jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Kentaro Fuku | Takefumi Yoshida
Source: ChemRxiv (preprint, 2024-05-28)
Field: Chemistry — Analytical Chemistry

Unsupervised Raw IR Spectra Images Recognition: Toward Chemist-Like Chemical Structure Classification The recent advancements in artificial intelligence have greatly improved spectral data analysis. Here, we explored using unsupervised machine learning to classify chemical compounds based on IR spectrum images without relying on prior chemical knowledge. The research demonstrated the potential of machine learning in chemical classification by extracting IR spectral images from the SDBS database and converting them into 218196-dimensional vector data. The hierarchical clustering of 227 compounds revealed distinct main clusters (A-G), each with specific subclusters showing higher intra-cluster similarity. Despite some challenges, such as sensitivity to spectral deviations and difficulty distinguishing delicate chemical structures in spectra with low transparency in the fingerprint area, the approach showed promise. The Tanimoto coefficient was used as a metric for molecular similarity, providing valuable insights, though sometimes diverging from chemist intuition. The study also highlighted that scaling composition formulas and molecular weights did not affect the classification results, as the high-dimensional features dominated the process. Overall, the research demonstrated the feasibility of using IR spectral image data in machine learning for chemical classification, offering a novel perspective that complements traditional methods, even though some classifications may not always align with chemist intuition. Chemist ; Pattern recognition (psychology) ; Artificial intelligence ; Computer science ; Chemistry ; Organic chemistry Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Roel J. Leenhouts | Nathan Morgan | Emad Al Ibrahim | William H. Green | Florence H. Vermeire
Source: arXiv (Cornell University) (preprint, 2024-12-02)
Field: Materials Science — Materials Chemistry

Pooling Solvent Mixtures for Solvation Free Energy Predictions Solvation free energy is an important design parameter in reaction kinetics and separation processes, making it a critical property to predict during process development. In previous research, directed message passing neural networks (D-MPNN) have successfully been used to predict solvation free energies and enthalpies in organic solvents. However, solvent mixtures provide greater flexibility for optimizing solvent interactions than monosolvents. This work aims to extend our previous models to mixtures. To handle mixtures in a permutation invariant manner we propose a pooling function; MolPool. With this pooling function, the machine learning models can learn and predict properties for an arbitrary number of molecules. The novel SolProp-mix software that applies MolPool to D-MPNN was compared to state-of-the-art architectures for predicting mixture properties and validated with our new database of COSMOtherm calculations; BinarySolv-QM. To improve predictions towards experimental accuracy, the network was then fine-tuned on experimental data in monosolvents. To demonstrate the benefit of this transfer learning methodology, experimental datasets of solvation free energies in binary (BinarySolv-Exp) and ternary (TernarySolv-Exp) solvent mixtures were compiled from data on vapor-liquid equilibria and activity coefficients. The neural network performed better than COSMOtherm calculations with an MAE of 0.25 kcal/mol and an RMSE of 0.37 kcal/mol for non-aqueous mixed solvents. Additionally, the ability to capture trends for a varying mixture composition was validated successfully. Our model's ability to accurately predict mixture properties from the combination of in silico data and pure component experimental data is promising given the scarcity of experimental data for mixtures in many fields. Solvation ; Pooling ; Energy (signal processing) ; Solvent ; Chemistry ; Thermodynamics ; Computational chemistry ; Computer science ; Mathematics ; Physics ; Artificial intelligence ; Organic chemistry ; Statistics Machine Learning in Materials Science
Authors: Takashiro Akitsu | Yuya Higashi | Rin Tsuchiya | Taiga Imae | Keishiro Komatsu | Daisuke Nakane | Dohyun Moon
Source: Symmetry (article, 2024-03-07)
Field: Materials Science — Materials Chemistry

Chemistry for Space Group Symmetry beyond Crystals As a recent cutting-edge research target, the prediction of crystal systems or space groups using machine learning based on databases has been actively reported [...] Space (punctuation) ; Group (periodic table) ; Symmetry (geometry) ; Enhanced Data Rates for GSM Evolution ; Crystal (programming language) ; Space group ; Theoretical physics ; Crystallography ; Chemistry ; Computer science ; Materials science ; Physics ; Artificial intelligence ; Mathematics ; Geometry ; Quantum mechanics ; X-ray crystallography Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; History and advancements in chemistry
Authors: Jiankai Wang | X. Chelsea Chen | Xiao Wang | Lihai Xu | Shuhui Tan | Jun Tang | Wei Chen | Haodong Tang
Source: SID Symposium Digest of Technical Papers (article, 2024-06-01)
Field: Materials Science — Materials Chemistry

P‐111: A Machine Learning Perspective for the Optimization of Annealing Parameters in Solution Processed Thin Film Devices This work adopts machine learning to refine the annealing process in solution‐processed thin films, such as QDs, perovskites, and organic semiconductors. The annealing temperature and time of PbS QD thin film is optimized using a machine learning algorithm. This method offers a systematic approach to determine the optimal annealing parameters, surpassing the traditional, less‐efficient methods. This adaptive model minimizes human bias, handles complex nonlinear relationships, and capable of optimizing multiple parameters simultaneously. By introducing machine learning algorithm, this method provides a universal and effective strategy for the optimization of parameters during the fabrication of thin films. Perspective (graphical) ; Annealing (glass) ; Thin film ; Materials science ; Simulated annealing ; Computer science ; Nanotechnology ; Artificial intelligence ; Machine learning ; Composite material Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Thin-Film Transistor Technologies
Authors: Seong-Min Kim | Yousung Jung | Joshua Schrier
Source: ChemRxiv (preprint, 2024-06-17)
Field: Materials Science — Materials Chemistry

Large Language Models for Inorganic Synthesis Predictions We evaluate the effectiveness of pre-trained and fine-tuned large language models (LLMs) for predicting the synthesizability of inorganic compounds and the selection of precursors needed to perform inorganic synthesis. The predictions of fine-tuned LLMs are comparable to—and sometimes better than—recent bespoke machine learning models for these tasks, but require only minimal user expertise, cost, and time to develop. Therefore, this strategy can serve both as an effective and strong baseline for future machine learning studies of various chemical applications and as a practical tool for experimental chemists. Bespoke ; Computer science ; Selection (genetic algorithm) ; Baseline (sea) ; Machine learning ; Artificial intelligence ; Business Machine Learning in Materials Science ; Topic Modeling ; Computational Drug Discovery Methods
Authors: Shinnosuke Hattori | Qiang Zhu
Source: ACS Omega (article, 2024-08-19)
Field: Chemistry — Physical and Theoretical Chemistry

Revisiting Aspirin Polymorphic Stability Using a Machine Learning Potential In this study, we present a systematic computational investigation to analyze the long-debated free energy stability of two well-known aspirin polymorphs, denoted as Form I and Form II. Specifically, we developed a strategy to collect training configurations covering diverse interatomic interactions between representative functional groups in aspirin crystals. Utilizing a state-of-the-art neural network interatomic potential (NNIP) model, we trained an accurate machine learning potential to simulate aspirin crystal dynamics under finite temperature conditions with ∼0.46 kJ/mol/molecule accuracy. Employing the trained NNIP model, we performed thermodynamic integration to assess the free energy difference between aspirins I and II, accounting for the anharmonic effects in a large supercell consisting of 512 molecules. For the first time, our results convincingly demonstrated that Form I is more stable than Form II at 300 K, ranging from 0.74 to 1.83 kJ/mol/molecule, aligning with experimental observations. Unlike the majority of previous simulations based on (quasi)harmonic approximations in a small super cell, which often found degenerate energies between aspirins I and II, our findings underscore the importance of anharmonic effects in determining polymorphic stability ranking. Furthermore, we proposed the use of the rotational degrees of freedom of methyl and ester/phenyl groups in aspirin crystals as characteristic motions to highlight rotational entropic contribution that favors the stability of Form I. From the structural perspective, we also found that the subtle free energy difference can be used to explain the distinct thermal expansion responses as observed in both experimental and simulation data. Beyond the aspirin polymorphism, we anticipate that such entropy-driven stabilization can be broadly applicable to many other organic systems, suggesting that our approach holds great promise for stability studies in small-molecule drug design. Anharmonicity ; Stability (learning theory) ; Degenerate energy levels ; Molecular dynamics ; Chemistry ; Molecule ; Computational chemistry ; Statistical physics ; Physics ; Thermodynamics ; Machine learning ; Computer science ; Quantum mechanics Crystallography and molecular interactions ; Nonlinear Optical Materials Research ; Crystallization and Solubility Studies
Authors: César Miguel Valdez Córdova
Source:  (article, 2024-07-27)
Field: Materials Science — Materials Chemistry

Towards Learning Activity Cliff-Aware Molecular Representations Current deep learning based methods for molecular property prediction show pronounced shortcomings when predicting molecular properties in the presence of activity cliffs (AC): pairs of structurally similar molecules with significant differences in potency. We investigate how inductive biases of increasing complexity, from simple Multilayer Perceptrons (MLPs) to selfsupervised models, impact the learning of representations from Extended-connectivity Fingerprints (ECFPs). Leveraging the Matched Molecular Pair (MMP) abstraction, we explore various pre-training schemes designed to capture AC relationships. While simple models remain competitive, we show extensive differences and avenues for potential improvement in performance across different inductive bias choices and pre-training strategies, paving the way for AC-aware and consequently, chemically robust model design. Cliff ; Computer science ; Human–computer interaction ; Geography ; Archaeology Machine Learning in Materials Science ; Scientific Computing and Data Management
Authors: Ryuhei Sato | Lewis J. Conway | Di Zhang | Chris J. Pickard | Kazuto Akagi | Kartik Sau | Hao Li | Shin‐ichi Orimo
Source: ChemRxiv (preprint, 2024-05-29)
Field: Materials Science — Materials Chemistry

Surface Melting Promoted by Hydrogenation: Guidelines for Superhydride Synthesis The synthesis of new superhydrides with high superconducting Tc is challenging owing to the high temperatures and pressures required. Herein, we use machine-learning potential molecular dynamics simulations to investigate the initial stages of superhydride formation in calcium hydrides. Upon contact with high-pressure H2, the surface of CaH2 melts, leading to CaH4 formation. This surface melting, facilitated by the negative enthalpy of the hydrogenation reaction, proceeds via a liquid CaH4 intermediate state. Our findings show that surface melting becomes more favorable than the melting of the superhydride bulk under high pressure, reducing the activation energy required for hydrogenation. From these thermodynamics, we propose superhydride synthesis guidelines based on bulk properties: superhydride melting temperature and pressure-dependent hydrogenation enthalpy, readily determined through supplementary calculations during structure prediction workflows. Surface (topology) ; Materials science ; Surface modification ; Chemical engineering ; Business ; Nanotechnology ; Mathematics ; Engineering ; Geometry Hydrogen Storage and Materials ; Superconducting Materials and Applications ; Spacecraft and Cryogenic Technologies
Authors: Manzar Masud | Aamir Mubashar
Source:  (article, 2024-08-20)
Field: Materials Science — Polymers and Plastics

Prediction of Low Velocity Impact Performance of Carbon/Flax Bio-Hybrid Composites Using Experimental and Neural Network Approaches In this paper., the effect of fiber hybridization and stacking configurations on the low-velocity impact behavior of carbon/flax bio-hybrid composite laminates was investigated. The methodology was developed based on experimental testing and then utilizing an artificial neural network-based machine learning regression model. Five different stacking configurations of carbon/flax material were manufactured using the vacuum bagging method and then subjected to low-velocity impact with impact energies ranging from 15J to 90J. Data in the form of peak impact force was recorded for each stacking configuration. A symmetric configuration with a uniform distribution of flax layers across the thickness exhibited enhanced performance. Two machine learning approaches—deep neural network (DNN) with Adam optimizer and DNN with stochastic gradient descent (SGD)—were applied. The performance metrics used to evaluate the performance were the coefficient of determination <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(\mathrm{R}^{2})$</tex>, mean square error (MSE)., and mean absolute error (MAE). The best-performing model for the prediction of peak impact force was DNN-SGD., with three hidden layers and 80 neurons in each hidden layer. The highest correlation and the smallest MSE and MAE values were attained using the DNN-SGD model. The developed methodology and the model serve as powerful tools for the prediction of the mechanical properties of bio-hybrid composite laminates., utilizing minimal resources and saving time as well. Composite material ; Artificial neural network ; Materials science ; Carbon fibers ; Computer science ; Artificial intelligence ; Composite number Natural Fiber Reinforced Composites ; Additive Manufacturing and 3D Printing Technologies ; Cellular and Composite Structures
Authors: Matthew Witman | Peter Schindler
Source: ChemRxiv (preprint, 2024-08-06)
Field: Materials Science — Materials Chemistry

MatFold: systematic insights into materials discovery models' performance through standardized cross-validation protocols Machine learning (ML) models in the materials sciences that are validated by overly simplistic cross-validation (CV) protocols can yield biased performance estimates for downstream modeling or materials screening tasks. This can be particularly counterproductive for applications where the time and cost of failed validation efforts (experimental synthesis, characterization, and testing) are consequential. We propose a set of standardized and increasingly difficult splitting protocols for chemically and structurally motivated CV that can be followed to validate any ML model for materials discovery. Among several benefits, this enables systematic insights into model generalizability, improvability, and uncertainty, provides benchmarks for fair comparison between competing models with access to differing quantities of data, and systematically reduces possible data leakage through increasingly strict splitting protocols. A general-purpose, model-agnostic toolkit, MatFold, is provided to automate the construction of these CV splits and encourage further community use. Generalizability theory ; Computer science ; Set (abstract data type) ; Data mining ; Model validation ; Data science ; Machine learning ; Mathematics ; Statistics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Edward Lee | Daniel Salley | Abhishek Sharma | Leroy Cronin
Source: arXiv (Cornell University) (preprint, 2024-09-08)
Field: Materials Science — Materials Chemistry

AI-Driven Robotic Crystal Explorer for Rapid Polymorph Identification Crystallisation is an important phenomenon which facilitates the purification as well as structural and bulk phase material characterisation using crystallographic methods. However, different conditions can lead to a vast set of different crystal structure polymorphs and these often exhibit different physical properties, allowing materials to be tailored to specific purposes. This means the high dimensionality that can result from variations in the conditions which affect crystallisation, and the interaction between them, means that exhaustive exploration is difficult, time-consuming, and costly to explore. Herein we present a robotic crystal search engine for the automated and efficient high-throughput approach to the exploration of crystallisation conditions. The system comprises a closed-loop computer crystal-vision system that uses machine learning to both identify crystals and classify their identity in a multiplexed robotic platform. By exploring the formation of a well-known polymorph, we were able to show how a robotic system could be used to efficiently search experimental space as a function of relative polymorph amount and efficiently create a high dimensionality phase diagram with minimal experimental budget and without expensive analytical techniques such as crystallography. In this way, we identify the set of polymorphs possible within a set of experimental conditions, as well as the optimal values of these conditions to grow each polymorph. Identification (biology) ; Crystal (programming language) ; Crystallography ; Computational biology ; Computer science ; Artificial intelligence ; Chemistry ; Biology ; Programming language ; Botany Crystallization and Solubility Studies
Authors: Ignas Pakamorė | Ross S. Forgan
Source: ChemRxiv (preprint, 2024-05-29)
Field: Chemistry — Organic Chemistry

Computation-Guided Exploration of The Reaction Parameter Space of N,N-Dimethylformamide Hydrolysis Navigating the reaction parameter space can pose challenges, especially considering the exponential growth in the number of parameters even in seemingly straightforward chemical reactions. Consequently, recent research efforts have been increasingly dedicated to the development of computational tools aimed at facilitating the exploration process. Herein, we introduce ChemSPX, a Python-based program specifically crafted for exploring the complex landscape of reaction parameter space. We propose the use of the inverse distance function to map reaction parameter space and efficiently sample sparse regions. This is implemented in ChemSPX to allow the user to simply generate sets of reaction conditions that efficiently sample wide parameter spaces. In addition, the program includes tools necessary for the analysis and comprehension of the multidimensional parameter space landscape. The developed algorithms were utilized to experimentally investigate the hydrolysis of N,N- dimethylformamide (DMF), a commonly employed solvent, in the specific context of metal- organic framework synthesis. We use ChemSPX to generate batches of experiments to sample parameter space, starting from an empty space, but subsequently assessing undersampled regions. We use statistical analysis and machine learning models to show that addition of strong acids induces hydrolysis, generating up to 1.0% (w/w) formic acid. The results show that ChemSPX can generate datasets that efficiently sample parameter space, in this case allowing the user to distinguish the individual effects of five different physical and chemical variables on reaction outcome. Dimethylformamide ; Computation ; Hydrolysis ; Space (punctuation) ; Parameter space ; Materials science ; Chemistry ; Computer science ; Mathematics ; Organic chemistry ; Algorithm ; Statistics Chemical Reaction Mechanisms
Authors: Debjyoti Bhattacharya | Harrison J. Cassady | Michael A. Hickner | Wesley F. Reinhart
Source: ChemRxiv (preprint, 2024-05-21)
Field: Materials Science — Materials Chemistry

Large Language Models as Molecular Design Engines The design of small molecules is crucial for technological applications ranging from drug discovery to energy storage. Due to the vast design space available to modern synthetic chemistry, the community has increasingly sought to use data-driven and machine learning approaches to navigate this space. Although generative machine learning methods have recently shown potential for computational molecular design, their use is hindered by complex training procedures, and they often fail to generate valid and unique molecules. In this context, pre-trained Large Language Models (LLMs) have emerged as potential tools for molecular design, as they appear to be capable of creating and modifying molecules based on simple instructions provided through natural language prompts. In this work, we show that the Claude 3 Opus LLM can read, write, and modify molecules according to prompts, with an impressive 97% valid and unique molecules. By quantifying these modifications in a low-dimensional latent space, we systematically evaluate the model’s behavior under different prompting conditions. Notably, the model is able to perform guided molecular generation when asked to manipulate the electronic structure of molecules using simple, natural-language prompts. Our findings highlight the potential of LLMs as powerful and versatile molecular design engines. Computer science ; Design language ; Programming language Machine Learning in Materials Science ; Topic Modeling
Authors: Sayantan Dandapat | Nepal Sahu | Chandrashekhar Azad | Uday Kumar
Source:  (article, 2024-06-21)
Field: Materials Science — Materials Chemistry

Lattice thermal conductivity of material and its correlation with Debye temperature using regression models Lattice thermal conductivity (K) of the material plays a crucial role in its variety of applications such as thermoelectric, thermal barrier coating and so on. The role of the Debye temperature (T<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</inf>) is a significant factor in determining the lattice thermal conductivity of any materials. However, the relation between the lattice thermal conductivity and Debye temperature is very less explored using Machine Learning (ML). In this study, we first predicted lattice thermal conductivity using four machine learning regression models: Neural Network (NN), Gaussian Process Regression (GPR), Boosted Tree Regression (BTR) and Fine Tree Regression (FTR). NN and GPR is found to be the best performing model among all the used models with R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> equal to 99.868 % and 97.333 % respectively. Further, for these two best performed models, we predict the relation between the K and T<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</inf> in both training and testing data. The relation between training and testing exhibits similar nature in the two models NN and GPR. This work guides a pathway for searching of materials for wide applications. Debye model ; Condensed matter physics ; Materials science ; Thermal conductivity ; Lattice (music) ; Regression analysis ; Correlation ; Physics ; Statistics ; Composite material ; Mathematics ; Acoustics Advanced Thermoelectric Materials and Devices ; Thermal properties of materials ; Machine Learning in Materials Science