jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Huibin Wang | Zehui Wang | Ying Qian
Source:  (article, 2025-06-30)
Field: Materials Science — Materials Chemistry

Modeling Diverse and Feasible Retrosynthesis via a Layer-Wise Conditional Variational Transformer Identifying reactants capable of directly synthesizing a target molecule, also known as single-step retrosynthesis, is a fundamental problem in organic chemistry. Recently, deep-learning-based methods have shown great promise by framing this problem as a sequence-to-sequence translation task using Transformer architectures. However, generating diverse sets of chemically feasible reactants remains a challenge due to the multi-modal nature of chemical reactions. To address this issue, we propose R-LCVAT, a translation-based retrosynthesis model that introduces layer-wise latent variables into a Transformer within the Conditional Variational Autoencoder framework. R-LCVAT learns latent variables from the hidden states of products (as molecular graphs) and reactants (as SMILES strings) in each encoder layer, effectively capturing the multi-modal distribution of reactions. These latent variables are then fused into the corresponding decoder layers to generate diverse reactant SMILES strings. The layer-wise fusion architecture additionally captures the long-range correlations between molecular transformations throughout the reaction process, ensuring the validity and feasibility of the generated reactants. Experimental results on two public chemical reaction datasets demonstrate that R-LCVAT achieves state-of-the-art performance in the template-free retrosynthesis. Autoencoder ; Transformer ; Retrosynthetic analysis ; Encoder ; Framing (construction) ; Latent variable ; Conditional probability ; Representation (politics) Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Asymmetric Hydrogenation and Catalysis
Authors: Karsten Duch (14230751) | Markus Illner (5464064) | Jens-Uwe Repke (2271700)
Source: Figshare (article, 2025-06-05)
Field: Chemistry — Organic Chemistry

Development of an Automated High-Throughput ScreeningPlatform for the Dynamic Phase Separation Analysis of MicroemulsionSystems with AI Image Recognition Increasing efforts are undertaken to develop new sustainable production processes, and homogeneous catalysis offers many advantages regarding selectivity and energy efficiency in new chemical production routes. A major factor often limiting the application of homogeneous catalysis is the retention of valuable catalysts. One promising option to introduce superior reaction performance and catalyst recovery in organic reactions is the use of water-soluble catalysts in aqueous reaction media with surfactants. However, these surfactant-based microemulsion systems (MES) exhibit a complex phase separation behavior that is dependent on various parameters such as temperature and component concentrations, while its prediction is currently not possible due to the complex thermodynamics. The characterization of the phase behavior hence requires extensive and time-consuming experimental investigation due to a lack of fundamental modeling approaches. To facilitate the acquisition of experimental data, this contribution presents the development of a high-throughput screening platform for dynamic phase separation analysis with an automated experimental procedure, AI analysis of separation images, and automated result data handling. The platform enables a fast characterization of MES separation behavior, which is required for process development and operation. The functionalities of the screening platform are demonstrated in a case study for the hydroformylation of decene. The image detection is performed with a Mask R-CNN model achieving a ±1.5% accuracy in phase height detection with a classification confidence threshold of 96%. The new setup enables a fast evaluation of over 722 measurement runs each with a different combination of separation temperature and mixture composition that only required at total of 38 manual dosing steps. The gathered data is also used to derive a correlation for a soft-sensor with interpretable machine learning, enabling online insights into otherwise inaccessible process variables in an MES plant and enabling its operability. Microemulsion ; Hydroformylation ; Phase (matter) ; Process (computing) ; Characterization (materials science) ; Separation process ; Separation (statistics) ; Homogeneous Surfactants and Colloidal Systems ; Pickering emulsions and particle stabilization ; Advanced Polymer Synthesis and Characterization
Authors: Xiaoming Mao
Source:  (article, 2025-06-18)
Field: Materials Science — Materials Chemistry

Training all mechanical neural networks for task learning through in situ backpropagation Recent advances unveiled physical neural networks as promising machine learning platforms, offering faster and more energy-efficient information processing. Compared with extensively-studied optical neural networks, the development of mechanical neural networks remains nascent and faces significant challenges, including heavy computational demands and learning with approximate gradients. Here, we introduce the mechanical analogue of in situ backpropagation to enable highly efficient training of mechanical neural networks. We theoretically prove that the exact gradient can be obtained locally, enabling learning through the immediate vicinity, and we experimentally demonstrate this backpropagation to obtain gradient with high precision. With the gradient information, we showcase the successful training of networks in simulations for behavior learning and machine learning tasks, achieving high accuracy in experiments of regression and classification. Furthermore, we present the retrainability of networks involving task-switching and damage, demonstrating the resilience. Our findings, which integrate the theory for training mechanical neural networks and experimental and numerical validations, pave the way for mechanical machine learning hardware and autonomous self-learning material systems. Backpropagation ; Artificial neural network ; Task (project management) ; Training (meteorology) ; Deep learning ; Types of artificial neural networks Machine Learning in Materials Science ; Neural Networks and Reservoir Computing ; Mechanical and Optical Resonators
Authors: Marco Eckhoff (6751847) | Markus Reiher (1282191)
Source: Figshare (article, 2025-09-23)
Field: Materials Science — Materials Chemistry

Lifelong MachineLearning Potentials for ChemicalReaction Network Explorations Recent developments in computational chemistry facilitate the automated quantum chemical exploration of chemical reaction networks for the in-silico prediction of synthesis pathways, yield, and selectivity. However, the underlying quantum chemical energy calculations require vast computational resources, limiting these explorations severely in practice. Machine learning potentials (MLPs) offer a solution to increase computational efficiency, while retaining the accuracy of reliable first-principles data used for their training. Unfortunately, MLPs will be limited in their generalization ability within chemical (reaction) space, if the underlying training data are not representative for a given application. Within the framework of automated reaction network exploration, where new reactants or reagents composed of any elements from the periodic table can be introduced, this lack of generalizability will be the rule rather than the exception. Here, we therefore evaluate the benefits of the lifelong MLP concept in this context. Lifelong MLPs push their adaptability by efficient continual learning of additional data. We propose an improved learning algorithm for lifelong adaptive data selection yielding efficient integration of new data while previous expertise is preserved. In this way, we can reach chemical accuracy in reaction search trials. Lifelong learning ; Generalizability theory ; Quantum chemical ; Generalization ; Adaptability ; Limiting ; Artificial neural network ; Incremental learning Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Asymmetric Hydrogenation and Catalysis
Authors: Revista, Zen | CHEMISTRY, 10
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2025-11-27)
Field: Materials Science — Materials Chemistry

Quantum Accuracy at DFT Cost: A Machine-Learned Hybrid Approach to Electron Correlation Electron correlation is a fundamental challenge in quantum chemistry, dictating the accuracy of theoretical predictions for molecular properties and chemical reactions. While high-level wavefunction methods like coupled-cluster theory with singles, doubles, and perturbative triples (CCSD(T)) offer near chemical accuracy, their prohibitive computational scaling (typically O(Ntextasciicircum7), where N is the number of basis functions) limits their applicability to small systems. Density Functional Theory (DFT), on the other hand, offers a more favorable scaling (O(Ntextasciicircum3) to O(Ntextasciicircum4)) but often struggles with strong correlation, dispersion interactions, and accurate thermochemistry due to approximations in the exchange-correlation functional. This paper introduces a novel machine-learned hybrid approach designed to bridge this accuracy-cost gap. By leveraging the power of machine learning, we aim to augment the computational efficiency of DFT with the high accuracy characteristic of advanced quantum mechanical methods. The methodology involves training machine learning models on a diverse dataset of systems, where reference high-accuracy electron correlation energies (from CCSD(T) or similar) are learned as a function of local chemical descriptors derived from DFT calculations. This machine-learned correction is then applied to a base DFT calculation, effectively yielding quantum-level accuracy at a computational cost that remains comparable to standard DFT. We present a framework for data generation, feature engineering, model selection, and validation using benchmark chemical systems, demonstrating significant improvements in predictive power for reaction energies, barrier heights, and non-covalent interactions while maintaining practical scalability. This hybrid approach represents a significant step towards enabling accurate and efficient quantum chemical simulations for a broader range of complex chemical and materials science problems. Density functional theory ; Thermochemistry ; Benchmark (surveying) ; Scaling ; Quantum chemical ; Wave function ; Hybrid functional ; Quantum ; Range (aeronautics) ; Function (biology) Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Advanced Physical and Chemical Molecular Interactions
Authors: Z.J. Zhang | Hongzhou Song | Yinlin Ji | Yan Cui | Xiang Li | Zili Zhang | Ziming Cai | Jie Zhang | Yunyi Wu | Huanxin Li | Bing Luo
Source: Microstructures (article, 2025-03-18)
Field: Materials Science — Electronic, Optical and Magnetic Materials

MELRSNet for accelerating the exploration of novel ultrawide bandgap semiconductors Ultrawide bandgap (UWBG) semiconductors, with bandgaps exceeding 3.4 eV of gallium nitride, offer the potential to overcome the limitations of conventional semiconductors and drive innovations in electronics and photovoltaics. However, discovering such materials remains a huge challenge due to the prohibitive cost of trial-and-error-based experiments and the complexity of cutting-edge quantum mechanical approaches. Here, we develop the Multistage Ensemble Learning Rapid Screening Network (MELRSNet), a data-driven hierarchical machine learning framework integrated with high-throughput first-principles calculations, designed for swift identification of UWBG semiconductors. Trained on the Materials Project dataset, MELRSNet utilizes elemental and structural features to classify, regress, and validate potential candidates. Its efficacy is underscored by the accurate prediction of bandgaps in UWBG oxides and the revelation of metric-bandgap relationships, aligning closely with first-principles calculations. Furthermore, MELRSNet's reliability is bolstered through the identification of eight novel ternary oxide compounds, derived from monoclinic hafnium oxide crystals, exhibiting high stability, desirable band gaps, and strong ultraviolet light absorption, marking them promising candidates for lab synthesis and subsequent applications. MELRSNet not only streamlines the discovery of UWBG semiconductors but also paves the way for high-throughput computational screening of other functional materials. Semiconductor ; Band gap ; Optoelectronics ; Materials science ; Engineering physics ; Physics Ga2O3 and related materials ; Semiconductor materials and devices ; Electronic and Structural Properties of Oxides
Authors: Yifan Chen | Y. Z. Zhang | Xinxin Zhang | Shuangbao Ma
Source: Measurement (article, 2025-12-19)
Field: Materials Science — Materials Chemistry

Machine learning-driven prediction of magnetic parameters in magnetic nanoparticles Coercivity ; Remanence ; Magnetization ; Hysteresis ; Saturation (graph theory) ; Magnetic moment ; Magnetic hysteresis ; Nanoparticle ; Magnetic domain Magnetic Properties and Synthesis of Ferrites ; Nanoparticle-Based Drug Delivery ; Machine Learning in Materials Science
Authors: Karsten Duch | Markus Illner | Jens-Uwe Repke
Source: DepositOnce (article, 2025-06-05)
Field: Chemistry — Organic Chemistry

Development of an automated high-throughput screening platform for the dynamic phase separation analysis of microemulsion systems with AI image recognition Increasing efforts are undertaken to develop new sustainable production processes, and homogeneous catalysis offers many advantages regarding selectivity and energy efficiency in new chemical production routes. A major factor often limiting the application of homogeneous catalysis is the retention of valuable catalysts. One promising option to introduce superior reaction performance and catalyst recovery in organic reactions is the use of water-soluble catalysts in aqueous reaction media with surfactants. However, these surfactant-based microemulsion systems (MES) exhibit a complex phase separation behavior that is dependent on various parameters such as temperature and component concentrations, while its prediction is currently not possible due to the complex thermodynamics. The characterization of the phase behavior hence requires extensive and time-consuming experimental investigation due to a lack of fundamental modeling approaches. To facilitate the acquisition of experimental data, this contribution presents the development of a high-throughput screening platform for dynamic phase separation analysis with an automated experimental procedure, AI analysis of separation images, and automated result data handling. The platform enables a fast characterization of MES separation behavior, which is required for process development and operation. The functionalities of the screening platform are demonstrated in a case study for the hydroformylation of decene. The image detection is performed with a Mask R-CNN model achieving a ±1.5% accuracy in phase height detection with a classification confidence threshold of 96%. The new setup enables a fast evaluation of over 722 measurement runs each with a different combination of separation temperature and mixture composition that only required at total of 38 manual dosing steps. The gathered data is also used to derive a correlation for a soft-sensor with interpretable machine learning, enabling online insights into otherwise inaccessible process variables in an MES plant and enabling its operability. Microemulsion ; Hydroformylation ; Phase (matter) ; Process (computing) ; Characterization (materials science) ; Separation process ; Separation (statistics) ; Homogeneous Surfactants and Colloidal Systems ; Pickering emulsions and particle stabilization ; Advanced Polymer Synthesis and Characterization
Authors: Hannah
N. Turney (22510977) | Micaela Matta (1567393)
Source: Figshare (article, 2025-10-28)
Field: Materials Science — Materials Chemistry

Atomistic PolymerModeling: Recent Advances and Challengesin Building and Parametrization Workflows Synthetic polymers are a broad and versatile class of soft materials covering a large chemical space. “Computational microscopy” approaches such as atomistic molecular dynamics (MD) simulations are an effective tool to validate and rationalize experimental data for structure–property characterization. The predictive quality of MD simulations and the properties derived from them are primarily driven by the accuracy and relevance of the force field used to represent the system. While biomolecular simulation (nucleic acids, proteins) workflows benefit from dedicated toolkits and domain-specific force fields, the modeling of synthetic polymers has not progressed to the same extent. This perspective will discuss recent efforts to improve system building and parametrization workflows for synthetic polymers, and the unique challenges differentiating them from biopolymers. We will outline shortcomings in established workflows, review best practices for FAIR polymer simulations, and highlight new tools/workflows leveraging cheminformatics, direct chemical perception, and neural networks. Workflow ; Parametrization (atmospheric modeling) ; Relevance (law) ; Field (mathematics) ; Perspective (graphical) ; Class (philosophy) Machine Learning in Materials Science ; Force Microscopy Techniques and Applications ; Block Copolymer Self-Assembly
Authors: Tim Kircher | Martin Votsmeier
Source: ChemRxiv (preprint, 2025-02-27)
Field: Materials Science — Materials Chemistry

Machine learning surrogate models for mechanistic kinetics: Embedding atom balance AND positivity Multiscale simulations of reactive flows are critical in many fields. However, their application is often hindered by the high computational cost of solving detailed chemical kinetics. Recent advances in surrogate models for reactive chemistry offer promising speedups, but ensuring that these models remain physically consistent remains challenging. In particular, machine learning models for chemical kinetics must enforce atom balance and guarantee the positivity of predicted concentrations. Here, we introduce a positivity preserving projection that simultaneously enforces both constraints. We demonstrate this approach using an example from atmospheric chemistry and an example from heterogeneous catalysis. In both cases, the positivity preserving projection yields exclusively positive model predictions conforming to the atom balance, without reducing the overall accuracy of the model. Kinetics ; Embedding ; Balance (ability) ; Atom (system on chip) ; Computer science ; Surrogate model ; Chemistry ; Artificial intelligence ; Physics ; Machine learning ; Psychology ; Classical mechanics ; Neuroscience ; Parallel computing Machine Learning in Materials Science
Authors: Rahul
Kumar Sharma (6257450) | Harpriya Minhas (13220737) | Biswarup Pathak (1491820)
Source: Figshare (article, 2025-07-03)
Field: Materials Science — Materials Chemistry

Machine Learning-Guided Discovery of Alloy Nanoclusters:Steering Morphology-Based Activity and Selectivity Relationships inBifunctional Electrocatalysts Nanoclusters designed with atomic precision are poised to transform next-generation electrode materials for energy devices due to their exceptional performance. However, traditional computational studies often focus solely on individual nanoclusters, neglecting the impact of structurally diverse, low-energy isomers that coexist in a sample. Herein, we present a data-driven approach to screen late-transition metal-based core–shell nanoclusters for bifunctional electrocatalysis. Utilizing geometric and electronic properties, we establish morphology-based relationships for activity and selectivity, emphasizing the critical role of structural diversity in fuel cell applications. We identify the unique single-cluster catalyst identity of M<sub>55</sub> nanoclusters, where intermediate adsorption is primarily governed by the constituent metals’ electronic and elemental characteristics. Our findings identified the Au<sub>48</sub>W<sub>7</sub> nanocluster as the most efficient electrocatalyst, exhibiting the lowest bifunctional overpotential of 0.76 V, with η<sub>OER</sub> = 0.33 V and η<sub>ORR</sub> = 0.43 V, highlighting its outstanding catalytic performance at the nano regime. Guided by the Sabatier principle, we highlight the limitations of conventional numerical methods and reshape the activity volcano, transitioning from RuO<sub>2</sub> and Pt to Au/Ag-based nanoclusters. Furthermore, the trained ML model enables the screening of electrocatalysts for two- and four-electron pathways, steering selectivity between H<sub>2</sub>O<sub>2</sub> and H<sub>2</sub>O formation. This study provides intuitive guidelines for designing efficient bifunctional electrocatalysts, redefining activity volcanoes, and modulates selectivity in nanocluster alloys. Bifunctional ; Nanoclusters ; Overpotential ; Selectivity ; Catalysis ; Electrocatalyst ; Alloy ; Nanoparticle Machine Learning in Materials Science ; Nanocluster Synthesis and Applications ; Electrocatalysts for Energy Conversion
Authors: Ouarda El Hani | Khalid Digua | Aziz Amine
Source: Research Square (preprint, 2025-10-09)
Field: Chemistry — Analytical Chemistry

Generic strategy for high-performance bio-based imprinted membranes free of non-specific adsorption: machine learning-assisted sensing Molecularly imprinted polymer ; Limiting ; Membrane ; Carboxymethyl cellulose ; Biopolymer ; Polymer ; Template Analytical chemistry methods development ; Microfluidic and Capillary Electrophoresis Applications ; Analytical Chemistry and Chromatography
Authors: Z. Y. Zhang | Lei Tao null | Shixuan Du
Source: Communications in Computational Chemistry (article, 2025-04-30)
Field: Materials Science — Materials Chemistry

First Principles Calculations on Atomistic Mechanisms of Surface/Interface-Induced Properties and Database Construction Surfaces and interfaces play a critical role in exploring novel chemical and physical properties because the interactions between surface and adsorbents, as well as interfacial interactions, can be finely tuned by the interface configurations at the atomic level across a wide range, from strong covalent and ionic bonds to weak hydrogen bonds and van der Waals interactions. These interactions enable precise control over molecular adsorption, self-assembly, and activation, facilitating the design of nanostructures and modulation of novel properties. Recent developments in computational technologies, including high-throughput screening and machine learning, have significantly accelerated the discovery of functional materials. This review highlights theoretical progress in manipulating the physical and chemical properties of organic molecules at surfaces and interfaces, with a focus on controlling molecular configuration and magnetism, selective activation, on-surface synthesis, and interfacial engineering of 2D materials. Our goal is to understand the key factors influencing the physical and chemical properties at surfaces and interfaces, with a particular focus on theoretical insights and computational approaches. Interface (matter) ; Computer science ; Database ; Materials science ; Parallel computing Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; Surface Chemistry and Catalysis
Authors: Abin Shakya | Bijaya B. Karki
Source:  (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Weakly Supervised Graph Neural Networks for Scalable 3D Phase Segmentation in Molecular Dynamics Simulations Segmentation ; Artificial neural network ; Graph ; Pattern recognition (psychology) ; Scalability ; Phase (matter) Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Quantum many-body systems