jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Baiqing Li | Shimin Su | Chan Zhu | Jie Lin | Xinyue Hu | Lebin Su | Zhunzhun Yu | Kuangbiao Liao | Hongming Chen
Source: Research Square (preprint, 2022-05-26)
Field: Materials Science — Materials Chemistry

GraphRXN: A Novel Representation for Reaction Prediction Abstract In recent years, it has been seen that artificial intelligence (AI) starts to bring revolutionary changes to chemical synthesis. However, the lack of suitable ways of representing chemical reactions and the scarceness of reaction data has limited the wider application of AI to reaction prediction. Here, we introduce a novel reaction representation, GraphRXN, for reaction prediction. It utilizes a universal graph-based neural network framework to encode chemical reactions by directly taking two-dimension reaction structures as inputs. The GraphRXN model was evaluated by three publically available chemical reaction datasets and gave on-par or superior results compared with other baseline models. To further evaluate the effectiveness of GraphRXN, wet-lab experiments were carried out for the purpose of generating reaction data. GraphRXN model was then built on high-throughput experimentation data and a decent accuracy (R 2 of 0.713) was obtained on our in-house data validation. This highlights that the GraphRXN model can be deployed in an integrated workflow which combines robotics and AI technologies for forward reaction prediction. Representation (politics) ; Computer science ; Artificial intelligence ; Political science ; Law Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: ZhenDong Li | Fan Pan | Shuai Ye | Hao Hu
Source: 2nd International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2022) (article, 2022-05-17)
Field: Materials Science — Materials Chemistry

Modeling and optimization of chemical reaction based on XGBoost-PSO This paper uses machine learning algorithm and intelligent optimization algorithm for chemical data modeling, build based on XGBoost and PSO of chemical reaction prediction and optimization model, for chemical reaction data, especially in organic reaction is low experimental efficiency, low prediction accuracy, we innovative use XGBoost machine learning model, for small batch chemical reaction data regression modeling, mining data high dimension of small sample characteristics. On the basis of the constructed regression model, the particle swarm optimization algorithm is used to optimize the reaction conditions to find the balance point in the chemical reaction, which overcomes the problems of low product rate and difficult raw material ratio in the chemical reaction.Based on this, we designed a set of universality algorithm for chemical reaction optimization, conducted data modeling through XGBoost, and quickly found the optimal reaction conditions by PSO, and applied them to ethanol-coupled C-4 olefin reaction preparation. Through experimental analysis, the MAE, MSE and R2 scores of our XGBoost model in regression analysis are 29.82, 4.01 and 0.93, all better than other machine learning models, which has certain statistical significance. Secondly, in the comparative literature and experiments, the optimal solution obtained by PSO search conforms to the principle and reality of chemical preparation, which has certain industrial value. The modeling algorithm can be further extended to the fields of biopharmaceutical and machine molecular preparation, to provide the basis for decision-making for researchers and find new experimental ideas and methods. The algorithm has strong general adaptation and popularization significance. Computer science ; Particle swarm optimization ; Algorithm ; Mathematical optimization ; Artificial intelligence ; Machine learning ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Qiannan Duan | Jiayuan Chen | Jianchao Lee | Sifan Bi | Run Luo | Fenli Liu | Yunjin Feng
Source: ChemRxiv (preprint, 2022-03-17)
Field: Materials Science — Materials Chemistry

Chemical Reaction Spectrum: A Holographic Image for Characterizing Multi-component Chemical Mixtures Multi-component chemical mixtures (MCMs) and their various effects always concerned in analytical chemistry, but current analytical techniques based on test-tube experiments often involves many high-cost and laborious operations. Today’s pop machine-learning (ML) technology has exhibited their successes in dealing with the analysis task of various complex systems. Predictably, the introduction of ML will radically accelerate the exploration of many fields involving mixture analysis. But the biggest challenge ahead for this process is how to provide some intelligible and sufficient data for various algorithms. In this study, we proposed a chemical imaging strategy to visualize various mixtures as some feature images by using ink-jet printing technology based on combinatorial chemistry. Here, these feature images were as a novel data form of chemical reaction spectrum (CRS), which can comprehensively describe and record the reaction characteristics of the complex sample. Compared with common imaging methods, the CRS with high-throughput chemical reaction dots is an efficient and economic information visualization way for the MCM sample. It is expected to be an important data acquisition approach for the application of ML in the field of chemistry in future. Computer science ; Visualization ; Component (thermodynamics) ; Holography ; Sample (material) ; Throughput ; Feature (linguistics) ; Process (computing) ; Field (mathematics) ; Chemical reaction ; Chemical imaging ; Chemical process ; Artificial intelligence ; Process engineering ; Chemistry ; Engineering ; Mathematics ; Chromatography ; Physics Machine Learning in Materials Science ; Cell Image Analysis Techniques ; CCD and CMOS Imaging Sensors
Authors: Lenz Fiedler | Nils Hoffmann | Parvez Mohammed | Gabriel Popoola | Tamar Yovell | Vladyslav Oles | John Ellis | S. Rajamanickam | Attila Cangi
Source: arXiv (Cornell University) (preprint, 2022-02-18)
Field: Materials Science — Materials Chemistry

Training-free hyperparameter optimization of neural networks for electronic structures in matter A myriad of phenomena in materials science and chemistry rely on quantum-level simulations of the electronic structure in matter. While moving to larger length and time scales has been a pressing issue for decades, such large-scale electronic structure calculations are still challenging despite modern software approaches and advances in high-performance computing. The silver lining in this regard is the use of machine learning to accelerate electronic structure calculations -- this line of research has recently gained growing attention. The grand challenge therein is finding a suitable machine-learning model during a process called hyperparameter optimization. This, however, causes a massive computational overhead in addition to that of data generation. We accelerate the construction of neural network models by roughly two orders of magnitude by circumventing excessive training during the hyperparameter optimization phase. We demonstrate our workflow for Kohn-Sham density functional theory, the most popular computational method in materials science and chemistry. Hyperparameter ; Workflow ; Computer science ; Overhead (engineering) ; Artificial neural network ; Machine learning ; Electronic structure ; Artificial intelligence ; Process (computing) ; Chemistry ; Computational chemistry Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Catalysis and Oxidation Reactions
Authors: Ting Wang | Jaroslaw Knap
Source: arXiv (Cornell University) (preprint, 2022-06-02)
Field: Materials Science — Materials Chemistry

Stochastic Deep-Ritz for Parametric Uncertainty Quantification Scientific machine learning has become an increasingly important tool in materials science and engineering. It is particularly well suited to tackle material problems involving many variables or to allow rapid construction of surrogates of material models, to name just a few. Mathematically, many problems in materials science and engineering can be cast as variational problems. However, handling of uncertainty, ever present in materials, in the context of variational formulations remains challenging for scientific machine learning. In this article, we propose a deep-learning-based numerical method for solving variational problems under uncertainty. Our approach seamlessly combines deep-learning approximation with Monte-Carlo sampling. The resulting numerical method is powerful yet remarkably simple. We assess its performance and accuracy on a number of variational problems. Computer science ; Context (archaeology) ; Uncertainty quantification ; Monte Carlo method ; Parametric statistics ; Deep learning ; Simple (philosophy) ; Variational Monte Carlo ; Artificial intelligence ; Mathematical optimization ; Machine learning ; Applied mathematics ; Mathematics ; Quantum Monte Carlo ; Epistemology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Manufacturing Process and Optimization
Authors: Cheng-Han Li | Daniel P. Tabor
Source: ChemRxiv (preprint, 2022-11-30)
Field: Materials Science — Materials Chemistry

Accelerating Organic Electronic Materials Design with Low-Cost, Robust Molecular Reorganization Energy Predictions A critical bottleneck for the design of high-conductivity organic materials is finding molecules with low reorganization energy. The development of low-cost machine-learning-based models for calculating the reorganization energy has proven to be challenging. Here we combine a graph-based neural network recently benchmarked for drug design applications, ChIRo, with low-cost conformational features and show the feasibility of reorganization energy predictions on the benchmark QM9 dataset without needing DFT geometries. Bottleneck ; Benchmark (surveying) ; Computer science ; Artificial neural network ; Low energy ; Minification ; Energy (signal processing) ; Organic molecules ; Deep neural networks ; Artificial intelligence ; Molecule ; Chemistry ; Physics ; Embedded system Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: 敦人 世古
Source: Kyoto University Research Information Repository (Kyoto University) (article, 2022-02-01)
Field: Materials Science — Materials Chemistry

Modeling of materials and its applications using density functional theory calculation and machine learning 第一原理計算は, 物質や材料のエネルギーや基礎的な物性, 電子状態を得るための手段として不可欠であるだけでなく, より高度な計算である材料スクリーニング, 結晶構造探索, 未知物質予測, 熱力学計算, 分子動力学計算などを行う手段としても一般的になりつつある. しかし, このような高度な計算は, 網羅的な第一原理計算を必要とするため, 実行可能な対象は限定的であるというのが現状である. 本稿では, 機械学習手法や効率的データ構造を導入することにより, 第一原理計算に基づいた高度な計算を効率的に実施し, それらの応用範囲(原子数, 時間範囲, 自由度)を大幅に拡大するための手法について紹介する. 第一原理計算は, 物質や材料のエネルギーや基礎的な物性, 電子状態を得るための手段として不可欠であるだけでなく, より高度な計算である材料スクリーニング, 結晶構造探索, 未知物質予測, 熱力学計算, 分子動力学計算などを行う手段としても一般的になりつつある. しかし, このような高度な計算は, 網羅的な第一原理計算を必要とするため, 実行可能な対象は限定的であるというのが現状である. 本稿では, 機械学習手法や効率的データ構造を導入することにより, 第一原理計算に基づいた高度な計算を効率的に実施し, それらの応用範囲(原子数, 時間範囲, 自由度)を大幅に拡大するための手法について紹介する. Computational learning theory ; Statistical learning theory ; Density functional theory ; Stability (learning theory) ; Feature (linguistics) Machine Learning in Materials Science ; Advanced Physical and Chemical Molecular Interactions ; X-ray Diffraction in Crystallography
Authors: Степанова, К. А. | Stepanova, K. A.
Source: Electronic scientific archive of UrFU (Ural Federal University) (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Validation of machine learning model to predict magnetic properties of nanocrystalline FINEMET type alloys В работе была произведена разработка модели машинного обучения на языке программирования Python, а также проведена ее валидация на этапах жизненного цикла. Целью создания модели машинного обучения является прогнозирование магнитных свойств нанокристаллических сплавов на основе железа по химическому составу и условиям обработки. Процесс валидации модели машинного обучения позволяет не только произвести контроль за соблюдением требований, предъявляемых при разработке и эксплуатации модели, к результатам, полученных с помощью моделирования, но и способствует внедрению модели в процесс производства. Процесс валидации включал в себя валидацию данных, в ходе которой были оценены типы, пропуски данных, соответствие цели исследования, распределения признаков и целевых характеристик, изучены корреляции признаков и целевых характеристик; валидацию алгоритмов, применяемых в модели: были проанализированы параметры алгоритмов с целью соблюдения требования о корректной обобщающей способности модели (отсутствие недо- и переобучения); оценку работы модели, благодаря которой был произведен анализ полученных результатов с помощью тестовых данных; верификацию результатов с помощью актуальных данных, полученных из статей, опубликованных с 2010 по 2022 год. В результате валидации модели было показано высокое качество разработанной модели, позволяющее получить оценки качества R2 0,65 и выше. Nanocrystalline material ; Type (biology) ; Work (physics) ; Support vector machine Machine Learning in Materials Science ; Magnetic Properties and Applications ; Metallic Glasses and Amorphous Alloys
Authors: Yujie Zhu | Qi Li | Jinliang He
Source: 2022 IEEE International Conference on High Voltage Engineering and Applications (ICHVE) (article, 2022-09-25)
Field: Materials Science — Materials Chemistry

Predicting Dielectric Polymer Component from Different Properties via Machine Learning Method Designing linear polymer of excellent insulating properties is always a disturbing problem in polymer design aspect, especially positively design which means positively get structure formula from possible properties. Many inspiring methods have been put effort into the aspect. In this paper, a new method has been proposed for predicting possible fragments in linear polymer and design the possible structure from targeted properties. Random forest method has been used for establishing the connection between five properties and 362 possible seed fragments. In the end, an example with properties values of PEI has been put through the method and different candidates with similar properties has been assembled including PEI polymer. Polymer ; Component (thermodynamics) ; Dielectric ; Connection (principal bundle) ; Materials science ; Computer science ; Biological system ; Algorithm ; Composite material ; Mechanical engineering ; Engineering ; Thermodynamics ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Fuel Cells and Related Materials
Authors: Yujie Zhu | Qi Li | Jinliang He
Source: 2022 IEEE International Conference on High Voltage Engineering and Applications (ICHVE) (article, 2022-09-25)
Field: Materials Science — Materials Chemistry

Predicting Dielectric Polymer Component from Different Properties via Machine Learning Method Designing linear polymer of excellent insulating properties is always a disturbing problem in polymer design aspect, especially positively design which means positively get structure formula from possible properties. Many inspiring methods have been put effort into the aspect. In this paper, a new method has been proposed for predicting possible fragments in linear polymer and design the possible structure from targeted properties. Random forest method has been used for establishing the connection between five properties and 362 possible seed fragments. In the end, an example with properties values of PEI has been put through the method and different candidates with similar properties has been assembled including PEI polymer. Polymer ; Component (thermodynamics) ; Dielectric ; Connection (principal bundle) ; Computer science ; Materials science ; Biological system ; Algorithm ; Mechanical engineering ; Composite material ; Engineering ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Fuel Cells and Related Materials
Authors: Matthew Witman | Anuj Goyal | Tadashi Ogitsu | Anthony H. McDaniel | Stephan Lany
Source:  (article, 2022-04-01)
Field: Materials Science — Materials Chemistry

Graph Convolutional Neural Network Modeling of Vacancy Formation for Materials Discovery in Solar Thermochemical Water Splitting. Abstract not provided. Template ; Computer science ; Leverage (statistics) ; Convolutional neural network ; Graph ; Set (abstract data type) ; Artificial neural network ; Theoretical computer science ; Artificial intelligence Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Fuel Cells and Related Materials
Authors: Junzhong Xie | Xuyuan Zhou | Lu-an Dong | Hong Jiang
Source: ChemRxiv (preprint, 2022-02-27)
Field: Materials Science — Materials Chemistry

Machine Learning Force Field Aided Cluster Expansion Approach to Configurationally Disordered Materials: Critical Assessment of Training Set Selection and Size Convergence Cluster expansion (CE) is a powerful theoretical tool to study the configuration-dependent properties of substitutionally disordered systems. Typically, a CE model is built by fitting a few tens or hundreds of target quantities calculated by first-principles approaches. To validate the reliability of the model, a convergence test of cross-validation (CV) score to the training set size is commonly conducted to verify the sufficiency of training data. However, such test only confirms the convergence of the predictive capability of the CE model within the training set and it is unknown whether the convergence of the CV score would lead to robust thermodynamic simulation results such as order-disorder phase transition temperature $T_{\rm c}$. In this work, using carbon defective MoC$_{1-x}$ as a model system and aided by the machine-learning force field technique, a training data pool with about 13000 configurations has been efficiently obtained and used to generate different training sets of the same size randomly. By conducting parallel Monte Carlo simulations with the CE models trained with different randomly selected training set, the uncertainty in calculated $T_{\rm c}$ can be evaluated at different training set size. It is found that the training set size that is sufficient for the CV score to converge still leads to a significant uncertainty in the predicted $T_{\rm c}$, and that the latter can be considerably reduced by enlarging the training set to that of a few thousand configurations. This work highlights the importance of considering large training set for building the optimal CE model that can achieve robust statistical modeling results, and the facility provided by the machine-learning force field approach to efficiently produce adequate training data. Convergence (economics) ; Test set ; Set (abstract data type) ; Training set ; Computer science ; Monte Carlo method ; Cluster (spacecraft) ; Machine learning ; Training (meteorology) ; Field (mathematics) ; Artificial intelligence ; Work (physics) ; Data set ; Algorithm ; Mathematics ; Statistics ; Physics ; Engineering ; Mechanical engineering Machine Learning in Materials Science ; Theoretical and Computational Physics ; nanoparticles nucleation surface interactions
Authors: David Meier | Rishan Ragunathan | Sebastian Degener | Alexander Liehr | Malte Vollmer | Thomas Niendorf | Bernhard Sick
Source: Research Square (preprint, 2022-08-23)
Field: Materials Science — Materials Chemistry

Reconstruction of Incomplete X-Ray Diffraction Pole Figures Using Deep Learning Abstract X-ray diffraction crystallography allows non-destructive examination of crystal structures. Furthermore, it has low requirements regarding the surface preparation, especially compared to electron backscatter diffraction. However, up to now, X-ray diffraction is highly time-consuming in standard laboratory conditions since we have to record intensities on multiple lattice planes by rotating and tilting the sample. In this article, we propose a method based on deep learning that allows faster experimentation due to accurate reconstructions of pole figure regions, which we did not probe experimentally. To speed up the development of our proposed method and further machine learning algorithms, we introduce a GPU-based simulation for data generation. Furthermore, we present a pole widths standardization technique using a custom deep learning architecture that makes algorithms more robust against influences from the experiment setup and material. Diffraction ; Electron backscatter diffraction ; Lattice (music) ; Computer science ; Deep learning ; Pole figure ; Optics ; Algorithm ; X-ray crystallography ; Artificial intelligence ; Physics ; Acoustics X-ray Diffraction in Crystallography ; Machine Learning in Materials Science ; Advanced X-ray Imaging Techniques
Authors: Daniel S. Karls | George Karypis | Ilia Nikiforov | Zeren Shui | Ellad B. Tadmor | Mingjian Wen
Source:  (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Injecting Domain Knowledge from Empirical Interatomic Potentials to Neural Networks for Predicting Material Properties Artificial neural network ; Material properties ; Domain (mathematical analysis) ; Interatomic potential Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Model Reduction and Neural Networks
Authors: Zeyu Xiang | Yu Pang | X. Qian | Ronggui Yang
Source: arXiv (Cornell University) (preprint, 2022-12-10)
Field: Materials Science — Materials Chemistry

Machine learning reconstruction of depth-dependent thermal conductivity profile from pump-probe thermoreflectance signals Characterizing materials with spatially varying thermal conductivities is significant to unveil the structure-property relation for a wide range of functional materials, such as chemical-vapor-deposited diamonds, ion-irradiated materials, nuclear materials under radiation, and battery electrode materials. Although the development of thermal conductivity microscopy based on time/frequency-domain thermoreflectance (TDTR/FDTR) enabled in-plane scanning of thermal conductivity profile, measuring depth-dependent thermal conductivity remains challenging. This work proposed a machine-learning-based reconstruction method for extracting depth-dependent thermal conductivity K(z) directly from frequency-domain phase signals. We demonstrated that the simple supervised-learning algorithm kernel ridge regression (KRR) can reconstruct K(z) without requiring pre-knowledge about the functional form of the profile. The reconstruction method can not only accurately reproduce typical K(z) distributions such as the pre-assumed exponential profile of chemical-vapor-deposited (CVD) diamonds and Gaussian profile of ion-irradiated materials, but also complex profiles artificially constructed by superimposing Gaussian, exponential, polynomial, and logarithmic functions. In addition to FDTR, the method also shows excellent performances of reconstructing K(z) of ion-irradiated semiconductors from TDTR signals. This work demonstrates that combining machine learning with pump-probe thermoreflectance is an effective way for depth-dependent thermal property mapping. Thermal conductivity ; Materials science ; Gaussian ; Gaussian function ; Irradiation ; Thermal ; Logarithm ; Optoelectronics ; Optics ; Computational physics ; Composite material ; Chemistry ; Physics ; Mathematics ; Thermodynamics ; Mathematical analysis Thermal properties of materials ; Thermography and Photoacoustic Techniques ; Machine Learning in Materials Science
Authors: H.P. Van Landeghem
Source: SPIRE - Sciences Po Institutional REpository (article, 2022-06-20)
Field: Materials Science — Materials Chemistry

High-throughput investigations of phase transformations in multi-component alloys : combinatorial experiments and machine learning International audience Component (thermodynamics) ; Throughput ; Computer science ; Phase (matter) ; Artificial intelligence ; Machine learning ; Chemistry ; Physics ; Operating system ; Thermodynamics Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; Additive Manufacturing Materials and Processes