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| 1 | Active learning in materials science with emphasis on adaptive sampling using uncertainties for targeted design显示文摘One of the main challenges in materials discovery is efficiently exploring the vast search space for targeted properties as approaches that rely on trial-and-error are impractical.We review how methods from the information sciences enable us to accelerate the search and discovery of new materials.In particular,active learning allows us to effectively navigate the search space iteratively to identify promising candidates for guiding experiments and computations.The approach relies on the use of uncertainties and making predictions from a surrogate model together with a utility function that prioritizes the decision making process on unexplored data.We discuss several utility functions and demonstrate their use in materials science applications,impacting both experimental and computational research.We summarize by indicating generalizations to multiple properties and multifidelity data,and identify challenges,future directions and opportunities in the emerging field of materials informatics. | Turab Lookman Prasanna V.Balachandran Dezhen Xue Ruihao Yuan | 2019 | npj Computational Materials2019，，1： | 28 |
| 2 | Developing an interatomic potential for martensitic phase transformations in zirconium by machine learning显示文摘Atomic simulations provide an effective means to understand the underlying physics of structural phase transformations.However,this remains a challenge for certain allotropic metals due to the failure of classical interatomic potentials to represent the multitude of bonding.Based on machine-learning(ML)techniques,we develop a hybrid method in which interatomic potentials describing martensitic transformations can be learned with a high degree of fidelity from ab initio molecular dynamics simulations(AIMD).Using zirconium as a model system,for which an adequate semiempirical potential describing the phase transformation process is lacking,we demonstrate the feasibility and effectiveness of our approach.Specifically,the ML-AIMD interatomic potential correctly captures the energetics and structural transformation properties of zirconium as compared to experimental and density-functional data for phonons,elastic constants,as well as stacking fault energies.Molecular dynamics simulations successfully reproduce the transformation mechanisms and reasonably map out the pressure–temperature phase diagram of zirconium. | Hongxiang Zong Ghanshyam Pilania Xiangdong Ding Graeme J.Ackland Turab Lookman | 2018 | npj Computational Materials2018，，1： | 7 |
| 3 | Automated pipeline for superalloy data by text mining显示文摘Data provides a foundation for machine learning,which has accelerated data-driven materials design.The scientific literature contains a large amount of high-quality,reliable data,and automatically extracting data from the literature continues to be a challenge.We propose a natural language processing pipeline to capture both chemical composition and property data that allows analysis and prediction of superalloys.Within 3 h,2531 records with both composition and property are extracted from 14,425 articles,coveringγ′solvus temperature,density,solidus,and liquidus temperatures.A data-driven model forγ′solvus temperature is built to predict unexplored Co-based superalloys with highγ′solvus temperatures within a relative error of 0.81%.We test the predictions via synthesis and characterization of three alloys.A web-based toolkit as an online open-source platform is provided and expected to serve as the basis for a general method to search for targeted materials using data extracted from the literature. | Weiren Wang Xue Jiang Shaohan Tian Pei Liu Depeng Dang Yanjing Su Turab Lookman Jianxin Xie | 2022 | npj Computational Materials2022，，1： | 2 |
| 4 | Improved stability of superelasticity and elastocaloric effect in Ti-Ni alloys by suppressing Lüders-like deformation under tensile load显示文摘Functional stability of superelasticity is crucial for practical applications of shape memory alloys.It is degraded by a Lüders-like deformation with elevated local stress concentration under tensile load.By increasing the degree of solute supersaturation and applying appropriate thermomechanical treatments,a Ti-Ni alloy with nanocrystallinity and dispersed nanoprecipitates is obtained.In contrast to conventional Ti-Ni alloys,the superelasticity in the target alloy is accompanied by homogeneous deformation due to the sluggish stress-induced martensitic transformation.The alloy thus shows a fully recoverable strain of 6%under tensile stress over 1 GPa and a large adiabatic temperature decrease of 13.1 K under tensile strain of 4.5%at room temperature.Moreover,both superelasticity and elastocaloric effect exhibit negligible degradation in response to applied strain of 4%during cycling.We attribute the improved functional stability to low dislocation activity resulting from the suppression of localized deformation and the combined strengthening effect of nanocrystalline structure and nanoprecipitates.Thus,the design of such a microstructure enabling homogeneous deformation provides a recipe for stable superelasticity and elastocaloric effect. | Pengfei Dang Jianbo Pang Yumei Zhou Lei Ding Lei Zhang Xiangdong Ding Turab Lookman Jun Sun Dezhen Xue | 2023 | Journal of Materials Science & Technology2023，，15： | 1 |
| 5 | Efficient sampling for decision making in materials discovery显示文摘Accelerating materials discovery crucially relies on strategies that efficiently sample the search space to label a pool of unlabeled data.This is important if the available labeled data sets are relatively small compared to the unlabeled data pool.Active learning with efficient sampling methods provides the means to guide the decision making to minimize the number of experiments or iterations required to find targeted properties.We review here different sampling strategies and show how they are utilized within an active learning loop in materials science. | 田原 Turab Lookman 薛德祯 | 2021 | Chinese Physics B2021，30，5： | 1 |
| 6 | 用于材料探索与设计的信息科学显示文摘材料是人类生存不可或缺的一部分,人类一直在寻找新材料道路上进行着不断地探索,也取得了很多重要的研究成果,这些重要的成果推动着人类文明的进步。为了获得所需功能的材料,人们需要进行大量的科学实验论证。因此,有必要找到一种行之有效的方法优化实验设计,从而降低实验的复杂性,减少实验次数,提高材料探索和设计的效率。本书就主要介绍了可以高效地进行材料探索和设计的方法。书中提到,可以用与人类基因组计划相似的方法来加速材料的探索,这种方法依赖于材料科学的大数据库、计算以及数学能力。 | Turab Lookman 王兆刚 | 2016 | 国外科技新书评介2016，0，12： | 0 |
| 7 | Machine learning assisted prediction of dielectric temperature spectrum of ferroelectrics显示文摘In material science and engineering,obtaining a spectrum from a measurement is often time-consuming and its accurate prediction using data mining can also be difficult.In this work,we propose a machine learning strategy based on a deep neural network model to accurately predict the dielectric temperature spectrum for a typical multi-component ferroelectric system,i.e.,(Ba_(1−x−y)Ca_(x)Sr_(y))(Ti_(1−u−v−w)Zr_(u)Sn_(v)Hf_(w))O_(3).The deep neural network model uses physical features as inputs and directly outputs the full spectrum,in addition to yielding the octahedral factor,Matyonov–Batsanov electronegativity,ratio of valence electron to nuclear charge,and core electron distance(Schubert)as four key descriptors.Owing to the physically meaningful features,our model exhibits better performance and generalization ability in the broader composition space of BaTiO3-based solid solutions.And the prediction accuracy is superior to traditional machine learning models that predict dielectric permittivity values at each temperature.Furthermore,the transition temperature and the degree of dispersion of the ferroelectric phase transition are easily extracted from the predicted spectra to provide richer physical information.The prediction is also experimentally validated by typical samples of(Ba_(0.85)Ca_(0.15))(Ti_(0.98–x)Zr_(x)Hf_(0.02))O_(3).This work provides insights for accelerating spectra predictions and extracting ferroelectric phase transition information. | Jingjin He Changxin Wang Junjie Li Chuanbao Liu Dezhen Xue Jiangli Cao Yanjing Su Lijie Qiao Turab Lookman Yang Bai | 2023 | Journal of Advanced Ceramics2023，12，9： | 0 |
| 8 | Symbolic regression in materials science via dimension-synchronous-computation显示文摘There is growing interest in applying machine learning techniques in the field of materials science.However,the interpretation and knowledge extracted from machine learning models is a major concern,particularly as formulating an explicit model that provides insight into physics is the goal of learning.In the present study,we propose a framework that utilizes the filtering ability of feature engineering,in conjunction with symbolic regression to extract explicit,quantitative expressions for the band gap energy from materials data.We propose enhancements to genetic programming with dimensional consistency and artificial constraints to improve the search efficiency of symbolic regression.We show how two descriptors attributed to volumetric and electronic factors,from 32 possible candidates,explicitly express the band gap energy of Na Cl-type compounds.Our approach provides a basis to capture underlying physical relationships between materials descriptors and target properties. | Changxin Wang Yan Zhang Cheng Wen Mingli Yang Turab Lookman Yanjing Su Tong-Yi Zhang | 2022 | Journal of Materials Science & Technology2022，，27： | 0 |
| 9 | Alloy synthesis and processing by semi-supervised text mining显示文摘Alloy synthesis and processing determine the design of alloys with desired microstructure and properties.However,using data science to identify optimal synthesis-design routes from a specified set of starting materials has been limited by large-scale data acquisition.Text mining has made it possible to convert scientific text into structured data collections.Still,the complexity,diversity,and flexibility of synthesis and processing expressions,and the lack of annotated corpora with a gold standard severely hinder accurate and efficient extraction.Here we introduce a semi-supervised text mining method to extract the parameters corresponding to the sequence of actions of synthesis and processing.We automatically extract a total of 9853 superalloy synthesis and processing actions with chemical compositions from a corpus of 16,604 superalloy articles published up to 2022.These have then been used to capture an explicitly expressed synthesis factor for predictingγ′phase coarsening.The synthesis factor derived from text mining significantly improves the performance of the data-drivenγ′size prediction model.The method thus complements the use of data-driven approaches in the search for relationships between synthesis and structures. | Weiren Wang Xue Jiang Shaohan Tian Pei Liu Turab Lookman Yanjing Su Jianxin Xie | 2023 | npj Computational Materials2023，，1： | 0 |
| 10 | Theγ/γ′microstructure in CoNiAlCr-based superalloys using triple-objective optimization显示文摘Optimizing several properties simultaneously based on small data-driven machine learning in complex black-box scenarios can present difficulties and challenges.Here we employ a triple-objective optimization algorithm deduced from probability density functions of multivariate Gaussian distributions to optimize theγ′volume fraction,size,and morphology in CoNiAlCr-based superalloys.The effectiveness of the algorithm is demonstrated by synthesizing alloys with desiredγ/γ′microstructure and optimizingγ′microstructural parameters.In addition,the method leads to incorporating refractory elements to improveγ/γ′microstructure in superalloys.After four iterations of experiments guided by the algorithm,we synthesize sixteen alloys of relatively high creep strength from~120,000 candidates of which three possess highγ′volume fraction(>54%),smallγ′size(<480 nm),and high cuboidalγ′fraction(>77%). | Pei Liu Haiyou Huang Cheng Wen Turab Lookman Yanjing Su | 2023 | npj Computational Materials2023，，1： | 0 |
| 11 | Temperature-field history dependence of the elastocaloric effect for a strain glass alloy显示文摘The singular change of the order parameter at the first order martensitic transformation(MT)temperature restricts the caloric response to a narrow temperature range.Here the MT is tuned into a sluggish strain glass transition by defect doping and a large elastocaloric effect appears in a wide temperature range.Moreover,an inverse elastocaloric effect is observed in the strain glass alloy with history of zerofield cooling and is attributed to the slow dynamics of the nanodomains in response to the external stress.This study offers a design recipe to expand the temperature range for good elastocaloric effect. | Deqing Xue Ruihao Yuan Yuanchao Yang Jianbo Pang Yumei Zhou Xiangdong Ding Turab Lookman Xiaobing Ren Jun Sun Dezhen Xue | 2022 | Journal of Materials Science & Technology2022，，8： | 0 |
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