jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Albert Zhu | Simon Batzner | Albert Musaelian | Boris Kozinsky
Source: arXiv (Cornell University) (preprint, 2022-11-17)
Field: Materials Science — Materials Chemistry

Fast Uncertainty Estimates in Deep Learning Interatomic Potentials Deep learning has emerged as a promising paradigm to give access to highly accurate predictions of molecular and materials properties. A common short-coming shared by current approaches, however, is that neural networks only give point estimates of their predictions and do not come with predictive uncertainties associated with these estimates. Existing uncertainty quantification efforts have primarily leveraged the standard deviation of predictions across an ensemble of independently trained neural networks. This incurs a large computational overhead in both training and prediction that often results in order-of-magnitude more expensive predictions. Here, we propose a method to estimate the predictive uncertainty based on a single neural network without the need for an ensemble. This allows us to obtain uncertainty estimates with virtually no additional computational overhead over standard training and inference. We demonstrate that the quality of the uncertainty estimates matches those obtained from deep ensembles. We further examine the uncertainty estimates of our methods and deep ensembles across the configuration space of our test system and compare the uncertainties to the potential energy surface. Finally, we study the efficacy of the method in an active learning setting and find the results to match an ensemble-based strategy at order-of-magnitude reduced computational cost. Computer science ; Inference ; Artificial neural network ; Machine learning ; Ensemble forecasting ; Artificial intelligence ; Uncertainty quantification ; Overhead (engineering) ; Deep learning ; Ensemble learning ; Quality (philosophy) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Machine Learning and Algorithms
Authors: Ruihao Yuan | Zhen Liu | Yangyang Xu | Ruowei Yin | Jingjin He | Yang Bai | Yumei Zhou | Jinshan Li | Dezhen Xue | Turab Lookman
Source: SSRN Electronic Journal (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Optimizing Electrocaloric Effect in Barium Titanate-Based Room Temperature Ferroelectrics: Combining Landau Theory, Machine Learning and Synthesis Barium titanate ; Electrocaloric effect ; Materials science ; Landau theory ; Condensed matter physics ; Ferroelectricity ; Optoelectronics ; Physics ; Dielectric ; Phase transition Ferroelectric and Piezoelectric Materials ; Ferroelectric and Negative Capacitance Devices ; Machine Learning in Materials Science
Authors: Zhonglin Cao | Rishikesh Magar | Yuyang Wang | Amir Barati Farimani
Source: arXiv (Cornell University) (preprint, 2022-10-25)
Field: Chemistry — Inorganic Chemistry

MOFormer: Self-Supervised Transformer model for Metal-Organic Framework Property Prediction Metal-Organic Frameworks (MOFs) are materials with a high degree of porosity that can be used for applications in energy storage, water desalination, gas storage, and gas separation. However, the chemical space of MOFs is close to an infinite size due to the large variety of possible combinations of building blocks and topology. Discovering the optimal MOFs for specific applications requires an efficient and accurate search over an enormous number of potential candidates. Previous high-throughput screening methods using computational simulations like DFT can be time-consuming. Such methods also require optimizing 3D atomic structure of MOFs, which adds one extra step when evaluating hypothetical MOFs. In this work, we propose a structure-agnostic deep learning method based on the Transformer model, named as MOFormer, for property predictions of MOFs. The MOFormer takes a text string representation of MOF (MOFid) as input, thus circumventing the need of obtaining the 3D structure of hypothetical MOF and accelerating the screening process. Furthermore, we introduce a self-supervised learning framework that pretrains the MOFormer via maximizing the cross-correlation between its structure-agnostic representations and structure-based representations of crystal graph convolutional neural network (CGCNN) on &gt;400k publicly available MOF data. Using self-supervised learning allows the MOFormer to intrinsically learn 3D structural information though it is not included in the input. Experiments show that pretraining improved the prediction accuracy of both models on various downstream prediction tasks. Furthermore, we revealed that MOFormer can be more data-efficient on quantum-chemical property prediction than structure-based CGCNN when training data is limited. Overall, MOFormer provides a novel perspective on efficient MOF design using deep learning. Computer science ; Chemical space ; Transformer ; Quantum chemical ; Convolutional neural network ; Machine learning ; Supervised learning ; Artificial neural network ; Property (philosophy) ; Artificial intelligence ; Chemistry ; Molecule Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Paulo Neves | Kelly McClure | Jonas Verhoeven | Natalia Dyubankova | Ramil Nugmanov | Andrey Gedich | Sairam Menon | zhicai Shi | Jörg K. Wegner
Source: ChemRxiv (preprint, 2022-10-27)
Field: Materials Science — Materials Chemistry

Global Reactivity Models are Impactful in Industrial Synthesis Applications Artificial Intelligence is revolutionizing many aspects of the pharmaceutical industry. Deep learning models are now routinely applied to guide drug discovery projects leading to faster and improved findings, but there are still many tasks with enormous unrealized potential. One such task is the reaction yield prediction. Every year more than one fifth of all synthesis attempts result in product yields which are either zero or too low. This equates to chemical and human resources being spent on activities which ultimately do not progress the programs, leading to a triple loss when accounting for the cost of opportunity in time wasted. In this work we pre-train a BERT model on more than 16 million reactions from 4 different data sources, and fine tune it to achieve an uncertainty calibrated global yield prediction model. This model is an improvement upon state of the art not just from the increase in pre-train data but also by introducing a new embedding layer which solves a few limitations of SMILES and enables integration of additional information such as equivalents and molecule role into the reaction encoding, the model is called BERT Enriched Embedding (BEE). The model is benchmarked on an open-source dataset against a state-of-the-art synthesis focused BERT showing a near 20-point improvement in r2 score. The model is fine-tuned and tested on an internal company data benchmark, and a prospective study shows that the application of the model can reduce the total number of negative reactions (yield under 5%) ran in Janssen by at least 34%. Lastly, we corroborate the previous results through experimental validation, by directly deploying the model in an on-going drug discovery project and showing that it can also be used successfully as a reagent recommender due to its fast inference speed and reliable confidence estimation, a critical feature for industry application. Benchmark (surveying) ; Computer science ; Task (project management) ; Embedding ; Yield (engineering) ; Deep learning ; Artificial intelligence ; Encoding (memory) ; Data science ; Machine learning ; Industrial engineering ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: А.М. Trostianchyn | Ivan Izonin | Zoia Duriagina | Roman Tkachenko | V. V. Kulyk | B.M. Havrysh
Source: Archives of Materials Science and Engineering (article, 2022-08-01)
Field: Materials Science — Materials Chemistry

Boosting-based model for solving Sm-Co alloy’s maximum energy product prediction task This paper aims to decide the Sm-Co alloy’s maximum energy product prediction task based on the boosting strategy of the ensemble of machine learning methods. This paper examines an ensemble-based approach to solving Sm-Co alloy’s maximum energy product prediction task. Because classical machine learning methods sometimes do not supply acceptable precision when solving the regression problem, the authors investigated the boosting ML model, namely Gradient Boosting. Building a boosting model based on several weak submodels, each of which considers the errors of the prior ones, provides substantial growth in the accuracy of the problem-solving. The obtained result is confirmed using an actual data set collected by the authors. This work demonstrates the high efficiency of applying the ensemble strategy of machine learning to the applied problem of materials science. The experiments determined the highest accuracy of solving the forecast task for the maximum energy product of Sm-Co alloy formed on the boosting model of machine learning in comparison with classical methods of machine learning. The boosting strategy of machine learning, in comparison with single algorithms of machine learning, requires much more computational and time resources to implement the learning process of the model. This work demonstrated the possibility of effectively solving Sm-Co alloy’s maximum energy product prediction task using machine learning. The studied boosting model of machine learning for solving the problem provides high accuracy of prediction, which reveals several advantages of their use in solving issues applied to computational material science. Furthermore, using the Orange modelling environment provides a simple and intuitive interface for using the researched methods. The proposed approach to the forecast significantly reduces the time and resource costs associated with studying expensive rare earth metals (REM)-based ferromagnetic materials. The authors have collected and formed a set of data on predicting the maximum energy product of the Sm-Co alloy. We used machine learning tools to solve the task. As a result, the most increased forecasting precision based on the boosting model is demonstrated compared to classical machine learning methods. Boosting (machine learning) ; Gradient boosting ; Machine learning ; Artificial intelligence ; Computer science ; Ensemble learning ; Random forest Machine Learning in Materials Science ; Electric Power Systems and Control ; Industrial Engineering and Technologies
Authors: Jan Weinreich | Dominik Lemm | von Rudorff, Guido Falk | O. Anatole von Lilienfeld
Source: PubMed (preprint, 2022-03-31)
Field: Materials Science — Materials Chemistry

Ab initio machine learning of phase space averages Equilibrium structures determine material properties and biochemical functions. We here propose to machine learn phase space averages, conventionally obtained by ab initio or force-field-based molecular dynamics (MD) or Monte Carlo (MC) simulations. In analogy to ab initio MD, our ab initio machine learning (AIML) model does not require bond topologies and, therefore, enables a general machine learning pathway to obtain ensemble properties throughout the chemical compound space. We demonstrate AIML for predicting Boltzmann averaged structures after training on hundreds of MD trajectories. The AIML output is subsequently used to train machine learning models of free energies of solvation using experimental data and to reach competitive prediction errors (mean absolute error ∼ 0.8 kcal/mol) for out-of-sample molecules-within milliseconds. As such, AIML effectively bypasses the need for MD or MC-based phase space sampling, enabling exploration campaigns of Boltzmann averages throughout the chemical compound space at a much accelerated pace. We contextualize our findings by comparison to state-of-the-art methods resulting in a Pareto plot for the free energy of solvation predictions in terms of accuracy and time. Ab initio ; Statistical physics ; Solvation ; Force field (fiction) ; Machine learning ; Artificial intelligence ; Phase space ; Computer science ; Molecular dynamics ; Monte Carlo method ; Physics ; Mathematics ; Computational chemistry ; Chemistry ; Thermodynamics ; Molecule ; Quantum mechanics ; Statistics Machine Learning in Materials Science ; Quantum many-body systems ; Protein Structure and Dynamics
Authors: Arpan Kumar
Source: Materials Today Proceedings (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Virtual Prediction of Material Properties Python (programming language) ; Ternary operation ; Computer science ; Support vector machine ; Classifier (UML) ; Machine learning ; Alloy ; Computational science ; Artificial intelligence ; Materials science ; Programming language ; Metallurgy Machine Learning in Materials Science ; Metallurgical Processes and Thermodynamics ; Advanced Materials Characterization Techniques
Authors: Victor S.C. Fung | Shuyi Jia | Jiaxin Zhang | Sirui Bi | Junqi Yin | P. Ganesh
Source: arXiv (Cornell University) (preprint, 2022-07-27)
Field: Materials Science — Materials Chemistry

Atomic structure generation from reconstructing structural fingerprints Data-driven machine learning methods have the potential to dramatically accelerate the rate of materials design over conventional human-guided approaches. These methods would help identify or, in the case of generative models, even create novel crystal structures of materials with a set of specified functional properties to then be synthesized or isolated in the laboratory. For crystal structure generation, a key bottleneck lies in developing suitable atomic structure fingerprints or representations for the machine learning model, analogous to the graph-based or SMILES representations used in molecular generation. However, finding data-efficient representations that are invariant to translations, rotations, and permutations, while remaining invertible to the Cartesian atomic coordinates remains an ongoing challenge. Here, we propose an alternative approach to this problem by taking existing non-invertible representations with the desired invariances and developing an algorithm to reconstruct the atomic coordinates through gradient-based optimization using automatic differentiation. This can then be coupled to a generative machine learning model which generates new materials within the representation space, rather than in the data-inefficient Cartesian space. In this work, we implement this end-to-end structure generation approach using atom-centered symmetry functions as the representation and conditional variational autoencoders as the generative model. We are able to successfully generate novel and valid atomic structures of sub-nanometer Pt nanoparticles as a proof of concept. Furthermore, this method can be readily extended to any suitable structural representation, thereby providing a powerful, generalizable framework towards structure-based generation. Computer science ; Cartesian coordinate system ; Invertible matrix ; Generative model ; Representation (politics) ; Algorithm ; Theoretical computer science ; Invariant (physics) ; Artificial intelligence ; Generative grammar ; Mathematics ; Geometry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Jesse Zhao
Source: Frontiers in Materials (article, 2022-09-02)
Field: Materials Science — Materials Chemistry

Estimation of inorganic crystal densities using gradient boosted trees Density is a fundamental material property that can be used to determine a variety of other properties and the material’s feasibility for various applications, such as with energetic materials. However, current methods for determining density require significant resource investment, are computationally expensive, or lack accuracy. We used the properties of roughly ∼15,000 inorganic crystals to develop a highly accurate machine learning algorithm that can predict density. Our algorithm takes in the desired crystal’s chemical formula and generates 249 predictors from online materials databases, which are fed into a gradient boosted trees model. It exhibits a strong predictive power with an R 2 of ∼99%. Property (philosophy) ; Crystal (programming language) ; Variety (cybernetics) ; Crystal structure prediction ; Computer science ; Materials science ; Biological system ; Gradient boosting ; Algorithm ; Statistical physics ; Crystal structure ; Artificial intelligence ; Physics ; Chemistry ; Crystallography ; Random forest Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Riccardo Alessandri | Juan Pablo
Source: arXiv (Cornell University) (preprint, 2022-09-05)
Field: Materials Science — Materials Chemistry

Prediction of Electronic Properties of Radical-Containing Polymers at Coarse-Grained Resolutions The properties of soft electronic materials depend on the coupling of electronic and conformational degrees of freedom over a wide range of spatiotemporal scales. Description of such properties requires multiscale approaches capable of, at the same time, accessing electronic properties and sampling the conformational space of soft materials. This could in principle be realized by connecting the coarse-grained (CG) methodologies required for adequate conformational sampling to conformationally-averaged electronic property distributions via backmapping to atomistic-resolution level models and repeated quantum-chemical calculations. Computational demands of such approaches, however, have hindered their application in high-throughput computer-aided soft materials discovery. Here, we present a method that, combining machine learning and CG techniques, can replace traditional backmapping-based approaches without sacrificing accuracy. We illustrate the method for an emerging class of soft electronic materials, namely non-conjugated, radical-containing polymers, promising materials for all-organic energy storage. Supervised machine learning models are trained to learn the dependence of electronic properties on polymer conformation at CG resolutions. We then parametrize CG models that retain electronic structure information, simulate CG condensed phases, and predict the electronic properties of such phases solely from the CG degrees of freedom. We validate our method by comparing it against a full backmapping-based approach, and find good agreement between both methods. This work demonstrates the potential of the proposed method to accelerate multiscale workflows, and provides a framework for the development of CG models that retain electronic structure information. Degrees of freedom (physics and chemistry) ; Electronic structure ; Computer science ; Workflow ; Statistical physics ; Biological system ; Materials science ; Computational chemistry ; Chemistry ; Physics ; Thermodynamics Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Surface Chemistry and Catalysis
Authors: Lai Wei | Nihang Fu | Yuqi Song | Qian Wang | Jianjun Hu
Source: arXiv (Cornell University) (preprint, 2022-09-20)
Field: Materials Science — Materials Chemistry

Probabilistic Generative Transformer Language models for Generative Design of Molecules Self-supervised neural language models have recently found wide applications in generative design of organic molecules and protein sequences as well as representation learning for downstream structure classification and functional prediction. However, most of the existing deep learning models for molecule design usually require a big dataset and have a black-box architecture, which makes it difficult to interpret their design logic. Here we propose Generative Molecular Transformer (GMTransformer), a probabilistic neural network model for generative design of molecules. Our model is built on the blank filling language model originally developed for text processing, which has demonstrated unique advantages in learning the "molecules grammars" with high-quality generation, interpretability, and data efficiency. Benchmarked on the MOSES datasets, our models achieve high novelty and Scaf compared to other baselines. The probabilistic generation steps have the potential in tinkering molecule design due to their capability of recommending how to modify existing molecules with explanation, guided by the learned implicit molecule chemistry. The source code and datasets can be accessed freely at https://github.com/usccolumbia/GMTransformer Computer science ; Interpretability ; Probabilistic logic ; Transformer ; Artificial intelligence ; Generative grammar ; Generative model ; Generative Design ; Language model ; Machine learning ; Natural language processing ; Programming language ; Engineering ; Compatibility (geochemistry) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Penelope Jones | Kara D. Fong | Kristin A. Persson | Alpha A. Lee
Source: arXiv (Cornell University) (preprint, 2022-08-05)
Field: Materials Science — Materials Chemistry

Inferring global dynamics from local structure in liquid electrolytes Ion transport in concentrated electrolytes plays a fundamental role in electrochemical systems such as lithium ion batteries. Nonetheless, the mechanism of transport amid strong ion-ion interactions remains enigmatic. A key question is whether the dynamics of ion transport can be predicted by the local static structure alone, and if so what are the key structural motifs that determine transport. In this paper, we show that machine learning can successfully decompose global conductivity into the spatio-temporal average of local, instantaneous ionic contributions, and relate this ``local molar conductivity" field to the local ionic environment. Our machine learning model accurately predicts the molar conductivity of electrolyte systems that were not part of the training set, suggesting that the dynamics of ion transport is predictable from local static structure. Further, through analysing this machine-learned local conductivity field, we observe that fluctuations in local conductivity at high concentration are negatively correlated with total molar conductivity. Surprisingly, these fluctuations arise due to a long tail distribution of low conductivity ions, rather than distinct ion pairs, and are spatially correlated through both like- and unlike-charge interactions. More broadly, our approach shows how machine learning can aid the understanding of complex soft matter systems, by learning a function that attributes global collective properties to local, atomistic contributions. Conductivity ; Chemical physics ; Electrolyte ; Ion ; Ionic conductivity ; Local structure ; Ion transporter ; Molar conductivity ; Molecular dynamics ; Local field ; Statistical physics ; Materials science ; Chemistry ; Physics ; Condensed matter physics ; Computational chemistry ; Electrode ; Physical chemistry ; Quantum mechanics Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Advanced Chemical Sensor Technologies