additionally, employing machine learning, we predicted regression coefficients from host properties, offering a comprehensive understanding and a predictive model for estimating the intercalation energy. Our work provides valuable insights into the energetics of layered intercalation compounds, facilitating targeted materials design. Ion ; Intercalation (chemistry) ; Aqueous solution ; Stability (learning theory) ; Energy (signal processing) ; Materials science ; Computational chemistry ; Thermodynamics ; Chemistry ; Chemical physics ; Inorganic chemistry ; Physical chemistry ; Computer science ; Physics ; Organic chemistry ; Quantum mechanics Inorganic Chemistry and Materials ; Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides
Authors: Yunlei Chen | Wende Hu | Xin Li | Chuanming Wang | Weimin Yang
Source: The Journal of Physical Chemistry C (article, 2024-02-21)
Field: Chemistry — Inorganic Chemistry

Machine Learning Prediction of Transition-State Energies in Small-Pore Zeolites Combining Acidity and Topology Descriptors Descriptor-based rational design approach has been extensively developed and applied for the computational discovery of high-performance catalysts, while it is still in its infancy in computational zeolite catalysis. Using toluene methylation with methanol to p-xylene as a model reaction, herein, we developed machine learning (ML) models for the prediction of transition-state (TS) energies in small-pore zeolites, taking both acidity and framework topology into account. General scaling relations between TS energies and the adsorption enthalpy of ammonia (ΔHNH3) as a descriptor of acid strength can be well established within individual zeolite frameworks, while acidity sensitivity varies across framework topologies. In order to capture the effect of the framework, we introduced several readily available zeolite topology descriptors to directly estimate TS energies using different ML models. The tree-based models combining only one acidity descriptor and one topology descriptor, trained on the random-selected data sets, could be used to predict TS energies, but they fail for new topologies. We demonstrate that it is necessary to take the linear regression models and at least four descriptors (e.g., adsorption enthalpy of ammonia, framework density, diameter of the largest included sphere, and accessible volume of zeolites) to achieve an accurate energy prediction on new topologies. The consistency of the coefficients and the robustness of the model are validated. This work thus reveals that no single topology descriptor could quantitatively capture the confinement effect of zeolites, and the proposed scheme in this work combining simple descriptors and ML models could be employed for the rational design of zeolite catalysts. Zeolite ; Topology (electrical circuits) ; Enthalpy ; Thermodynamics ; Machine learning ; Chemistry ; Computer science ; Materials science ; Mathematics ; Catalysis ; Physics ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Zeolite Catalysis and Synthesis ; Machine Learning in Materials Science
Authors: Hanqing Xie | Kaifeng Liu | Zhengqiang Li | Zhi Wang | Chunyu Wang | Fengxi Li | Weiwei Han | Lei Wang
Source: JACS Au (article, 2024-11-21)
Field: Chemistry — Organic Chemistry

Machine-Learning-Aided Engineering Hemoglobin as Carbene Transferase for Catalyzing Enantioselective Olefin Cyclopropanation In this study, we developed a machine-learning-aided protein design strategy for engineering <i>Vitreoscilla</i> hemoglobin (VHb) as carbene transferase. A Natural Language Processing (NLP) model was used for the first time to construct an algorithm (EESP, enzyme enantioselectivity score predictor) and predict the enantioselectivity of VHb. We identified critical amino acid residue sites by molecular docking and established a simplified mutation library by site-saturated mutagenesis. Based on the simplified mutant library, the trianed EESP scored 160,000 virtual mutants, and 15 predicted high-score mutants were chosen for experimental validation. Among these mutants, VHb-WK (Y29W/P54K) demonstrated the highest diastereoselectivity and enantioselectivity of carbene transferase for the olefin cyclopropanation in aqueous conditions. Subsequently, molecular dynamics simulations were performed to explore the interaction between protein and substrates, finding that the high enantioselectivity of VHb-WK stems from the interactions of R47, Q53, and K84, which narrows the entrance of the enzyme's pocket, favoring the restriction of the formation of reaction intermediates. Integrating the NLP model and enzyme modification offers significant advantages by reducing economic costs and workloads associated with the protein engineering process. Cyclopropanation ; Enantioselective synthesis ; Olefin fiber ; Carbene ; Chemistry ; Transferase ; Combinatorial chemistry ; Organic chemistry ; Catalysis ; Enzyme Cyclopropane Reaction Mechanisms ; Asymmetric Hydrogenation and Catalysis
Authors: Azamat Mukhametov | Insaf Samikov | Elena A. Korznikova | Andrey A. Kistanov
Source: Molecules (article, 2024-08-10)
Field: Materials Science — Materials Chemistry

Density Functional Theory-Based Indicators to Estimate the Corrosion Potentials of Zinc Alloys in Chlorine-, Oxidizing-, and Sulfur-Harsh Environments Nowadays, biodegradable metals and alloys, as well as their corrosion behavior, are of particular interest. The corrosion process of metals and alloys under various harsh conditions can be studied via the investigation of corrosion atom adsorption on metal surfaces. This can be performed using density functional theory-based simulations. Importantly, comprehensive analytical data obtained in simulations including parameters such as adsorption energy, the amount of charge transferred, atomic coordinates, etc., can be utilized in machine learning models to predict corrosion behavior, adsorption ability, catalytic activity, etc., of metals and alloys. In this work, data on the corrosion indicators of Zn surfaces in Cl-, S-, and O-rich harsh environments are collected. A dataset containing adsorption height, adsorption energy, partial density of states, work function values, and electronic charges of individual atoms is presented. In addition, based on these corrosion descriptors, it is found that a Cl-rich environment is less harmful for different Zn surfaces compared to an O-rich environment, and more harmful compared to a S-rich environment. Corrosion ; Adsorption ; Density functional theory ; Oxidizing agent ; Metal ; Materials science ; Chlorine ; Zinc ; Atom (system on chip) ; Sulfur ; Inorganic chemistry ; Catalysis ; Work (physics) ; Chemical physics ; Metallurgy ; Chemical engineering ; Chemistry ; Computational chemistry ; Thermodynamics ; Physical chemistry ; Organic chemistry ; Computer science Corrosion Behavior and Inhibition ; Gas Sensing Nanomaterials and Sensors ; Machine Learning in Materials Science
Authors: Chengchun Liu | Ruqiang Zou | Fanyang Mo
Source: Analytical Chemistry (article, 2024-09-19)
Field: Materials Science — Materials Chemistry

Infrared Spectra Prediction for Functional Group Region Utilizing a Machine Learning Approach with Structural Neighboring Mechanism Infrared (IR) spectroscopy is a pivotal technique in chemical research for elucidating molecular structures and dynamics through vibrational and rotational transitions. However, the intricate molecular fingerprints characterized by unique vibrational and rotational patterns present substantial analytical challenges. Here, we present a machine learning approach employing a structural neighboring mechanism tailored to enhance the prediction and interpretation of infrared spectra. Our model distinguishes itself by honing in on chemical information proximal to functional groups, thereby significantly bolstering the accuracy, robustness, and interpretability of spectral predictions. This method not only demystifies the correlations between infrared spectral features and molecular structures but also offers a scalable and efficient paradigm for dissecting complex molecular interactions. Chemistry ; Mechanism (biology) ; Group (periodic table) ; Infrared ; Infrared spectroscopy ; Pattern recognition (psychology) ; Artificial intelligence ; Biological system ; Computational chemistry ; Optics ; Organic chemistry Machine Learning in Materials Science ; Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies
Authors: Balaji Mohan | Abdullah S. AlRamadan
Source: Fuel (article, 2024-10-30)
Field: Materials Science — Materials Chemistry

Estimation of cetane number using machine learning Cetane number ; Estimation ; Computer science ; Mathematics ; Artificial intelligence ; Pattern recognition (psychology) ; Biological system ; Chemistry ; Engineering ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Analytical Chemistry and Chromatography