jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Adrian Krzyzanowski (5855381) | Stephen D. Pickett (1523533) | Peter Pogány (2092990)
Source: Figshare (article, 2025-05-01)
Field: Materials Science — Materials Chemistry

Exploring BERTfor Reaction Yield Prediction: Evaluatingthe Impact of Tokenization, Molecular Representation, and PretrainingData Augmentation Predicting reaction yields in synthetic chemistry remains a significant challenge. This study systematically evaluates the impact of tokenization, molecular representation, pretraining data, and adversarial training on a BERT-based model for yield prediction of Buchwald-Hartwig and Suzuki-Miyaura coupling reactions using publicly available HTE data sets. We demonstrate that molecular representation choice (SMILES, DeepSMILES, SELFIES, Morgan fingerprint-based notation, IUPAC names) has minimal impact on model performance, while typically BPE and SentencePiece tokenization outperform other methods. WordPiece is strongly discouraged for SELFIES and fingerprint-based notation. Furthermore, pretraining with relatively small data sets (<100 K reactions) achieves comparable performance to larger data sets containing millions of examples. The use of artificially generated domain-specific pretraining data is proposed. The artificially generated sets prove to be a good surrogate for the reaction schemes extracted from reaction data sets such as Pistachio or Reaxys. The best performance was observed for hybrid pretraining sets combining the real and the domain-specific, artificial data. Finally, we show that a novel adversarial training approach, perturbing input embeddings dynamically, improves model robustness and generalizability for yield and reaction success prediction. These findings provide valuable insights for developing robust and practical machine learning models for yield prediction in synthetic chemistry. GSK’s BERT training code base is made available to the community with this work. Generalizability theory ; Yield (engineering) ; Robustness (evolution) ; Training set ; Synthetic data ; Representation (politics) ; Boosting (machine learning) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Asymmetric Hydrogenation and Catalysis
Authors: Yuyang Wang | Jian Liu
Source: The Innovation Informatics (article, 2025-11-27)
Field: Materials Science — Materials Chemistry

Optimization of neural network wave functions in variational Monte Carlo &lt;p&gt;The central challenge in materials science and quantum chemistry is solving the electronic Schrodinger equation, complicated by the curse of dimensionality as system size grows. Neural network-based variational Monte Carlo (NN-VMC) offers a promising path forward, achieving unprecedented accuracy at far lower cost than traditional high-level methods. However, the flexibility of neural network wavefunctions introduces a bottleneck: their optimization is high-dimensional, stochastic, and non-convex. This Perspective reviews the evolution of optimization methods in NN-VMC, from stochastic reconfiguration to approximate second-order algorithms and geometric insights. We highlight key challenges currently limiting scalability and efficiency, and outline future opportunities to advance the field. With continued progress in optimization, neural network techniques, and computer architectures, NN-VMC can tackle larger and more complex quantum systems and move from trailing experiments to guiding them.&lt;/p&gt; Curse of dimensionality ; Artificial neural network ; Flexibility (engineering) ; Monte Carlo method ; Wave function ; Quantum ; Quantum Monte Carlo ; Stochastic neural network Machine Learning in Materials Science ; Quantum Computing Algorithms and Architecture ; Quantum many-body systems
Authors: Jiayi Li | Yahan Sun | Chaopeng Ma | Z. H. Zhang | Jiake Wang | Xiangmei Li | Selwyn Gao
Source:  (article, 2025-11-29)
Field: Materials Science — Materials Chemistry

MolLoRA: Low-Rank Adaptation of Large Language Models for Molecular Property Prediction Molecular property prediction is essential for drug discovery and materials science, yet most high-performing methods rely on molecular foundation models pretrained on large chemical corpora, demanding substantial domain-specific resources. We explore a different paradigm: adapting a general-purpose large language model (LLM)—without any molecular pretraining—directly to SMILES-based prediction tasks. We propose MolLoRA, a parameter-efficient framework that fine-tunes the Qwen3-0.6B model using Low-Rank Adaptation (LoRA), updating only 1 % of parameters. Evaluated on five MoleculeNet datasets against strong baselines including FPGNN, GAT, and the molecular transformer MolFormer, MolLoRA matches or surpasses specialized models on multiple datasets despite its lack of chemical pretraining. This work demonstrates that efficiently adapted general-purpose LLMs can rival or exceed domain-specific architectures, offering a scalable and resource-efficient alternative for molecular machine learning. Property (philosophy) ; Scalability ; Adaptation (eye) ; Language model ; Domain adaptation ; Transformer Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Srivathsan Badrinarayanan (20455734) | Rishikesh Magar (9636817) | Akshay Antony (17943907) | Radheesh Sharma Meda (17328605) | Amir Barati Farimani (7822376)
Source: Figshare (article, 2025-08-28)
Field: Chemistry — Inorganic Chemistry

MOFGPT: GenerativeDesign of Metal–OrganicFrameworks using Language Models The discovery of Metal–Organic Frameworks (MOFs) with application-specific properties remains a central challenge in materials chemistry, owing to the immense size and complexity of their structural design space. Conventional computational screening techniques such as molecular simulations and density functional theory (DFT), while accurate, are computationally prohibitive at scale. Machine learning offers an exciting alternative by leveraging data-driven approaches to accelerate materials discovery. The complexity of MOFs, with their extended periodic structures and diverse topologies, creates both opportunities and challenges for generative modeling approaches. To address these challenges, we present a reinforcement learning-enhanced, transformer-based framework for the de novo design of MOFs. Central to our approach is MOFid, a chemically informed string representation encoding both connectivity and topology, enabling scalable generative modeling. Our pipeline comprises three components: (1) a generative GPT model trained on MOFid sequences, (2) MOFormer, a transformer-based property predictor, and (3) a reinforcement learning (RL) module that optimizes generated candidates via property-guided reward functions. By integrating property feedback into sequence generation, our method drives the model toward synthesizable, topologically valid MOFs with desired functional attributes. This work demonstrates the potential of large language models, when coupled with reinforcement learning, to accelerate inverse design in reticular chemistry and unlock new frontiers in computational MOF discovery. Generative grammar ; Reinforcement learning ; String (physics) ; Generative Design ; Generative model ; Scalability ; Encoding (memory) Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Nanocluster Synthesis and Applications
Authors: Santhosh, Archa
Source: OpenHSU (article, 2025-11-06)
Field: Materials Science — Materials Chemistry

Ab-initio and machine-learning accelerated atomistic simulations for TiFe hydrogen storage applications In this thesis, atomistic modeling and simulation methods are thoroughly employed to understand and explore the mechanism of oxidation and subsequent hydrogenation of the TiFe intermetallic compound. Parallel to the material investigations, the computational methodology has also advanced with the incorporation of machine-learning techniques. TiFe is a commercially utilized stationary hydrogen storage material with attractive properties for practical applications. The formation of oxide-films on the TiFe surface is a major concern limiting its wide application. The first chapter of this work constructs and characterizes the oxygen-exposed TiFe surface with the use of global-optimization techniques and ab-initio methods. The energetics and dynamics of early-oxidation of the material surface is studied at different temperatures and the activation barriers for hydrogen penetration through the oxide layers are evaluated with a focus on the activation mechanisms. Structure fingerprint tools employed are able to identify metal oxide structural motifs consistent with experimental findings. This work also produces valuable mechanistic insights for the activation of TiFe. Progressively, the influence of transition metal dopants, V, Cr, Mn and Ni, substituting Fe in TiFe is investigated with a focus on their relative preference to oxidize and their involvement in the surface oxidation. Their suitability to promote hydrogenation is evaluated and characterized by assessing the change in surface chemistry and through a comparison with the pristine TiFe surface. Accurate atomistic simulations are burdened by heavy computational cost and limited to small systems. Hence, in the last chapter of this thesis, an extensive machine-learning interatomic model with ab-initio accuracy is developed for Ti-Fe-H system utilizing the first-principles data generated throughout this work. This deep neural network model provides upto 1000 times faster computation and scales linearly with the size of the system bridging the gap between ab-initio and large-scale atomistic simulations. Intermetallic ; Oxide ; Hydrogen storage ; Hydrogen ; Transition metal ; Work (physics) ; Limiting Hydrogen Storage and Materials ; Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion
Authors: Serhii Hospodariuk | Andrii SAMILA
Source: Security of Infocommunication Systems and Internet of Things (article, 2025-12-30)
Field: Materials Science — Materials Chemistry

Review of Nuclear Quadrupole Resonance Signal Processing and Analysis Methods This article presents a structured and comprehensive review of signal processing and analysis methods used in nuclear quadrupole resonance (NQR), with a focus on their performance under real-world operating conditions, where signals are typically weak, noisy, and highly sensitive to environmental influences. Classical approaches, including Fourier analysis, wavelet transforms, adaptive and matched filtering, as well as high-resolution spectral estimation techniques, are considered with respect to their ability to enhance signal detectability and frequency resolution. Particular attention is devoted to modern machine learning-based methods, including deep neural networks and hybrid architectures, which enable automatic feature extraction and demonstrate high robustness in low signal-to-noise ratio conditions. Each group of methods is systematically analyzed according to a set of practical criteria, including noise immunity, computational complexity, adaptability to nonstationary environments, sensitivity to parameter variations, and suitability for implementation in portable or resource-constrained NQR systems. The review highlights key challenges in current research, such as the lack of standardized and representative training datasets, limited interpretability of learning-based models, and difficulties associated with deploying computationally intensive algorithms in compact instrumentation. It is shown that no single signal processing approach provides a universally optimal solution across all operating scenarios. Instead, the most promising direction lies in the development of hybrid signal processing frameworks that combine classical preprocessing techniques with data-driven machine learning models. The presented analysis provides a methodological basis for the informed selection and design of robust, adaptive NQR systems intended for practical applications in security screening, material characterization, and chemical substance identification under diverse and challenging conditions. Signal processing ; Preprocessor ; Interpretability ; Robustness (evolution) ; Artificial neural network ; Wavelet ; Multidimensional signal processing ; Noise (video) ; Feature extraction Solid-state spectroscopy and crystallography ; Advanced NMR Techniques and Applications ; Electron Spin Resonance Studies
Authors: Jacob Wafula | Clint Steed | Gerrit Ter Haar
Source: Springer Link (Chiba Institute of Technology) (article, 2025-11-25)
Field: Materials Science — Materials Chemistry

Evaluating the performance of active learning models on selective laser melting data sampling Data acquisition in additive manufacturing, specifically selective laser melting, is always expensive and worsens when the material under study is also costly. To address this challenge, researchers use available design of experiment (DoE) tools. This marks a shift away from trial-and-error and one-factor-at-a-time approaches, which are ineffective and cause the number of required experiments to grow exponentially as the number of parameters increases. However, the traditional design of experiments struggles in analysing complex, multi-parameter, and noisy systems, inherent characteristics of selective laser melting data. Active machine learning can excel in this limited data and sophisticated field. This study evaluates the performance of active machine learning models based on neural networks and Gaussian process regression (GPR) with a D-optimal design for predicting Ti-5-5-5-3 and Beta 21S samples. This study demonstrates the ability of both active learning methods to reduce data required with improved predictability, with GPR outperforming the others. These results demonstrate the potential of GPR for effective SLM experimentation and emphasize the necessity of improving active learning based on neural networks to increase experimental accuracy with less data. Active learning (machine learning) ; Artificial neural network ; Kriging ; Process (computing) ; Gaussian process ; Sampling (signal processing) ; Design of experiments ; Data acquisition ; Experimental data Machine Learning in Materials Science ; Additive Manufacturing Materials and Processes ; Gaussian Processes and Bayesian Inference
Authors: Jacob Wafula | Clint Alex Steed
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Evaluating The Performance of Active Learning Models on Selective Laser Melting Data Sampling Sampling (signal processing) ; Selective laser melting ; Active learning (machine learning) ; Computer science ; Laser ; Machine learning ; Artificial intelligence ; Materials science ; Physics ; Optics Crystallization and Solubility Studies ; Forecasting Techniques and Applications ; Mineral Processing and Grinding
Authors: Tsolak Ghukasyan | Vahagn Altunyan | Aram Bughdaryan | Tigran Aghajanyan | Khachik Smbatyan | Garegin A. Papoian | Garik Petrosyan
Source: Scientific Reports (article, 2025-02-27)
Field: Materials Science — Materials Chemistry

Smart distributed data factory volunteer computing platform for active learning-driven molecular data acquisition This paper presents the smart distributed data factory (SDDF), an AI-driven distributed computing platform designed to address challenges in drug discovery by creating comprehensive datasets of molecular conformations and their properties. SDDF uses volunteer computing, leveraging the processing power of personal computers worldwide to accelerate quantum chemistry (DFT) calculations. To tackle the vast chemical space and limited high-quality data, SDDF employs an ensemble of machine learning (ML) models to predict molecular properties and selectively choose the most challenging data points for further DFT calculations. The platform also generates new molecular conformations using molecular dynamics with the forces derived from these models. SDDF makes several contributions: the volunteer computing platform for DFT calculations; an active learning framework for constructing a dataset of molecular conformations; a large public dataset of diverse ENAMINE molecules with calculated energies; an ensemble of ML models for accurate energy prediction. The energy dataset was generated to validate the SDDF approach of reducing the need for extensive calculations. With its strict scaffold split, the dataset can be used for training and benchmarking energy models. By combining active learning, distributed computing, and quantum chemistry, SDDF offers a scalable, cost-effective solution for developing accurate molecular models and ultimately accelerating drug discovery. Computer science ; Scalability ; Benchmarking ; Molecular descriptor ; Chemical space ; Distributed Computing Environment ; Drug discovery ; Computational science ; Artificial intelligence ; Machine learning ; Distributed computing ; Database ; Chemistry ; Quantitative structure–activity relationship Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation ; Computational Drug Discovery Methods
Authors: Cheenepalli Nagarjuna | Peyala Dharmaiah | Babu Madavali | Murali Bissannagari | S. Shyamal | Weizong Bao | Soon‐Jik Hong | Jinxue Ding | Xufei Fang | Byungmin Ahn | Wenjun Lu | Binbin He
Source: Matter (article, 2025-12-01)
Field: Materials Science — Materials Chemistry

Entropy-guided design of thermoelectric properties in multi-component compounds Thermoelectric effect ; Thermoelectric materials ; Thermal ; Microscale chemistry ; Electronics ; Lattice (music) ; Electronic structure ; Scalability Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science ; Heusler alloys: electronic and magnetic properties
Authors: Patrizia Mazzeo | Lorenzo Cupellini | Benedetta Mennucci
Source: ChemRxiv (article, 2025-11-28)
Field: Materials Science — Materials Chemistry

Multiscale machine learning prediction of infrared spectra of solvated molecules Spectral line ; Infrared ; Molecular dynamics ; Representation (politics) ; Dipole ; Molecule ; Infrared spectroscopy Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Molecular spectroscopy and chirality
Authors: Veronica Enblom | Rebecca Clulow | Taejun Ha | Matthew Witman | Lauren Way | Sang M. Han | Paulo H. B. Brant Carvalho | Vitalie Stavila | Jin‐Yoo Suh | Martin Sahlberg | Julien O. Fadonougbo
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

On the Design of Room Temperature TiZrMnCrFeNi-Based High Entropy Hydrides Materials science ; Thermodynamics ; Physics Nuclear Materials and Properties ; Hydrogen Storage and Materials ; High Temperature Alloys and Creep
Authors: Peiwen Yun | Huadong Fu | Hongtao Zhang | Lei Jiang | Xingyu Xiao | Shuaicheng Zhu | Yujiao Jia | Changtai Li | Wenjin Yang | Jianxin Xie
Source: Scientific Data (article, 2025-12-11)
Field: Materials Science — Materials Chemistry

Mechanical and Electrical Properties dataset of Cu-Cr-X alloys generated with automated figure data extraction Extracting data from scientific literature and constructing machine learning-ready (ML-ready) datasets is crucial for data-driven materials discovery. However, material properties under various compositions and processing conditions are often summarized in figures, making manual data extraction time-consuming and labor-intensive. In this study, we propose an automated figure data extraction tool to rapidly generate datasets from figures in materials science literature on Cu-Cr-X alloys used in high-end lead frames, aiming to support the development of next-generation high-strength, high-conductivity copper alloys. We collected 251 figures containing mechanical and electrical performance data from 146 papers and automatically extracted 3,018 high-quality Cu-Cr-X data records. Each record includes article metadata and composition/processing information manually extracted from text, enabling construction of a domain-specific dataset linking alloy composition, processing, and properties. The dataset summarizes 20 microalloying elements and mechanical and electrical performance under different processing conditions. This tool can significantly improve the efficiency of large-scale data accumulation in materials science. Metadata ; Data extraction ; Extraction (chemistry) ; Information extraction ; Data processing ; Experimental data Machine Learning in Materials Science ; Additive Manufacturing Materials and Processes ; Microstructure and mechanical properties
Authors: Yang Wang
Source: Communications Chemistry (article, 2025-12-16)
Field: Chemistry — Organic Chemistry

Autodesmotic reactions for general strain energy evaluation in polycyclic aromatic nanocarbons Strain energy fundamentally shapes the structure, stability, and reactivity of π-conjugated nanocarbons, making its accurate quantification essential for rational molecular design. However, existing approaches rely on arbitrary reference choices, overlook critical π-energy balance, or demand extensive computations, limiting their reliability and scope. Here we introduce autodesmotic reactions, a general and efficient framework that maps any strained π-conjugated nanocarbon onto an operationally defined single-molecule reference while preserving molecular topology and ensuring proper π-energy balance. This reference resides within a virtual chemical space constructed from physically motivated models trained on planar benzenoid hydrocarbons. Benchmarking across diverse carbon nanobelts confirms the accuracy and robustness of the method, and applications to circulenes, helicenes, bowl-shaped hydrocarbons, nanotubes, and fullerenes demonstrate its broad versatility and reveal insightful strain-structure-property relationships. By resolving the fundamental and computational limitations of established methods, autodesmotic reactions provide a rigorous, general, and highly efficient route to strain energy evaluation, requiring only a single quantum chemical calculation per molecule. As a conceptual advance linking topology, π-energy, and strain, this framework lays a foundation for accelerated design of strained aromatic nanocarbons and offers a platform readily extensible via emerging machine-learning strategies. Limiting ; Robustness (evolution) ; Chemical space ; Fullerene ; Planar ; Quantum ; Oxide ; Quantum chemical ; Strain energy ; Extensibility Synthesis and Properties of Aromatic Compounds ; Fullerene Chemistry and Applications ; Carbon Nanotubes in Composites
Authors: Mia Nayeem | MA Hashem
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Chemistry — Analytical Chemistry

Spectroscopic Discrimination of Cooked Chicken Using Nir Data and Predictive Modeling Computer science ; Artificial intelligence ; Pattern recognition (psychology) ; Food science ; Chemistry Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Meat and Animal Product Quality
Authors: Zhanjie Liu | Yuchi Huo | Wanyi Chen | Siqi Zhan | Qian Li | Liqun Zhang | Lihong Cui | Jun Liu
Source: Computational Materials Science (article, 2025-09-02)
Field: Materials Science — Materials Chemistry

Predicting polymer properties based on wavelet transform and Transformer Wavelet transform ; Transformer ; Materials science ; Polymer ; Wavelet ; Computer science ; Artificial intelligence ; Composite material ; Engineering ; Electrical engineering ; Voltage Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Injection Molding Process and Properties