The structure type for the crystal of 4,4'-bis-(2-hydroxy-ethoxyl)-biphenyl 1 has been predicted by using the previously developed interfacial model for small organic molecules. Based on the calculated hydrophobic...The structure type for the crystal of 4,4'-bis-(2-hydroxy-ethoxyl)-biphenyl 1 has been predicted by using the previously developed interfacial model for small organic molecules. Based on the calculated hydrophobic to hydrophilic volume of 1, this model predicts the crystal structure to be of lamellar or bicontinuous type, which has been confirmed by the X-ray single-crystal structure analysis (C20H26O6, monoclinic, P21/C, a = 16.084(1), b = 6.0103(4), c = 9.6410(7) A, β9 = 103.014(2)°, V= 908.1(1) A3, Z = 2, Dc= 1.325 g/cm3, F(000)=388,μ = 0.097 mm-1, MoKα radiation, λ = 0.71073 A, R = 0.0382 and wR = 0.0882 with I > 2σ(I) for 7121 reflections collected, 1852 unique reflections and 170 parameters). As predicted, the hydrophobic and hydrophilic portions of 1 form in the lamellae. The same interfacial model is applied to other amphilphilic small molecule organic systems for structural type prediction.展开更多
Structure prediction methods have been widely used as a state-of-the-art tool for structure searches and materials discovery, leading to many theory-driven breakthroughs on discoveries of new materials. These methods ...Structure prediction methods have been widely used as a state-of-the-art tool for structure searches and materials discovery, leading to many theory-driven breakthroughs on discoveries of new materials. These methods generally involve the exploration of the potential energy surfaces of materials through various structure sampling techniques and optimization algorithms in conjunction with quantum mechanical calculations. By taking advantage of the general feature of materials potential energy surface and swarm-intelligence-based global optimization algorithms, we have developed the CALYPSO method for structure prediction, which has been widely used in fields as diverse as computational physics, chemistry, and materials science. In this review, we provide the basic theory of the CALYPSO method, placing particular emphasis on the principles of its various structure dealing methods. We also survey the current challenges faced by structure prediction methods and include an outlook on the future developments of CALYPSO in the conclusions.展开更多
Structural genomics (SG) is an international effort that aims at solving three-dimensional shapes of important biological macro-molecules with primary focus on proteins. One of the main bottlenecks in SG is the abilit...Structural genomics (SG) is an international effort that aims at solving three-dimensional shapes of important biological macro-molecules with primary focus on proteins. One of the main bottlenecks in SG is the ability to produce dif-fraction quality crystals for X-ray crystallogra-phy based protein structure determination. SG pipelines allow for certain flexibility in target selection which motivates development of in- silico methods for sequence-based prediction/ assessment of the protein crystallization pro-pensity. We overview existing SG databanks that are used to derive these predictive models and we discuss analytical results concerning protein sequence properties that were discov-ered to correlate with the ability to form crystals. We also contrast and empirically compare mo- dern sequence-based predictors of crystalliza-tion propensity including OB-Score, ParCrys, XtalPred and CRYSTALP2. Our analysis shows that these methods provide useful and compli-mentary predictions. Although their average ac- curacy is similar at around 70%, we show that application of a simple majority-vote based en-semble improves accuracy to almost 74%. The best improvements are achieved by combining XtalPred with CRYSTALP2 while OB-Score and ParCrys methods overlap to a larger extend, although they still complement the other two predictors. We also demonstrate that 90% of the protein chains can be correctly predicted by at least one of these methods, which suggests that more accurate ensembles could be built in the future. We believe that current protein crystalli-zation propensity predictors could provide useful input for the target selection procedures utilized by the SG centers.展开更多
Many properties of planets such as their interior structure and thermal evolution depend on the high-pressure properties of their constituent materials. This paper reviews how crystal structure prediction methodology ...Many properties of planets such as their interior structure and thermal evolution depend on the high-pressure properties of their constituent materials. This paper reviews how crystal structure prediction methodology can help shed light on the transformations materials undergo at the extreme conditions inside planets. The discussion focuses on three areas:(i) the propensity of iron to form compounds with volatile elements at planetary core conditions(important to understand the chemical makeup of Earth's inner core),(ii) the chemistry of mixtures of planetary ices(relevant for the mantle regions of giant icy planets), and(iii) examples of mantle minerals. In all cases the abilities and current limitations of crystal structure prediction are discussed across a range of example studies.展开更多
Crystal structure prediction algorithms have become powerful tools for materials discovery in recent years, however, they are usually limited to relatively small systems. The main challenge is that the number of local...Crystal structure prediction algorithms have become powerful tools for materials discovery in recent years, however, they are usually limited to relatively small systems. The main challenge is that the number of local minima grows exponentially with the system size. In this work, we proposed two crossover-mutation schemes based on graph theory to accelerate the evolutionary structure searching by automatic decomposition methods. These schemes can detect molecules or clusters inside periodic networks using quotient graphs for crystals, and the decomposition can dramatically reduce the searching space. Sufficient examples for test, including the high-pressure phases of methane, ammonia, MgAl2O4 and boron, show that these new evolution schemes can significantly improve the success rate and searching efficiency compared with the standard method in both isolated and extended systems.展开更多
Based on structure prediction method,the machine learning method is used instead of the density functional theory(DFT)method to predict the material properties,thereby accelerating the material search process.In this ...Based on structure prediction method,the machine learning method is used instead of the density functional theory(DFT)method to predict the material properties,thereby accelerating the material search process.In this paper,we established a data set of carbon materials by high-throughput calculation with available carbon structures obtained from the Samara Carbon Allotrope Database.We then trained a machine learning(ML)model that specifically predicts the elastic modulus(bulk modulus,shear modulus,and the Young's modulus)and confirmed that the accuracy is better than that of AFLOW-ML in predicting the elastic modulus of a carbon allotrope.We further combined our ML model with the CALYPSO code to search for new carbon structures with a high Young's modulus.A new carbon allotrope not included in the Samara Carbon Allotrope Database,named Cmcm-C24,which exhibits a hardness greater than 80 GPa,was firstly revealed.The Cmcm-C24 phase was identified as a semiconductor with a direct bandgap.The structural stability,elastic modulus,and electronic properties of the new carbon allotrope were systematically studied,and the obtained results demonstrate the feasibility of ML methods accelerating the material search process.展开更多
Inverse materials design tackles the challenge of finding materials with desired properties, tailored to specific applications, by combining atomistic simulations and optimization methods. The search for optimal mater...Inverse materials design tackles the challenge of finding materials with desired properties, tailored to specific applications, by combining atomistic simulations and optimization methods. The search for optimal materials requires one to survey large spaces of candidate solids. These spaces of materials can encompass both known and hypothetical compounds. When hypothetical compounds are explored, it becomes crucial to determine which ones are stable（and can be synthesized） and which are not. Crystal structure prediction is a necessary step for assessing theoretically the stability of a hypothetical material and, therefore, is a crucial step in inverse materials design protocols. Here, we describe how biologically-inspired global optimization methods can efficiently predict the stable crystal structure of solids. Specifically,we discuss the application of genetic algorithms to search for optimal atom configurations in systems in which the underlying lattice is given,and of evolutionary algorithms to address the general lattice-type prediction problem.展开更多
Crystal structure prediction is a central problem of crystallography and materials science, which until mid-2000s was consideredintractable. Several methods, based on either energy landscape exploration or, more commo...Crystal structure prediction is a central problem of crystallography and materials science, which until mid-2000s was consideredintractable. Several methods, based on either energy landscape exploration or, more commonly, global optimization, largely solvedthis problem and enabled fully non-empirical computational materials discovery. A major shortcoming is that, to avoid expensivecalculations of the entropy, crystal structure prediction was done at zero Kelvin, reducing to the search for the global minimum ofthe enthalpy rather than the free energy. As a consequence, high-temperature phases (especially those which are not quenchableto zero temperature) could be missed. Here we develop an accurate and affordable solution, enabling crystal structure prediction atfinite temperatures. Structure relaxation and fully anharmonic free energy calculations are done by molecular dynamics with aforcefield (which can be anything from a parametric forcefield for simpler cases to a trained on-the-fly machine learning interatomicpotential), the errors of which are corrected using thermodynamic perturbation theory to yield accurate results with full ab initioaccuracy. We illustrate this method by applications to metals (probing the P–T phase diagram of Al and Fe), a refractory covalentsolid (WB), an Earth-forming silicate MgSiO_(3) (at pressures and temperatures of the Earth’s lower mantle), and ceramic oxide HfO_(2).展开更多
密度是决定含能材料爆轰性能的重要参数。为评估现有CHON类含能材料密度的计算方法,对等电子密度面法、分子表面静电势法、基团加和法、晶体堆积法、定量构效关系法、经验公式法等进行分析和归类。结果表明,基于分子体积预测方法的精度...密度是决定含能材料爆轰性能的重要参数。为评估现有CHON类含能材料密度的计算方法,对等电子密度面法、分子表面静电势法、基团加和法、晶体堆积法、定量构效关系法、经验公式法等进行分析和归类。结果表明,基于分子体积预测方法的精度取决于分子间和分子内相互作用对密度影响描述的准确度。其中,准确描述氢键和van der Waals作用充满了挑战性。基于晶体体积计算密度的核心在于晶体结构的准确预测,结构搜索要面对巨大的状态空间和高度复杂的能量曲面的困难,预测效率是亟待解决的问题。体积加和法和经验公式法存在无法区分同分异构体和晶型的缺点,且对新发现的具有特殊结构的分子由于缺乏实验数据难以获得准确的经验参数,计算结果偏差较大。引入人工神经网络、遗传算法以及支持向量机等机器学习算法后,定量构效关系法在含能化合物性能与结构关系研究中取得很大成就,模型精度进一步提高将为基于材料基因组模式的含能材料设计研发奠定基础,这也是今后密度预测方法发展的主要方向。展开更多
基金This work was supported by the National Science Foundation(Grant DMR-9812351)
文摘The structure type for the crystal of 4,4'-bis-(2-hydroxy-ethoxyl)-biphenyl 1 has been predicted by using the previously developed interfacial model for small organic molecules. Based on the calculated hydrophobic to hydrophilic volume of 1, this model predicts the crystal structure to be of lamellar or bicontinuous type, which has been confirmed by the X-ray single-crystal structure analysis (C20H26O6, monoclinic, P21/C, a = 16.084(1), b = 6.0103(4), c = 9.6410(7) A, β9 = 103.014(2)°, V= 908.1(1) A3, Z = 2, Dc= 1.325 g/cm3, F(000)=388,μ = 0.097 mm-1, MoKα radiation, λ = 0.71073 A, R = 0.0382 and wR = 0.0882 with I > 2σ(I) for 7121 reflections collected, 1852 unique reflections and 170 parameters). As predicted, the hydrophobic and hydrophilic portions of 1 form in the lamellae. The same interfacial model is applied to other amphilphilic small molecule organic systems for structural type prediction.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.11534003 and 11604117)the National Key Research and Development Program of China(Grant No.2016YFB0201201)+1 种基金the Program for JLU Science and Technology Innovative Research Team(JLUSTIRT)of Chinathe Science Challenge Project of China(Grant No.TZ2016001)
文摘Structure prediction methods have been widely used as a state-of-the-art tool for structure searches and materials discovery, leading to many theory-driven breakthroughs on discoveries of new materials. These methods generally involve the exploration of the potential energy surfaces of materials through various structure sampling techniques and optimization algorithms in conjunction with quantum mechanical calculations. By taking advantage of the general feature of materials potential energy surface and swarm-intelligence-based global optimization algorithms, we have developed the CALYPSO method for structure prediction, which has been widely used in fields as diverse as computational physics, chemistry, and materials science. In this review, we provide the basic theory of the CALYPSO method, placing particular emphasis on the principles of its various structure dealing methods. We also survey the current challenges faced by structure prediction methods and include an outlook on the future developments of CALYPSO in the conclusions.
文摘Structural genomics (SG) is an international effort that aims at solving three-dimensional shapes of important biological macro-molecules with primary focus on proteins. One of the main bottlenecks in SG is the ability to produce dif-fraction quality crystals for X-ray crystallogra-phy based protein structure determination. SG pipelines allow for certain flexibility in target selection which motivates development of in- silico methods for sequence-based prediction/ assessment of the protein crystallization pro-pensity. We overview existing SG databanks that are used to derive these predictive models and we discuss analytical results concerning protein sequence properties that were discov-ered to correlate with the ability to form crystals. We also contrast and empirically compare mo- dern sequence-based predictors of crystalliza-tion propensity including OB-Score, ParCrys, XtalPred and CRYSTALP2. Our analysis shows that these methods provide useful and compli-mentary predictions. Although their average ac- curacy is similar at around 70%, we show that application of a simple majority-vote based en-semble improves accuracy to almost 74%. The best improvements are achieved by combining XtalPred with CRYSTALP2 while OB-Score and ParCrys methods overlap to a larger extend, although they still complement the other two predictors. We also demonstrate that 90% of the protein chains can be correctly predicted by at least one of these methods, which suggests that more accurate ensembles could be built in the future. We believe that current protein crystalli-zation propensity predictors could provide useful input for the target selection procedures utilized by the SG centers.
基金A Research Fellowship for International Young Scientists by the National Natural Science Foundation (NNSF) on “In-silico studies of planetary materials” Computing resources provided by the UK national high performance computing service, ARCHER, and the UK Materials and Molecular Modelling Hub, which is partially funded by EPSRC (EP/P020194)for which access was obtained via the UKCP consortium funded by EPSRC grant No. EP/P022561/1
文摘Many properties of planets such as their interior structure and thermal evolution depend on the high-pressure properties of their constituent materials. This paper reviews how crystal structure prediction methodology can help shed light on the transformations materials undergo at the extreme conditions inside planets. The discussion focuses on three areas:(i) the propensity of iron to form compounds with volatile elements at planetary core conditions(important to understand the chemical makeup of Earth's inner core),(ii) the chemistry of mixtures of planetary ices(relevant for the mantle regions of giant icy planets), and(iii) examples of mantle minerals. In all cases the abilities and current limitations of crystal structure prediction are discussed across a range of example studies.
基金support from the National Natural Science Foundation of China (Grant Nos. 11974162 and 11834006)the National Key R&D Program of China (Grant Nos. 2016YFA0300404)the Fundamental Research Funds for the Central Universities.
文摘Crystal structure prediction algorithms have become powerful tools for materials discovery in recent years, however, they are usually limited to relatively small systems. The main challenge is that the number of local minima grows exponentially with the system size. In this work, we proposed two crossover-mutation schemes based on graph theory to accelerate the evolutionary structure searching by automatic decomposition methods. These schemes can detect molecules or clusters inside periodic networks using quotient graphs for crystals, and the decomposition can dramatically reduce the searching space. Sufficient examples for test, including the high-pressure phases of methane, ammonia, MgAl2O4 and boron, show that these new evolution schemes can significantly improve the success rate and searching efficiency compared with the standard method in both isolated and extended systems.
基金This work was financlally supported by the Fundamental Research Funds for the Central Universities,the Na-tional Natural Science Foundation of China(Grant Nos.11965005 and 11964026)the 111 Project(No.B17035)the Natural Sci-ence Basie Research plan in Shaanxi Province of China(Grant Nos.2020JM-186 and 2020JM-621).
文摘Based on structure prediction method,the machine learning method is used instead of the density functional theory(DFT)method to predict the material properties,thereby accelerating the material search process.In this paper,we established a data set of carbon materials by high-throughput calculation with available carbon structures obtained from the Samara Carbon Allotrope Database.We then trained a machine learning(ML)model that specifically predicts the elastic modulus(bulk modulus,shear modulus,and the Young's modulus)and confirmed that the accuracy is better than that of AFLOW-ML in predicting the elastic modulus of a carbon allotrope.We further combined our ML model with the CALYPSO code to search for new carbon structures with a high Young's modulus.A new carbon allotrope not included in the Samara Carbon Allotrope Database,named Cmcm-C24,which exhibits a hardness greater than 80 GPa,was firstly revealed.The Cmcm-C24 phase was identified as a semiconductor with a direct bandgap.The structural stability,elastic modulus,and electronic properties of the new carbon allotrope were systematically studied,and the obtained results demonstrate the feasibility of ML methods accelerating the material search process.
基金Jilin Provincial Science and Technology Development Joint Fund Project(YDZJ202201ZYTS581)Scientific and Technological Research Project of Jilin Province Education Department(Grant No.JJKH20240077KJ)。
文摘Inverse materials design tackles the challenge of finding materials with desired properties, tailored to specific applications, by combining atomistic simulations and optimization methods. The search for optimal materials requires one to survey large spaces of candidate solids. These spaces of materials can encompass both known and hypothetical compounds. When hypothetical compounds are explored, it becomes crucial to determine which ones are stable（and can be synthesized） and which are not. Crystal structure prediction is a necessary step for assessing theoretically the stability of a hypothetical material and, therefore, is a crucial step in inverse materials design protocols. Here, we describe how biologically-inspired global optimization methods can efficiently predict the stable crystal structure of solids. Specifically,we discuss the application of genetic algorithms to search for optimal atom configurations in systems in which the underlying lattice is given,and of evolutionary algorithms to address the general lattice-type prediction problem.
基金I.A.K.gratefully acknowledges the financial support from the Ministry of Science and Higher Education(Agreement No.075-15-2021-606)and from the Foundation for Assistance to Small Innovative Enterprises in Science and Technology(the UMNIK program)A.B.M.thanks the Russian Science Foundation(grant No.19-73-00237)for financial supportThe work of A.R.O.is supported by the Russian Science Foundation(grant 19-72-30043).
文摘Crystal structure prediction is a central problem of crystallography and materials science, which until mid-2000s was consideredintractable. Several methods, based on either energy landscape exploration or, more commonly, global optimization, largely solvedthis problem and enabled fully non-empirical computational materials discovery. A major shortcoming is that, to avoid expensivecalculations of the entropy, crystal structure prediction was done at zero Kelvin, reducing to the search for the global minimum ofthe enthalpy rather than the free energy. As a consequence, high-temperature phases (especially those which are not quenchableto zero temperature) could be missed. Here we develop an accurate and affordable solution, enabling crystal structure prediction atfinite temperatures. Structure relaxation and fully anharmonic free energy calculations are done by molecular dynamics with aforcefield (which can be anything from a parametric forcefield for simpler cases to a trained on-the-fly machine learning interatomicpotential), the errors of which are corrected using thermodynamic perturbation theory to yield accurate results with full ab initioaccuracy. We illustrate this method by applications to metals (probing the P–T phase diagram of Al and Fe), a refractory covalentsolid (WB), an Earth-forming silicate MgSiO_(3) (at pressures and temperatures of the Earth’s lower mantle), and ceramic oxide HfO_(2).
文摘密度是决定含能材料爆轰性能的重要参数。为评估现有CHON类含能材料密度的计算方法,对等电子密度面法、分子表面静电势法、基团加和法、晶体堆积法、定量构效关系法、经验公式法等进行分析和归类。结果表明,基于分子体积预测方法的精度取决于分子间和分子内相互作用对密度影响描述的准确度。其中,准确描述氢键和van der Waals作用充满了挑战性。基于晶体体积计算密度的核心在于晶体结构的准确预测,结构搜索要面对巨大的状态空间和高度复杂的能量曲面的困难,预测效率是亟待解决的问题。体积加和法和经验公式法存在无法区分同分异构体和晶型的缺点,且对新发现的具有特殊结构的分子由于缺乏实验数据难以获得准确的经验参数,计算结果偏差较大。引入人工神经网络、遗传算法以及支持向量机等机器学习算法后,定量构效关系法在含能化合物性能与结构关系研究中取得很大成就,模型精度进一步提高将为基于材料基因组模式的含能材料设计研发奠定基础,这也是今后密度预测方法发展的主要方向。
